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from collections import defaultdict
from interface import implements
from numpy import iinfo, uint32, multiply
from zipline.data.fx import ExplodingFXRateReader
from zipline.lib.adjusted_array import AdjustedArray
from zipline.utils.numpy_utils import repeat_first_axis
from .base import PipelineLoader
from .utils import shift_dates
from ..data.equity_pricing import EquityPricing
UINT32_MAX = iinfo(uint32).max
class EquityPricingLoader(implements(PipelineLoader)):
"""A PipelineLoader for loading daily OHLCV data.
Parameters
----------
raw_price_reader : zipline.data.session_bars.SessionBarReader
Reader providing raw prices.
adjustments_reader : zipline.data.adjustments.SQLiteAdjustmentReader
Reader providing price/volume adjustments.
fx_reader : zipline.data.fx.FXRateReader
Reader providing currency conversions.
"""
def __init__(self,
raw_price_reader,
adjustments_reader,
fx_reader):
self.raw_price_reader = raw_price_reader
self.adjustments_reader = adjustments_reader
self.fx_reader = fx_reader
@classmethod
def without_fx(cls, raw_price_reader, adjustments_reader):
"""
Construct an EquityPricingLoader without support for fx rates.
The returned loader will raise an error if requested to load
currency-converted columns.
Parameters
----------
raw_price_reader : zipline.data.session_bars.SessionBarReader
Reader providing raw prices.
adjustments_reader : zipline.data.adjustments.SQLiteAdjustmentReader
Reader providing price/volume adjustments.
Returns
-------
loader : EquityPricingLoader
A loader that can only provide currency-naive data.
"""
return cls(
raw_price_reader=raw_price_reader,
adjustments_reader=adjustments_reader,
fx_reader=ExplodingFXRateReader(),
)
def load_adjusted_array(self, domain, columns, dates, sids, mask):
# load_adjusted_array is called with dates on which the user's algo
# will be shown data, which means we need to return the data that would
# be known at the **start** of each date. We assume that the latest
# data known on day N is the data from day (N - 1), so we shift all
# query dates back by a trading session.
sessions = domain.all_sessions()
shifted_dates = shift_dates(sessions, dates[0], dates[-1], shift=1)
ohlcv_cols, currency_cols = self._split_column_types(columns)
del columns # From here on we should use ohlcv_cols or currency_cols.
ohlcv_colnames = [c.name for c in ohlcv_cols]
raw_ohlcv_arrays = self.raw_price_reader.load_raw_arrays(
ohlcv_colnames,
shifted_dates[0],
shifted_dates[-1],
sids,
)
# Currency convert raw_arrays in place if necessary. We use shifted
# dates to load currency conversion rates to make them line up with
# dates used to fetch prices.
self._inplace_currency_convert(
ohlcv_cols,
raw_ohlcv_arrays,
shifted_dates,
sids,
)
adjustments = self.adjustments_reader.load_pricing_adjustments(
ohlcv_colnames,
dates,
sids,
)
out = {}
for c, c_raw, c_adjs in zip(ohlcv_cols, raw_ohlcv_arrays, adjustments):
out[c] = AdjustedArray(
c_raw.astype(c.dtype),
c_adjs,
c.missing_value,
)
for c in currency_cols:
codes_1d = self.raw_price_reader.currency_codes(sids)
codes = repeat_first_axis(codes_1d, len(dates))
out[c] = AdjustedArray(
codes,
adjustments={},
missing_value=None,
)
return out
@property
def currency_aware(self):
# Tell the pipeline engine that this loader supports currency
# conversion if we have a non-dummy fx rates reader.
return not isinstance(self.fx_reader, ExplodingFXRateReader)
def _inplace_currency_convert(self, columns, arrays, dates, sids):
"""
Currency convert raw data loaded for ``column``.
Parameters
----------
columns : list[zipline.pipeline.data.BoundColumn]
List of columns whose raw data has been loaded.
arrays : list[np.array]
List of arrays, parallel to ``columns`` containing data for the
column.
dates : pd.DatetimeIndex
Labels for rows of ``arrays``. These are the dates that should
be used to fetch fx rates for conversion.
sids : np.array[int64]
Labels for columns of ``arrays``.
Returns
-------
None
Side Effects
------------
Modifies ``arrays`` in place by applying currency conversions.
"""
# Group columns by currency conversion spec.
by_spec = defaultdict(list)
for column, array in zip(columns, arrays):
by_spec[column.currency_conversion].append(array)
# Nothing to do for terms with no currency conversion.
by_spec.pop(None, None)
if not by_spec:
return
fx_reader = self.fx_reader
base_currencies = self.raw_price_reader.currency_codes(sids)
# Columns with the same conversion spec will use the same multipliers.
for spec, arrays in by_spec.items():
rates = fx_reader.get_rates(
rate=spec.field,
quote=spec.currency.code,
bases=base_currencies,
dts=dates,
)
for arr in arrays:
multiply(arr, rates, out=arr)
def _split_column_types(self, columns):
"""Split out currency columns from OHLCV columns.
Parameters
----------
columns : list[zipline.pipeline.data.BoundColumn]
Columns to be loaded by ``load_adjusted_array``.
Returns
-------
ohlcv_columns : list[zipline.pipeline.data.BoundColumn]
Price and volume columns from ``columns``.
currency_columns : list[zipline.pipeline.data.BoundColumn]
Currency code column from ``columns``, if present.
"""
currency_name = EquityPricing.currency.name
ohlcv = []
currency = []
for c in columns:
if c.name == currency_name:
currency.append(c)
else:
ohlcv.append(c)
return ohlcv, currency
# Backwards compat alias.
USEquityPricingLoader = EquityPricingLoader | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/equity_pricing_loader.py | equity_pricing_loader.py |
from interface import implements
from numpy import (
arange,
array,
eye,
float64,
full,
iinfo,
nan,
uint32,
)
from numpy.random import RandomState
from pandas import DataFrame, Timestamp
from six import iteritems
from sqlite3 import connect as sqlite3_connect
from .base import PipelineLoader
from .frame import DataFrameLoader
from zipline.data.adjustments import (
SQLiteAdjustmentReader,
SQLiteAdjustmentWriter,
)
from zipline.data.bcolz_daily_bars import US_EQUITY_PRICING_BCOLZ_COLUMNS
from zipline.utils.numpy_utils import (
bool_dtype,
datetime64ns_dtype,
float64_dtype,
int64_dtype,
object_dtype,
)
UINT_32_MAX = iinfo(uint32).max
def nanos_to_seconds(nanos):
return nanos / (1000 * 1000 * 1000)
class PrecomputedLoader(implements(PipelineLoader)):
"""
Synthetic PipelineLoader that uses a pre-computed array for each column.
Parameters
----------
values : dict
Map from column to values to use for that column.
Values can be anything that can be passed as the first positional
argument to a DataFrame whose indices are ``dates`` and ``sids``
dates : iterable[datetime-like]
Row labels for input data. Can be anything that pd.DataFrame will
coerce to a DatetimeIndex.
sids : iterable[int-like]
Column labels for input data. Can be anything that pd.DataFrame will
coerce to an Int64Index.
Notes
-----
Adjustments are unsupported by this loader.
"""
def __init__(self, constants, dates, sids):
loaders = {}
for column, const in iteritems(constants):
frame = DataFrame(
const,
index=dates,
columns=sids,
dtype=column.dtype,
)
loaders[column] = DataFrameLoader(
column=column,
baseline=frame,
adjustments=None,
)
self._loaders = loaders
def load_adjusted_array(self, domain, columns, dates, sids, mask):
"""
Load by delegating to sub-loaders.
"""
out = {}
for col in columns:
try:
loader = self._loaders.get(col)
if loader is None:
loader = self._loaders[col.unspecialize()]
except KeyError:
raise ValueError("Couldn't find loader for %s" % col)
out.update(
loader.load_adjusted_array(domain, [col], dates, sids, mask)
)
return out
class EyeLoader(PrecomputedLoader):
"""
A PrecomputedLoader that emits arrays containing 1s on the diagonal and 0s
elsewhere.
Parameters
----------
columns : list[BoundColumn]
Columns that this loader should know about.
dates : iterable[datetime-like]
Same as PrecomputedLoader.
sids : iterable[int-like]
Same as PrecomputedLoader
"""
def __init__(self, columns, dates, sids):
shape = (len(dates), len(sids))
super(EyeLoader, self).__init__(
{column: eye(shape, dtype=column.dtype) for column in columns},
dates,
sids,
)
class SeededRandomLoader(PrecomputedLoader):
"""
A PrecomputedLoader that emits arrays randomly-generated with a given seed.
Parameters
----------
seed : int
Seed for numpy.random.RandomState.
columns : list[BoundColumn]
Columns that this loader should know about.
dates : iterable[datetime-like]
Same as PrecomputedLoader.
sids : iterable[int-like]
Same as PrecomputedLoader
"""
def __init__(self, seed, columns, dates, sids):
self._seed = seed
super(SeededRandomLoader, self).__init__(
{c: self.values(c.dtype, dates, sids) for c in columns},
dates,
sids,
)
def values(self, dtype, dates, sids):
"""
Make a random array of shape (len(dates), len(sids)) with ``dtype``.
"""
shape = (len(dates), len(sids))
return {
datetime64ns_dtype: self._datetime_values,
float64_dtype: self._float_values,
int64_dtype: self._int_values,
bool_dtype: self._bool_values,
object_dtype: self._object_values,
}[dtype](shape)
@property
def state(self):
"""
Make a new RandomState from our seed.
This ensures that every call to _*_values produces the same output
every time for a given SeededRandomLoader instance.
"""
return RandomState(self._seed)
def _float_values(self, shape):
"""
Return uniformly-distributed floats between -0.0 and 100.0.
"""
return self.state.uniform(low=0.0, high=100.0, size=shape)
def _int_values(self, shape):
"""
Return uniformly-distributed integers between 0 and 100.
"""
return (self.state.randint(low=0, high=100, size=shape)
.astype('int64')) # default is system int
def _datetime_values(self, shape):
"""
Return uniformly-distributed dates in 2014.
"""
start = Timestamp('2014', tz='UTC').asm8
offsets = self.state.randint(
low=0,
high=364,
size=shape,
).astype('timedelta64[D]')
return start + offsets
def _bool_values(self, shape):
"""
Return uniformly-distributed True/False values.
"""
return self.state.randn(*shape) < 0
def _object_values(self, shape):
res = self._int_values(shape).astype(str).astype(object)
return res
OHLCV = ('open', 'high', 'low', 'close', 'volume')
OHLC = ('open', 'high', 'low', 'close')
PSEUDO_EPOCH = Timestamp('2000-01-01', tz='UTC')
def asset_start(asset_info, asset):
ret = asset_info.loc[asset]['start_date']
if ret.tz is None:
ret = ret.tz_localize('UTC')
assert ret.tzname() == 'UTC', "Unexpected non-UTC timestamp"
return ret
def asset_end(asset_info, asset):
ret = asset_info.loc[asset]['end_date']
if ret.tz is None:
ret = ret.tz_localize('UTC')
assert ret.tzname() == 'UTC', "Unexpected non-UTC timestamp"
return ret
def make_bar_data(asset_info, calendar, holes=None):
"""
For a given asset/date/column combination, we generate a corresponding raw
value using the following formula for OHLCV columns:
data(asset, date, column) = (100,000 * asset_id)
+ (10,000 * column_num)
+ (date - Jan 1 2000).days # ~6000 for 2015
where:
column_num('open') = 0
column_num('high') = 1
column_num('low') = 2
column_num('close') = 3
column_num('volume') = 4
We use days since Jan 1, 2000 to guarantee that there are no collisions
while also the produced values smaller than UINT32_MAX / 1000.
For 'day' and 'id', we use the standard format expected by the base class.
Parameters
----------
asset_info : DataFrame
DataFrame with asset_id as index and 'start_date'/'end_date' columns.
calendar : pd.DatetimeIndex
The trading calendar to use.
holes : dict[int -> tuple[pd.Timestamps]], optional
A dict mapping asset ids to the tuple of dates that should have
no data for that asset in the output. Default is no holes.
Yields
------
p : (int, pd.DataFrame)
A sid, data pair to be passed to BcolzDailyDailyBarWriter.write
"""
assert (
# Using .value here to avoid having to care about UTC-aware dates.
PSEUDO_EPOCH.value <
calendar.normalize().min().value <=
asset_info['start_date'].min().value
), "calendar.min(): %s\nasset_info['start_date'].min(): %s" % (
calendar.min(),
asset_info['start_date'].min(),
)
assert (asset_info['start_date'] < asset_info['end_date']).all()
def _raw_data_for_asset(asset_id):
"""
Generate 'raw' data that encodes information about the asset.
See docstring for a description of the data format.
"""
# Get the dates for which this asset existed according to our asset
# info.
datetimes = calendar[calendar.slice_indexer(
asset_start(asset_info, asset_id),
asset_end(asset_info, asset_id),
)]
data = full(
(len(datetimes), len(US_EQUITY_PRICING_BCOLZ_COLUMNS)),
asset_id * 100 * 1000,
dtype=uint32,
)
# Add 10,000 * column-index to OHLCV columns
data[:, :5] += arange(5, dtype=uint32) * 1000
# Add days since Jan 1 2001 for OHLCV columns.
data[:, :5] += (datetimes - PSEUDO_EPOCH).days[:, None].astype(uint32)
frame = DataFrame(
data,
index=datetimes,
columns=US_EQUITY_PRICING_BCOLZ_COLUMNS,
)
if holes is not None and asset_id in holes:
for dt in holes[asset_id]:
frame.loc[dt, OHLC] = nan
frame.loc[dt, ['volume']] = 0
frame['day'] = nanos_to_seconds(datetimes.asi8)
frame['id'] = asset_id
return frame
for asset in asset_info.index:
yield asset, _raw_data_for_asset(asset)
def expected_bar_value(asset_id, date, colname):
"""
Check that the raw value for an asset/date/column triple is as
expected.
Used by tests to verify data written by a writer.
"""
from_asset = asset_id * 100000
from_colname = OHLCV.index(colname) * 1000
from_date = (date - PSEUDO_EPOCH).days
return from_asset + from_colname + from_date
def expected_bar_value_with_holes(asset_id,
date,
colname,
holes,
missing_value):
# Explicit holes are filled with the missing value.
if asset_id in holes and date in holes[asset_id]:
return missing_value
return expected_bar_value(asset_id, date, colname)
def expected_bar_values_2d(dates,
assets,
asset_info,
colname,
holes=None):
"""
Return an 2D array containing cls.expected_value(asset_id, date,
colname) for each date/asset pair in the inputs.
Missing locs are filled with 0 for volume and NaN for price columns:
- Values before/after an asset's lifetime.
- Values for asset_ids not contained in asset_info.
- Locs defined in `holes`.
"""
if colname == 'volume':
dtype = uint32
missing = 0
else:
dtype = float64
missing = float('nan')
data = full((len(dates), len(assets)), missing, dtype=dtype)
for j, asset in enumerate(assets):
# Use missing values when asset_id is not contained in asset_info.
if asset not in asset_info.index:
continue
start = asset_start(asset_info, asset)
end = asset_end(asset_info, asset)
for i, date in enumerate(dates):
# No value expected for dates outside the asset's start/end
# date.
if not (start <= date <= end):
continue
if holes is not None:
expected = expected_bar_value_with_holes(
asset,
date,
colname,
holes,
missing,
)
else:
expected = expected_bar_value(asset, date, colname)
data[i, j] = expected
return data
class NullAdjustmentReader(SQLiteAdjustmentReader):
"""
A SQLiteAdjustmentReader that stores no adjustments and uses in-memory
SQLite.
"""
def __init__(self):
conn = sqlite3_connect(':memory:')
writer = SQLiteAdjustmentWriter(conn, None, None)
empty = DataFrame({
'sid': array([], dtype=uint32),
'effective_date': array([], dtype=uint32),
'ratio': array([], dtype=float),
})
empty_dividends = DataFrame({
'sid': array([], dtype=uint32),
'amount': array([], dtype=float64),
'record_date': array([], dtype='datetime64[ns]'),
'ex_date': array([], dtype='datetime64[ns]'),
'declared_date': array([], dtype='datetime64[ns]'),
'pay_date': array([], dtype='datetime64[ns]'),
})
writer.write(splits=empty, mergers=empty, dividends=empty_dividends)
super(NullAdjustmentReader, self).__init__(conn) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/synthetic.py | synthetic.py |
from functools import partial
from interface import implements
from numpy import (
ix_,
zeros,
)
from pandas import (
DataFrame,
DatetimeIndex,
Index,
Int64Index,
)
from zipline.lib.adjusted_array import AdjustedArray
from zipline.lib.adjustment import make_adjustment_from_labels
from zipline.utils.numpy_utils import as_column
from .base import PipelineLoader
ADJUSTMENT_COLUMNS = Index([
'sid',
'value',
'kind',
'start_date',
'end_date',
'apply_date',
])
class DataFrameLoader(implements(PipelineLoader)):
"""
A PipelineLoader that reads its input from DataFrames.
Mostly useful for testing, but can also be used for real work if your data
fits in memory.
Parameters
----------
column : zipline.pipeline.data.BoundColumn
The column whose data is loadable by this loader.
baseline : pandas.DataFrame
A DataFrame with index of type DatetimeIndex and columns of type
Int64Index. Dates should be labelled with the first date on which a
value would be **available** to an algorithm. This means that OHLCV
data should generally be shifted back by a trading day before being
supplied to this class.
adjustments : pandas.DataFrame, default=None
A DataFrame with the following columns:
sid : int
value : any
kind : int (zipline.pipeline.loaders.frame.ADJUSTMENT_TYPES)
start_date : datetime64 (can be NaT)
end_date : datetime64 (must be set)
apply_date : datetime64 (must be set)
The default of None is interpreted as "no adjustments to the baseline".
"""
def __init__(self, column, baseline, adjustments=None):
self.column = column
self.baseline = baseline.values.astype(self.column.dtype)
self.dates = baseline.index
self.assets = baseline.columns
if adjustments is None:
adjustments = DataFrame(
index=DatetimeIndex([]),
columns=ADJUSTMENT_COLUMNS,
)
else:
# Ensure that columns are in the correct order.
adjustments = adjustments.reindex(ADJUSTMENT_COLUMNS, axis=1)
adjustments.sort_values(['apply_date', 'sid'], inplace=True)
self.adjustments = adjustments
self.adjustment_apply_dates = DatetimeIndex(adjustments.apply_date)
self.adjustment_end_dates = DatetimeIndex(adjustments.end_date)
self.adjustment_sids = Int64Index(adjustments.sid)
def format_adjustments(self, dates, assets):
"""
Build a dict of Adjustment objects in the format expected by
AdjustedArray.
Returns a dict of the form:
{
# Integer index into `dates` for the date on which we should
# apply the list of adjustments.
1 : [
Float64Multiply(first_row=2, last_row=4, col=3, value=0.5),
Float64Overwrite(first_row=3, last_row=5, col=1, value=2.0),
...
],
...
}
"""
make_adjustment = partial(make_adjustment_from_labels, dates, assets)
min_date, max_date = dates[[0, -1]]
# TODO: Consider porting this to Cython.
if len(self.adjustments) == 0:
return {}
# Mask for adjustments whose apply_dates are in the requested window of
# dates.
date_bounds = self.adjustment_apply_dates.slice_indexer(
min_date,
max_date,
)
dates_filter = zeros(len(self.adjustments), dtype='bool')
dates_filter[date_bounds] = True
# Ignore adjustments whose apply_date is in range, but whose end_date
# is out of range.
dates_filter &= (self.adjustment_end_dates >= min_date)
# Mask for adjustments whose sids are in the requested assets.
sids_filter = self.adjustment_sids.isin(assets.values)
adjustments_to_use = self.adjustments.loc[
dates_filter & sids_filter
].set_index('apply_date')
# For each apply_date on which we have an adjustment, compute
# the integer index of that adjustment's apply_date in `dates`.
# Then build a list of Adjustment objects for that apply_date.
# This logic relies on the sorting applied on the previous line.
out = {}
previous_apply_date = object()
for row in adjustments_to_use.itertuples():
# This expansion depends on the ordering of the DataFrame columns,
# defined above.
apply_date, sid, value, kind, start_date, end_date = row
if apply_date != previous_apply_date:
# Get the next apply date if no exact match.
row_loc = dates.get_loc(apply_date, method='bfill')
current_date_adjustments = out[row_loc] = []
previous_apply_date = apply_date
# Look up the approprate Adjustment constructor based on the value
# of `kind`.
current_date_adjustments.append(
make_adjustment(start_date, end_date, sid, kind, value)
)
return out
def load_adjusted_array(self, domain, columns, dates, sids, mask):
"""
Load data from our stored baseline.
"""
if len(columns) != 1:
raise ValueError(
"Can't load multiple columns with DataFrameLoader"
)
column = columns[0]
self._validate_input_column(column)
date_indexer = self.dates.get_indexer(dates)
assets_indexer = self.assets.get_indexer(sids)
# Boolean arrays with True on matched entries
good_dates = (date_indexer != -1)
good_assets = (assets_indexer != -1)
data = self.baseline[ix_(date_indexer, assets_indexer)]
mask = (good_assets & as_column(good_dates)) & mask
# Mask out requested columns/rows that didn't match.
data[~mask] = column.missing_value
return {
column: AdjustedArray(
# Pull out requested columns/rows from our baseline data.
data=data,
adjustments=self.format_adjustments(dates, sids),
missing_value=column.missing_value,
),
}
def _validate_input_column(self, column):
"""Make sure a passed column is our column.
"""
if column != self.column and column.unspecialize() != self.column:
raise ValueError("Can't load unknown column %s" % column) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/frame.py | frame.py |
import numpy as np
import pandas as pd
from zipline.errors import NoFurtherDataError
from zipline.pipeline.common import TS_FIELD_NAME, SID_FIELD_NAME
from zipline.utils.numpy_utils import categorical_dtype
def is_sorted_ascending(a):
"""Check if a numpy array is sorted."""
return (np.fmax.accumulate(a) <= a).all()
def validate_event_metadata(event_dates,
event_timestamps,
event_sids):
assert is_sorted_ascending(event_dates), "event dates must be sorted"
assert len(event_sids) == len(event_dates) == len(event_timestamps), \
"mismatched arrays: %d != %d != %d" % (
len(event_sids),
len(event_dates),
len(event_timestamps),
)
def next_event_indexer(all_dates,
data_query_cutoff,
all_sids,
event_dates,
event_timestamps,
event_sids):
"""
Construct an index array that, when applied to an array of values, produces
a 2D array containing the values associated with the next event for each
sid at each moment in time.
Locations where no next event was known will be filled with -1.
Parameters
----------
all_dates : ndarray[datetime64[ns], ndim=1]
Row labels for the target output.
data_query_cutoff : pd.DatetimeIndex
The boundaries for the given trading sessions in ``all_dates``.
all_sids : ndarray[int, ndim=1]
Column labels for the target output.
event_dates : ndarray[datetime64[ns], ndim=1]
Dates on which each input events occurred/will occur. ``event_dates``
must be in sorted order, and may not contain any NaT values.
event_timestamps : ndarray[datetime64[ns], ndim=1]
Dates on which we learned about each input event.
event_sids : ndarray[int, ndim=1]
Sids assocated with each input event.
Returns
-------
indexer : ndarray[int, ndim=2]
An array of shape (len(all_dates), len(all_sids)) of indices into
``event_{dates,timestamps,sids}``.
"""
validate_event_metadata(event_dates, event_timestamps, event_sids)
out = np.full((len(all_dates), len(all_sids)), -1, dtype=np.int64)
sid_ixs = all_sids.searchsorted(event_sids)
# side='right' here ensures that we include the event date itself
# if it's in all_dates.
dt_ixs = all_dates.searchsorted(event_dates, side='right')
ts_ixs = data_query_cutoff.searchsorted(event_timestamps, side='right')
# Walk backward through the events, writing the index of the event into
# slots ranging from the event's timestamp to its asof. This depends for
# correctness on the fact that event_dates is sorted in ascending order,
# because we need to overwrite later events with earlier ones if their
# eligible windows overlap.
for i in range(len(event_sids) - 1, -1, -1):
start_ix = ts_ixs[i]
end_ix = dt_ixs[i]
out[start_ix:end_ix, sid_ixs[i]] = i
return out
def previous_event_indexer(data_query_cutoff_times,
all_sids,
event_dates,
event_timestamps,
event_sids):
"""
Construct an index array that, when applied to an array of values, produces
a 2D array containing the values associated with the previous event for
each sid at each moment in time.
Locations where no previous event was known will be filled with -1.
Parameters
----------
data_query_cutoff : pd.DatetimeIndex
The boundaries for the given trading sessions.
all_dates : ndarray[datetime64[ns], ndim=1]
Row labels for the target output.
all_sids : ndarray[int, ndim=1]
Column labels for the target output.
event_dates : ndarray[datetime64[ns], ndim=1]
Dates on which each input events occurred/will occur. ``event_dates``
must be in sorted order, and may not contain any NaT values.
event_timestamps : ndarray[datetime64[ns], ndim=1]
Dates on which we learned about each input event.
event_sids : ndarray[int, ndim=1]
Sids assocated with each input event.
Returns
-------
indexer : ndarray[int, ndim=2]
An array of shape (len(all_dates), len(all_sids)) of indices into
``event_{dates,timestamps,sids}``.
"""
validate_event_metadata(event_dates, event_timestamps, event_sids)
out = np.full(
(len(data_query_cutoff_times), len(all_sids)),
-1,
dtype=np.int64,
)
eff_dts = np.maximum(event_dates, event_timestamps)
sid_ixs = all_sids.searchsorted(event_sids)
dt_ixs = data_query_cutoff_times.searchsorted(eff_dts, side='right')
# Walk backwards through the events, writing the index of the event into
# slots ranging from max(event_date, event_timestamp) to the start of the
# previously-written event. This depends for correctness on the fact that
# event_dates is sorted in ascending order, because we need to have written
# later events so we know where to stop forward-filling earlier events.
last_written = {}
for i in range(len(event_dates) - 1, -1, -1):
sid_ix = sid_ixs[i]
dt_ix = dt_ixs[i]
out[dt_ix:last_written.get(sid_ix, None), sid_ix] = i
last_written[sid_ix] = dt_ix
return out
def last_in_date_group(df,
data_query_cutoff_times,
assets,
reindex=True,
have_sids=True,
extra_groupers=None):
"""
Determine the last piece of information known on each date in the date
index for each group. Input df MUST be sorted such that the correct last
item is chosen from each group.
Parameters
----------
df : pd.DataFrame
The DataFrame containing the data to be grouped. Must be sorted so that
the correct last item is chosen from each group.
data_query_cutoff_times : pd.DatetimeIndex
The dates to use for grouping and reindexing.
assets : pd.Int64Index
The assets that should be included in the column multiindex.
reindex : bool
Whether or not the DataFrame should be reindexed against the date
index. This will add back any dates to the index that were grouped
away.
have_sids : bool
Whether or not the DataFrame has sids. If it does, they will be used
in the groupby.
extra_groupers : list of str
Any extra field names that should be included in the groupby.
Returns
-------
last_in_group : pd.DataFrame
A DataFrame with dates as the index and fields used in the groupby as
levels of a multiindex of columns.
"""
idx = [data_query_cutoff_times[data_query_cutoff_times.searchsorted(
pd.DatetimeIndex(df[TS_FIELD_NAME]),
)]]
if have_sids:
idx += [SID_FIELD_NAME]
if extra_groupers is None:
extra_groupers = []
idx += extra_groupers
last_in_group = df.drop(TS_FIELD_NAME, axis=1).groupby(
idx,
sort=False,
).last()
# For the number of things that we're grouping by (except TS), unstack
# the df. Done this way because of an unresolved pandas bug whereby
# passing a list of levels with mixed dtypes to unstack causes the
# resulting DataFrame to have all object-type columns.
for _ in range(len(idx) - 1):
last_in_group = last_in_group.unstack(-1)
if reindex:
if have_sids:
cols = last_in_group.columns
last_in_group = last_in_group.reindex(
index=data_query_cutoff_times,
columns=pd.MultiIndex.from_product(
tuple(cols.levels[0:len(extra_groupers) + 1]) + (assets,),
names=cols.names,
),
)
else:
last_in_group = last_in_group.reindex(data_query_cutoff_times)
return last_in_group
def ffill_across_cols(df, columns, name_map):
"""
Forward fill values in a DataFrame with special logic to handle cases
that pd.DataFrame.ffill cannot and cast columns to appropriate types.
Parameters
----------
df : pd.DataFrame
The DataFrame to do forward-filling on.
columns : list of BoundColumn
The BoundColumns that correspond to columns in the DataFrame to which
special filling and/or casting logic should be applied.
name_map: map of string -> string
Mapping from the name of each BoundColumn to the associated column
name in `df`.
"""
df.ffill(inplace=True)
# Fill in missing values specified by each column. This is made
# significantly more complex by the fact that we need to work around
# two pandas issues:
# 1) When we have sids, if there are no records for a given sid for any
# dates, pandas will generate a column full of NaNs for that sid.
# This means that some of the columns in `dense_output` are now
# float instead of the intended dtype, so we have to coerce back to
# our expected type and convert NaNs into the desired missing value.
# 2) DataFrame.ffill assumes that receiving None as a fill-value means
# that no value was passed. Consequently, there's no way to tell
# pandas to replace NaNs in an object column with None using fillna,
# so we have to roll our own instead using df.where.
for column in columns:
column_name = name_map[column.name]
# Special logic for strings since `fillna` doesn't work if the
# missing value is `None`.
if column.dtype == categorical_dtype:
df[column_name] = df[
column.name
].where(pd.notnull(df[column_name]),
column.missing_value)
else:
# We need to execute `fillna` before `astype` in case the
# column contains NaNs and needs to be cast to bool or int.
# This is so that the NaNs are replaced first, since pandas
# can't convert NaNs for those types.
df[column_name] = df[
column_name
].fillna(column.missing_value)
#].fillna(column.missing_value).astype(column.dtype)
def shift_dates(dates, start_date, end_date, shift):
"""
Shift dates of a pipeline query back by ``shift`` days.
Parameters
----------
dates : DatetimeIndex
All known dates.
start_date : pd.Timestamp
Start date of the pipeline query.
end_date : pd.Timestamp
End date of the pipeline query.
shift : int
The number of days to shift back the query dates.
Returns
-------
shifted : pd.DatetimeIndex
The range [start_date, end_date] from ``dates``, shifted backwards by
``shift`` days.
Raises
------
ValueError
If ``start_date`` or ``end_date`` is not in ``dates``.
NoFurtherDataError
If shifting ``start_date`` back by ``shift`` days would push it off the
end of ``dates``.
"""
try:
start = dates.get_loc(start_date)
except KeyError:
if start_date < dates[0]:
raise NoFurtherDataError(
msg=(
"Pipeline Query requested data starting on {query_start}, "
"but first known date is {calendar_start}"
).format(
query_start=str(start_date),
calendar_start=str(dates[0]),
)
)
else:
raise ValueError("Query start %s not in calendar" % start_date)
# Make sure that shifting doesn't push us out of the calendar.
if start < shift:
raise NoFurtherDataError(
msg=(
"Pipeline Query requested data from {shift}"
" days before {query_start}, but first known date is only "
"{start} days earlier."
).format(shift=shift, query_start=start_date, start=start),
)
try:
end = dates.get_loc(end_date)
except KeyError:
if end_date > dates[-1]:
raise NoFurtherDataError(
msg=(
"Pipeline Query requesting data up to {query_end}, "
"but last known date is {calendar_end}"
).format(
query_end=end_date,
calendar_end=dates[-1],
)
)
else:
raise ValueError("Query end %s not in calendar" % end_date)
return dates[start - shift:end - shift + 1] # +1 to be inclusive | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/utils.py | utils.py |
from abc import abstractmethod, abstractproperty
from interface import implements
import numpy as np
import pandas as pd
from six import viewvalues
from toolz import groupby
from zipline.lib.adjusted_array import AdjustedArray
from zipline.lib.adjustment import (
Datetime641DArrayOverwrite,
Datetime64Overwrite,
Float641DArrayOverwrite,
Float64Multiply,
Float64Overwrite,
)
from zipline.pipeline.common import (
EVENT_DATE_FIELD_NAME,
FISCAL_QUARTER_FIELD_NAME,
FISCAL_YEAR_FIELD_NAME,
SID_FIELD_NAME,
TS_FIELD_NAME,
)
from zipline.pipeline.loaders.base import PipelineLoader
from zipline.utils.numpy_utils import datetime64ns_dtype, float64_dtype
from zipline.pipeline.loaders.utils import (
ffill_across_cols,
last_in_date_group,
)
INVALID_NUM_QTRS_MESSAGE = "Passed invalid number of quarters %s; " \
"must pass a number of quarters >= 0"
NEXT_FISCAL_QUARTER = 'next_fiscal_quarter'
NEXT_FISCAL_YEAR = 'next_fiscal_year'
NORMALIZED_QUARTERS = 'normalized_quarters'
PREVIOUS_FISCAL_QUARTER = 'previous_fiscal_quarter'
PREVIOUS_FISCAL_YEAR = 'previous_fiscal_year'
SHIFTED_NORMALIZED_QTRS = 'shifted_normalized_quarters'
SIMULATION_DATES = 'dates'
def normalize_quarters(years, quarters):
return years * 4 + quarters - 1
def split_normalized_quarters(normalized_quarters):
years = normalized_quarters // 4
quarters = normalized_quarters % 4
return years, quarters + 1
# These metadata columns are used to align event indexers.
metadata_columns = frozenset({
TS_FIELD_NAME,
SID_FIELD_NAME,
EVENT_DATE_FIELD_NAME,
FISCAL_QUARTER_FIELD_NAME,
FISCAL_YEAR_FIELD_NAME,
})
def required_estimates_fields(columns):
"""
Compute the set of resource columns required to serve
`columns`.
"""
# We also expect any of the field names that our loadable columns
# are mapped to.
return metadata_columns.union(viewvalues(columns))
def validate_column_specs(events, columns):
"""
Verify that the columns of ``events`` can be used by a
EarningsEstimatesLoader to serve the BoundColumns described by
`columns`.
"""
required = required_estimates_fields(columns)
received = set(events.columns)
missing = required - received
if missing:
raise ValueError(
"EarningsEstimatesLoader missing required columns {missing}.\n"
"Got Columns: {received}\n"
"Expected Columns: {required}".format(
missing=sorted(missing),
received=sorted(received),
required=sorted(required),
)
)
def add_new_adjustments(adjustments_dict,
adjustments,
column_name,
ts):
try:
adjustments_dict[column_name][ts].extend(adjustments)
except KeyError:
adjustments_dict[column_name][ts] = adjustments
class EarningsEstimatesLoader(implements(PipelineLoader)):
"""
An abstract pipeline loader for estimates data that can load data a
variable number of quarters forwards/backwards from calendar dates
depending on the `num_announcements` attribute of the columns' dataset.
If split adjustments are to be applied, a loader, split-adjusted columns,
and the split-adjusted asof-date must be supplied.
Parameters
----------
estimates : pd.DataFrame
The raw estimates data.
``estimates`` must contain at least 5 columns:
sid : int64
The asset id associated with each estimate.
event_date : datetime64[ns]
The date on which the event that the estimate is for will/has
occurred..
timestamp : datetime64[ns]
The datetime where we learned about the estimate.
fiscal_quarter : int64
The quarter during which the event has/will occur.
fiscal_year : int64
The year during which the event has/will occur.
name_map : dict[str -> str]
A map of names of BoundColumns that this loader will load to the
names of the corresponding columns in `events`.
"""
def __init__(self, estimates, name_map):
validate_column_specs(
estimates,
name_map
)
self.estimates = estimates[
estimates[EVENT_DATE_FIELD_NAME].notnull() &
estimates[FISCAL_QUARTER_FIELD_NAME].notnull() &
estimates[FISCAL_YEAR_FIELD_NAME].notnull()
]
self.estimates[NORMALIZED_QUARTERS] = normalize_quarters(
self.estimates[FISCAL_YEAR_FIELD_NAME],
self.estimates[FISCAL_QUARTER_FIELD_NAME],
)
self.array_overwrites_dict = {
datetime64ns_dtype: Datetime641DArrayOverwrite,
float64_dtype: Float641DArrayOverwrite,
}
self.scalar_overwrites_dict = {
datetime64ns_dtype: Datetime64Overwrite,
float64_dtype: Float64Overwrite,
}
self.name_map = name_map
@abstractmethod
def get_zeroth_quarter_idx(self, stacked_last_per_qtr):
raise NotImplementedError('get_zeroth_quarter_idx')
@abstractmethod
def get_shifted_qtrs(self, zero_qtrs, num_announcements):
raise NotImplementedError('get_shifted_qtrs')
@abstractmethod
def create_overwrite_for_estimate(self,
column,
column_name,
last_per_qtr,
next_qtr_start_idx,
requested_quarter,
sid,
sid_idx,
col_to_split_adjustments,
split_adjusted_asof_idx):
raise NotImplementedError('create_overwrite_for_estimate')
@abstractproperty
def searchsorted_side(self):
return NotImplementedError('searchsorted_side')
def get_requested_quarter_data(self,
zero_qtr_data,
zeroth_quarter_idx,
stacked_last_per_qtr,
num_announcements,
dates):
"""
Selects the requested data for each date.
Parameters
----------
zero_qtr_data : pd.DataFrame
The 'time zero' data for each calendar date per sid.
zeroth_quarter_idx : pd.Index
An index of calendar dates, sid, and normalized quarters, for only
the rows that have a next or previous earnings estimate.
stacked_last_per_qtr : pd.DataFrame
The latest estimate known with the dates, normalized quarter, and
sid as the index.
num_announcements : int
The number of annoucements out the user requested relative to
each date in the calendar dates.
dates : pd.DatetimeIndex
The calendar dates for which estimates data is requested.
Returns
--------
requested_qtr_data : pd.DataFrame
The DataFrame with the latest values for the requested quarter
for all columns; `dates` are the index and columns are a MultiIndex
with sids at the top level and the dataset columns on the bottom.
"""
zero_qtr_data_idx = zero_qtr_data.index
requested_qtr_idx = pd.MultiIndex.from_arrays(
[
zero_qtr_data_idx.get_level_values(0),
zero_qtr_data_idx.get_level_values(1),
self.get_shifted_qtrs(
zeroth_quarter_idx.get_level_values(
NORMALIZED_QUARTERS,
),
num_announcements,
),
],
names=[
zero_qtr_data_idx.names[0],
zero_qtr_data_idx.names[1],
SHIFTED_NORMALIZED_QTRS,
],
)
requested_qtr_data = stacked_last_per_qtr.loc[requested_qtr_idx]
requested_qtr_data = requested_qtr_data.reset_index(
SHIFTED_NORMALIZED_QTRS,
)
# Calculate the actual year/quarter being requested and add those in
# as columns.
(requested_qtr_data[FISCAL_YEAR_FIELD_NAME],
requested_qtr_data[FISCAL_QUARTER_FIELD_NAME]) = \
split_normalized_quarters(
requested_qtr_data[SHIFTED_NORMALIZED_QTRS]
)
# Once we're left with just dates as the index, we can reindex by all
# dates so that we have a value for each calendar date.
return requested_qtr_data.unstack(SID_FIELD_NAME).reindex(dates)
def get_split_adjusted_asof_idx(self, dates):
"""
Compute the index in `dates` where the split-adjusted-asof-date
falls. This is the date up to which, and including which, we will
need to unapply all adjustments for and then re-apply them as they
come in. After this date, adjustments are applied as normal.
Parameters
----------
dates : pd.DatetimeIndex
The calendar dates over which the Pipeline is being computed.
Returns
-------
split_adjusted_asof_idx : int
The index in `dates` at which the data should be split.
"""
split_adjusted_asof_idx = dates.searchsorted(
self._split_adjusted_asof
)
# The split-asof date is after the date index.
if split_adjusted_asof_idx == len(dates):
split_adjusted_asof_idx = len(dates) - 1
elif self._split_adjusted_asof < dates[0].tz_localize(None):
split_adjusted_asof_idx = -1
return split_adjusted_asof_idx
def collect_overwrites_for_sid(self,
group,
dates,
requested_qtr_data,
last_per_qtr,
sid_idx,
columns,
all_adjustments_for_sid,
sid):
"""
Given a sid, collect all overwrites that should be applied for this
sid at each quarter boundary.
Parameters
----------
group : pd.DataFrame
The data for `sid`.
dates : pd.DatetimeIndex
The calendar dates for which estimates data is requested.
requested_qtr_data : pd.DataFrame
The DataFrame with the latest values for the requested quarter
for all columns.
last_per_qtr : pd.DataFrame
A DataFrame with a column MultiIndex of [self.estimates.columns,
normalized_quarters, sid] that allows easily getting the timeline
of estimates for a particular sid for a particular quarter.
sid_idx : int
The sid's index in the asset index.
columns : list of BoundColumn
The columns for which the overwrites should be computed.
all_adjustments_for_sid : dict[int -> AdjustedArray]
A dictionary of the integer index of each timestamp into the date
index, mapped to adjustments that should be applied at that
index for the given sid (`sid`). This dictionary is modified as
adjustments are collected.
sid : int
The sid for which overwrites should be computed.
"""
# If data was requested for only 1 date, there can never be any
# overwrites, so skip the extra work.
if len(dates) == 1:
return
next_qtr_start_indices = dates.searchsorted(
group[EVENT_DATE_FIELD_NAME].values,
side=self.searchsorted_side,
)
qtrs_with_estimates = group.index.get_level_values(
NORMALIZED_QUARTERS
).values
for idx in next_qtr_start_indices:
if 0 < idx < len(dates):
# Find the quarter being requested in the quarter we're
# crossing into.
requested_quarter = requested_qtr_data[
SHIFTED_NORMALIZED_QTRS, sid,
].iloc[idx]
# Only add adjustments if the next quarter starts somewhere
# in our date index for this sid. Our 'next' quarter can
# never start at index 0; a starting index of 0 means that
# the next quarter's event date was NaT.
self.create_overwrites_for_quarter(
all_adjustments_for_sid,
idx,
last_per_qtr,
qtrs_with_estimates,
requested_quarter,
sid,
sid_idx,
columns
)
def get_adjustments_for_sid(self,
group,
dates,
requested_qtr_data,
last_per_qtr,
sid_to_idx,
columns,
col_to_all_adjustments,
**kwargs):
"""
Parameters
----------
group : pd.DataFrame
The data for the given sid.
dates : pd.DatetimeIndex
The calendar dates for which estimates data is requested.
requested_qtr_data : pd.DataFrame
The DataFrame with the latest values for the requested quarter
for all columns.
last_per_qtr : pd.DataFrame
A DataFrame with a column MultiIndex of [self.estimates.columns,
normalized_quarters, sid] that allows easily getting the timeline
of estimates for a particular sid for a particular quarter.
sid_to_idx : dict[int -> int]
A dictionary mapping sid to he sid's index in the asset index.
columns : list of BoundColumn
The columns for which the overwrites should be computed.
col_to_all_adjustments : dict[int -> AdjustedArray]
A dictionary of the integer index of each timestamp into the date
index, mapped to adjustments that should be applied at that
index. This dictionary is for adjustments for ALL sids. It is
modified as adjustments are collected.
kwargs :
Additional arguments used in collecting adjustments; unused here.
"""
# Collect all adjustments for a given sid.
all_adjustments_for_sid = {}
sid = int(group.name)
self.collect_overwrites_for_sid(group,
dates,
requested_qtr_data,
last_per_qtr,
sid_to_idx[sid],
columns,
all_adjustments_for_sid,
sid)
self.merge_into_adjustments_for_all_sids(
all_adjustments_for_sid, col_to_all_adjustments
)
def merge_into_adjustments_for_all_sids(self,
all_adjustments_for_sid,
col_to_all_adjustments):
"""
Merge adjustments for a particular sid into a dictionary containing
adjustments for all sids.
Parameters
----------
all_adjustments_for_sid : dict[int -> AdjustedArray]
All adjustments for a particular sid.
col_to_all_adjustments : dict[int -> AdjustedArray]
All adjustments for all sids.
"""
for col_name in all_adjustments_for_sid:
if col_name not in col_to_all_adjustments:
col_to_all_adjustments[col_name] = {}
for ts in all_adjustments_for_sid[col_name]:
adjs = all_adjustments_for_sid[col_name][ts]
add_new_adjustments(col_to_all_adjustments,
adjs,
col_name,
ts)
def get_adjustments(self,
zero_qtr_data,
requested_qtr_data,
last_per_qtr,
dates,
assets,
columns,
**kwargs):
"""
Creates an AdjustedArray from the given estimates data for the given
dates.
Parameters
----------
zero_qtr_data : pd.DataFrame
The 'time zero' data for each calendar date per sid.
requested_qtr_data : pd.DataFrame
The requested quarter data for each calendar date per sid.
last_per_qtr : pd.DataFrame
A DataFrame with a column MultiIndex of [self.estimates.columns,
normalized_quarters, sid] that allows easily getting the timeline
of estimates for a particular sid for a particular quarter.
dates : pd.DatetimeIndex
The calendar dates for which estimates data is requested.
assets : pd.Int64Index
An index of all the assets from the raw data.
columns : list of BoundColumn
The columns for which adjustments need to be calculated.
kwargs :
Additional keyword arguments that should be forwarded to
`get_adjustments_for_sid` and to be used in computing adjustments
for each sid.
Returns
-------
col_to_all_adjustments : dict[int -> AdjustedArray]
A dictionary of all adjustments that should be applied.
"""
zero_qtr_data.sort_index(inplace=True)
# Here we want to get the LAST record from each group of records
# corresponding to a single quarter. This is to ensure that we select
# the most up-to-date event date in case the event date changes.
quarter_shifts = zero_qtr_data.groupby(
level=[SID_FIELD_NAME, NORMALIZED_QUARTERS]
).nth(-1)
col_to_all_adjustments = {}
sid_to_idx = dict(zip(assets, range(len(assets))))
quarter_shifts.groupby(level=SID_FIELD_NAME).apply(
self.get_adjustments_for_sid,
dates,
requested_qtr_data,
last_per_qtr,
sid_to_idx,
columns,
col_to_all_adjustments,
**kwargs
)
return col_to_all_adjustments
def create_overwrites_for_quarter(self,
col_to_overwrites,
next_qtr_start_idx,
last_per_qtr,
quarters_with_estimates_for_sid,
requested_quarter,
sid,
sid_idx,
columns):
"""
Add entries to the dictionary of columns to adjustments for the given
sid and the given quarter.
Parameters
----------
col_to_overwrites : dict [column_name -> list of ArrayAdjustment]
A dictionary mapping column names to all overwrites for those
columns.
next_qtr_start_idx : int
The index of the first day of the next quarter in the calendar
dates.
last_per_qtr : pd.DataFrame
A DataFrame with a column MultiIndex of [self.estimates.columns,
normalized_quarters, sid] that allows easily getting the timeline
of estimates for a particular sid for a particular quarter; this
is particularly useful for getting adjustments for 'next'
estimates.
quarters_with_estimates_for_sid : np.array
An array of all quarters for which there are estimates for the
given sid.
requested_quarter : float
The quarter for which the overwrite should be created.
sid : int
The sid for which to create overwrites.
sid_idx : int
The index of the sid in `assets`.
columns : list of BoundColumn
The columns for which to create overwrites.
"""
for col in columns:
column_name = self.name_map[col.name]
if column_name not in col_to_overwrites:
col_to_overwrites[column_name] = {}
# If there are estimates for the requested quarter,
# overwrite all values going up to the starting index of
# that quarter with estimates for that quarter.
if requested_quarter in quarters_with_estimates_for_sid:
adjs = self.create_overwrite_for_estimate(
col,
column_name,
last_per_qtr,
next_qtr_start_idx,
requested_quarter,
sid,
sid_idx,
)
add_new_adjustments(col_to_overwrites,
adjs,
column_name,
next_qtr_start_idx)
# There are no estimates for the quarter. Overwrite all
# values going up to the starting index of that quarter
# with the missing value for this column.
else:
adjs = [self.overwrite_with_null(
col,
next_qtr_start_idx,
sid_idx)]
add_new_adjustments(col_to_overwrites,
adjs,
column_name,
next_qtr_start_idx)
def overwrite_with_null(self,
column,
next_qtr_start_idx,
sid_idx):
return self.scalar_overwrites_dict[column.dtype](
0,
next_qtr_start_idx - 1,
sid_idx,
sid_idx,
column.missing_value
)
def load_adjusted_array(self, domain, columns, dates, sids, mask):
# Separate out getting the columns' datasets and the datasets'
# num_announcements attributes to ensure that we're catching the right
# AttributeError.
col_to_datasets = {col: col.dataset for col in columns}
try:
groups = groupby(lambda col:
col_to_datasets[col].num_announcements,
col_to_datasets)
except AttributeError:
raise AttributeError("Datasets loaded via the "
"EarningsEstimatesLoader must define a "
"`num_announcements` attribute that defines "
"how many quarters out the loader should load"
" the data relative to `dates`.")
if any(num_qtr < 0 for num_qtr in groups):
raise ValueError(
INVALID_NUM_QTRS_MESSAGE % ','.join(
str(qtr) for qtr in groups if qtr < 0
)
)
out = {}
# To optimize performance, only work below on assets that are
# actually in the raw data.
data_query_cutoff_times = domain.data_query_cutoff_for_sessions(dates)
assets_with_data = set(sids) & set(self.estimates[SID_FIELD_NAME])
last_per_qtr, stacked_last_per_qtr = self.get_last_data_per_qtr(
assets_with_data,
columns,
dates,
data_query_cutoff_times,
)
# Determine which quarter is immediately next/previous for each
# date.
zeroth_quarter_idx = self.get_zeroth_quarter_idx(stacked_last_per_qtr)
zero_qtr_data = stacked_last_per_qtr.loc[zeroth_quarter_idx]
for num_announcements, columns in groups.items():
requested_qtr_data = self.get_requested_quarter_data(
zero_qtr_data,
zeroth_quarter_idx,
stacked_last_per_qtr,
num_announcements,
dates,
)
# Calculate all adjustments for the given quarter and accumulate
# them for each column.
col_to_adjustments = self.get_adjustments(
zero_qtr_data,
requested_qtr_data,
last_per_qtr,
dates,
sids,
columns
)
# Lookup the asset indexer once, this is so we can reindex
# the assets returned into the assets requested for each column.
# This depends on the fact that our column multiindex has the same
# sids for each field. This allows us to do the lookup once on
# level 1 instead of doing the lookup each time per value in
# level 0.
asset_indexer = sids.get_indexer_for(
requested_qtr_data.columns.levels[1],
)
for col in columns:
column_name = self.name_map[col.name]
# allocate the empty output with the correct missing value
output_array = np.full(
(len(dates), len(sids)),
col.missing_value,
dtype=col.dtype,
)
# overwrite the missing value with values from the computed
# data
output_array[
:,
asset_indexer,
] = requested_qtr_data[column_name].values
out[col] = AdjustedArray(
output_array,
# There may not be any adjustments at all (e.g. if
# len(date) == 1), so provide a default.
dict(col_to_adjustments.get(column_name, {})),
col.missing_value,
)
return out
def get_last_data_per_qtr(self,
assets_with_data,
columns,
dates,
data_query_cutoff_times):
"""
Determine the last piece of information we know for each column on each
date in the index for each sid and quarter.
Parameters
----------
assets_with_data : pd.Index
Index of all assets that appear in the raw data given to the
loader.
columns : iterable of BoundColumn
The columns that need to be loaded from the raw data.
data_query_cutoff_times : pd.DatetimeIndex
The calendar of dates for which data should be loaded.
Returns
-------
stacked_last_per_qtr : pd.DataFrame
A DataFrame indexed by [dates, sid, normalized_quarters] that has
the latest information for each row of the index, sorted by event
date.
last_per_qtr : pd.DataFrame
A DataFrame with columns that are a MultiIndex of [
self.estimates.columns, normalized_quarters, sid].
"""
# Get a DataFrame indexed by date with a MultiIndex of columns of
# [self.estimates.columns, normalized_quarters, sid], where each cell
# contains the latest data for that day.
last_per_qtr = last_in_date_group(
self.estimates,
data_query_cutoff_times,
assets_with_data,
reindex=True,
extra_groupers=[NORMALIZED_QUARTERS],
)
last_per_qtr.index = dates
# Forward fill values for each quarter/sid/dataset column.
ffill_across_cols(last_per_qtr, columns, self.name_map)
# Stack quarter and sid into the index.
stacked_last_per_qtr = last_per_qtr.stack(
[SID_FIELD_NAME, NORMALIZED_QUARTERS],
)
# Set date index name for ease of reference
stacked_last_per_qtr.index.set_names(
SIMULATION_DATES,
level=0,
inplace=True,
)
stacked_last_per_qtr = stacked_last_per_qtr.sort_values(
EVENT_DATE_FIELD_NAME,
)
stacked_last_per_qtr[EVENT_DATE_FIELD_NAME] = pd.to_datetime(
stacked_last_per_qtr[EVENT_DATE_FIELD_NAME]
)
return last_per_qtr, stacked_last_per_qtr
class NextEarningsEstimatesLoader(EarningsEstimatesLoader):
searchsorted_side = 'right'
def create_overwrite_for_estimate(self,
column,
column_name,
last_per_qtr,
next_qtr_start_idx,
requested_quarter,
sid,
sid_idx,
col_to_split_adjustments=None,
split_adjusted_asof_idx=None):
return [self.array_overwrites_dict[column.dtype](
0,
next_qtr_start_idx - 1,
sid_idx,
sid_idx,
last_per_qtr[
column_name,
requested_quarter,
sid,
].values[:next_qtr_start_idx],
)]
def get_shifted_qtrs(self, zero_qtrs, num_announcements):
return zero_qtrs + (num_announcements - 1)
def get_zeroth_quarter_idx(self, stacked_last_per_qtr):
"""
Filters for releases that are on or after each simulation date and
determines the next quarter by picking out the upcoming release for
each date in the index.
Parameters
----------
stacked_last_per_qtr : pd.DataFrame
A DataFrame with index of calendar dates, sid, and normalized
quarters with each row being the latest estimate for the row's
index values, sorted by event date.
Returns
-------
next_releases_per_date_index : pd.MultiIndex
An index of calendar dates, sid, and normalized quarters, for only
the rows that have a next event.
"""
next_releases_per_date = stacked_last_per_qtr.loc[
stacked_last_per_qtr[EVENT_DATE_FIELD_NAME] >=
stacked_last_per_qtr.index.get_level_values(SIMULATION_DATES)
].groupby(
level=[SIMULATION_DATES, SID_FIELD_NAME],
as_index=False,
# Here we take advantage of the fact that `stacked_last_per_qtr` is
# sorted by event date.
).nth(0)
return next_releases_per_date.index
class PreviousEarningsEstimatesLoader(EarningsEstimatesLoader):
searchsorted_side = 'left'
def create_overwrite_for_estimate(self,
column,
column_name,
dates,
next_qtr_start_idx,
requested_quarter,
sid,
sid_idx,
col_to_split_adjustments=None,
split_adjusted_asof_idx=None,
split_dict=None):
return [self.overwrite_with_null(
column,
next_qtr_start_idx,
sid_idx,
)]
def get_shifted_qtrs(self, zero_qtrs, num_announcements):
return zero_qtrs - (num_announcements - 1)
def get_zeroth_quarter_idx(self, stacked_last_per_qtr):
"""
Filters for releases that are on or after each simulation date and
determines the previous quarter by picking out the most recent
release relative to each date in the index.
Parameters
----------
stacked_last_per_qtr : pd.DataFrame
A DataFrame with index of calendar dates, sid, and normalized
quarters with each row being the latest estimate for the row's
index values, sorted by event date.
Returns
-------
previous_releases_per_date_index : pd.MultiIndex
An index of calendar dates, sid, and normalized quarters, for only
the rows that have a previous event.
"""
previous_releases_per_date = stacked_last_per_qtr.loc[
stacked_last_per_qtr[EVENT_DATE_FIELD_NAME] <=
stacked_last_per_qtr.index.get_level_values(SIMULATION_DATES)
].groupby(
level=[SIMULATION_DATES, SID_FIELD_NAME],
as_index=False,
# Here we take advantage of the fact that `stacked_last_per_qtr` is
# sorted by event date.
).nth(-1)
return previous_releases_per_date.index
def validate_split_adjusted_column_specs(name_map, columns):
to_be_split = set(columns)
available = set(name_map.keys())
extra = to_be_split - available
if extra:
raise ValueError(
"EarningsEstimatesLoader got the following extra columns to be "
"split-adjusted: {extra}.\n"
"Got Columns: {to_be_split}\n"
"Available Columns: {available}".format(
extra=sorted(extra),
to_be_split=sorted(to_be_split),
available=sorted(available),
)
)
class SplitAdjustedEstimatesLoader(EarningsEstimatesLoader):
"""
Estimates loader that loads data that needs to be split-adjusted.
Parameters
----------
split_adjustments_loader : SQLiteAdjustmentReader
The loader to use for reading split adjustments.
split_adjusted_column_names : iterable of str
The column names that should be split-adjusted.
split_adjusted_asof : pd.Timestamp
The date that separates data into 2 halves: the first half is the set
of dates up to and including the split_adjusted_asof date. All
adjustments occurring during this first half are applied to all
dates in this first half. The second half is the set of dates after
the split_adjusted_asof date. All adjustments occurring during this
second half are applied sequentially as they appear in the timeline.
"""
def __init__(self,
estimates,
name_map,
split_adjustments_loader,
split_adjusted_column_names,
split_adjusted_asof):
validate_split_adjusted_column_specs(name_map,
split_adjusted_column_names)
self._split_adjustments = split_adjustments_loader
self._split_adjusted_column_names = split_adjusted_column_names
self._split_adjusted_asof = split_adjusted_asof
self._split_adjustment_dict = {}
super(SplitAdjustedEstimatesLoader, self).__init__(
estimates,
name_map
)
@abstractmethod
def collect_split_adjustments(self,
adjustments_for_sid,
requested_qtr_data,
dates,
sid,
sid_idx,
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
requested_split_adjusted_columns):
raise NotImplementedError('collect_split_adjustments')
def get_adjustments_for_sid(self,
group,
dates,
requested_qtr_data,
last_per_qtr,
sid_to_idx,
columns,
col_to_all_adjustments,
split_adjusted_asof_idx=None,
split_adjusted_cols_for_group=None):
"""
Collects both overwrites and adjustments for a particular sid.
Parameters
----------
split_adjusted_asof_idx : int
The integer index of the date on which the data was split-adjusted.
split_adjusted_cols_for_group : list of str
The names of requested columns that should also be split-adjusted.
"""
all_adjustments_for_sid = {}
sid = int(group.name)
self.collect_overwrites_for_sid(group,
dates,
requested_qtr_data,
last_per_qtr,
sid_to_idx[sid],
columns,
all_adjustments_for_sid,
sid)
(pre_adjustments,
post_adjustments) = self.retrieve_split_adjustment_data_for_sid(
dates, sid, split_adjusted_asof_idx
)
sid_estimates = self.estimates[
self.estimates[SID_FIELD_NAME] == sid
]
# We might not have any overwrites but still have
# adjustments, and we will need to manually add columns if
# that is the case.
for col_name in split_adjusted_cols_for_group:
if col_name not in all_adjustments_for_sid:
all_adjustments_for_sid[col_name] = {}
self.collect_split_adjustments(
all_adjustments_for_sid,
requested_qtr_data,
dates,
sid,
sid_to_idx[sid],
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
split_adjusted_cols_for_group
)
self.merge_into_adjustments_for_all_sids(
all_adjustments_for_sid, col_to_all_adjustments
)
def get_adjustments(self,
zero_qtr_data,
requested_qtr_data,
last_per_qtr,
dates,
assets,
columns,
**kwargs):
"""
Calculates both split adjustments and overwrites for all sids.
"""
split_adjusted_cols_for_group = [
self.name_map[col.name]
for col in columns
if self.name_map[col.name] in self._split_adjusted_column_names
]
# Add all splits to the adjustment dict for this sid.
split_adjusted_asof_idx = self.get_split_adjusted_asof_idx(
dates
)
return super(SplitAdjustedEstimatesLoader, self).get_adjustments(
zero_qtr_data,
requested_qtr_data,
last_per_qtr,
dates,
assets,
columns,
split_adjusted_cols_for_group=split_adjusted_cols_for_group,
split_adjusted_asof_idx=split_adjusted_asof_idx
)
def determine_end_idx_for_adjustment(self,
adjustment_ts,
dates,
upper_bound,
requested_quarter,
sid_estimates):
"""
Determines the date until which the adjustment at the given date
index should be applied for the given quarter.
Parameters
----------
adjustment_ts : pd.Timestamp
The timestamp at which the adjustment occurs.
dates : pd.DatetimeIndex
The calendar dates over which the Pipeline is being computed.
upper_bound : int
The index of the upper bound in the calendar dates. This is the
index until which the adjusment will be applied unless there is
information for the requested quarter that comes in on or before
that date.
requested_quarter : float
The quarter for which we are determining how the adjustment
should be applied.
sid_estimates : pd.DataFrame
The DataFrame of estimates data for the sid for which we're
applying the given adjustment.
Returns
-------
end_idx : int
The last index to which the adjustment should be applied for the
given quarter/sid.
"""
end_idx = upper_bound
# Find the next newest kd that happens on or after
# the date of this adjustment
newest_kd_for_qtr = sid_estimates[
(sid_estimates[NORMALIZED_QUARTERS] == requested_quarter) &
(sid_estimates[TS_FIELD_NAME] >= adjustment_ts)
][TS_FIELD_NAME].min()
if pd.notnull(newest_kd_for_qtr):
newest_kd_idx = dates.searchsorted(
newest_kd_for_qtr
)
# We have fresh information that comes in
# before the end of the overwrite and
# presumably is already split-adjusted to the
# current split. We should stop applying the
# adjustment the day before this new
# information comes in.
if newest_kd_idx <= upper_bound:
end_idx = newest_kd_idx - 1
return end_idx
def collect_pre_split_asof_date_adjustments(
self,
split_adjusted_asof_date_idx,
sid_idx,
pre_adjustments,
requested_split_adjusted_columns
):
"""
Collect split adjustments that occur before the
split-adjusted-asof-date. All those adjustments must first be
UN-applied at the first date index and then re-applied on the
appropriate dates in order to match point in time share pricing data.
Parameters
----------
split_adjusted_asof_date_idx : int
The index in the calendar dates as-of which all data was
split-adjusted.
sid_idx : int
The index of the sid for which adjustments should be collected in
the adjusted array.
pre_adjustments : tuple(list(float), list(int))
The adjustment values, indexes in `dates`, and timestamps for
adjustments that happened after the split-asof-date.
requested_split_adjusted_columns : list of str
The requested split adjusted columns.
Returns
-------
col_to_split_adjustments : dict[str -> dict[int -> list of Adjustment]]
The adjustments for this sid that occurred on or before the
split-asof-date.
"""
col_to_split_adjustments = {}
if len(pre_adjustments[0]):
adjustment_values, date_indexes = pre_adjustments
for column_name in requested_split_adjusted_columns:
col_to_split_adjustments[column_name] = {}
# We need to undo all adjustments that happen before the
# split_asof_date here by reversing the split ratio.
col_to_split_adjustments[column_name][0] = [Float64Multiply(
0,
split_adjusted_asof_date_idx,
sid_idx,
sid_idx,
1 / future_adjustment
) for future_adjustment in adjustment_values]
for adjustment, date_index in zip(adjustment_values,
date_indexes):
adj = Float64Multiply(
0,
split_adjusted_asof_date_idx,
sid_idx,
sid_idx,
adjustment
)
add_new_adjustments(col_to_split_adjustments,
[adj],
column_name,
date_index)
return col_to_split_adjustments
def collect_post_asof_split_adjustments(self,
post_adjustments,
requested_qtr_data,
sid,
sid_idx,
sid_estimates,
requested_split_adjusted_columns):
"""
Collect split adjustments that occur after the
split-adjusted-asof-date. Each adjustment needs to be applied to all
dates on which knowledge for the requested quarter was older than the
date of the adjustment.
Parameters
----------
post_adjustments : tuple(list(float), list(int), pd.DatetimeIndex)
The adjustment values, indexes in `dates`, and timestamps for
adjustments that happened after the split-asof-date.
requested_qtr_data : pd.DataFrame
The requested quarter data for each calendar date per sid.
sid : int
The sid for which adjustments need to be collected.
sid_idx : int
The index of `sid` in the adjusted array.
sid_estimates : pd.DataFrame
The raw estimates data for this sid.
requested_split_adjusted_columns : list of str
The requested split adjusted columns.
Returns
-------
col_to_split_adjustments : dict[str -> dict[int -> list of Adjustment]]
The adjustments for this sid that occurred after the
split-asof-date.
"""
col_to_split_adjustments = {}
if post_adjustments:
# Get an integer index
requested_qtr_timeline = requested_qtr_data[
SHIFTED_NORMALIZED_QTRS
][sid].reset_index()
requested_qtr_timeline = requested_qtr_timeline[
requested_qtr_timeline[sid].notnull()
]
# Split the data into range by quarter and determine which quarter
# was being requested in each range.
# Split integer indexes up by quarter range
qtr_ranges_idxs = np.split(
requested_qtr_timeline.index,
np.where(np.diff(requested_qtr_timeline[sid]) != 0)[0] + 1
)
requested_quarters_per_range = [requested_qtr_timeline[sid][r[0]]
for r in qtr_ranges_idxs]
# Try to apply each adjustment to each quarter range.
for i, qtr_range in enumerate(qtr_ranges_idxs):
for adjustment, date_index, timestamp in zip(
*post_adjustments
):
# In the default case, apply through the end of the quarter
upper_bound = qtr_range[-1]
# Find the smallest KD in estimates that is on or after the
# date of the given adjustment. Apply the given adjustment
# until that KD.
end_idx = self.determine_end_idx_for_adjustment(
timestamp,
requested_qtr_data.index,
upper_bound,
requested_quarters_per_range[i],
sid_estimates
)
# In the default case, apply adjustment on the first day of
# the quarter.
start_idx = qtr_range[0]
# If the adjustment happens during this quarter, apply the
# adjustment on the day it happens.
if date_index > start_idx:
start_idx = date_index
# We only want to apply the adjustment if we have any stale
# data to apply it to.
if qtr_range[0] <= end_idx:
for column_name in requested_split_adjusted_columns:
if column_name not in col_to_split_adjustments:
col_to_split_adjustments[column_name] = {}
adj = Float64Multiply(
# Always apply from first day of qtr
qtr_range[0],
end_idx,
sid_idx,
sid_idx,
adjustment
)
add_new_adjustments(
col_to_split_adjustments,
[adj],
column_name,
start_idx
)
return col_to_split_adjustments
def retrieve_split_adjustment_data_for_sid(self,
dates,
sid,
split_adjusted_asof_idx):
"""
dates : pd.DatetimeIndex
The calendar dates.
sid : int
The sid for which we want to retrieve adjustments.
split_adjusted_asof_idx : int
The index in `dates` as-of which the data is split adjusted.
Returns
-------
pre_adjustments : tuple(list(float), list(int), pd.DatetimeIndex)
The adjustment values and indexes in `dates` for
adjustments that happened before the split-asof-date.
post_adjustments : tuple(list(float), list(int), pd.DatetimeIndex)
The adjustment values, indexes in `dates`, and timestamps for
adjustments that happened after the split-asof-date.
"""
adjustments = self._split_adjustments.get_adjustments_for_sid(
'splits', sid
)
sorted(adjustments, key=lambda adj: adj[0])
# Get rid of any adjustments that happen outside of our date index.
adjustments = list(filter(lambda x: dates[0] <= x[0] <= dates[-1],
adjustments))
adjustment_values = np.array([adj[1] for adj in adjustments])
timestamps = pd.DatetimeIndex([adj[0] for adj in adjustments])
# We need the first date on which we would have known about each
# adjustment.
date_indexes = dates.searchsorted(timestamps)
pre_adjustment_idxs = np.where(
date_indexes <= split_adjusted_asof_idx
)[0]
last_adjustment_split_asof_idx = -1
if len(pre_adjustment_idxs):
last_adjustment_split_asof_idx = pre_adjustment_idxs.max()
pre_adjustments = (
adjustment_values[:last_adjustment_split_asof_idx + 1],
date_indexes[:last_adjustment_split_asof_idx + 1]
)
post_adjustments = (
adjustment_values[last_adjustment_split_asof_idx + 1:],
date_indexes[last_adjustment_split_asof_idx + 1:],
timestamps[last_adjustment_split_asof_idx + 1:]
)
return pre_adjustments, post_adjustments
def _collect_adjustments(self,
requested_qtr_data,
sid,
sid_idx,
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
requested_split_adjusted_columns):
pre_adjustments_dict = self.collect_pre_split_asof_date_adjustments(
split_adjusted_asof_idx,
sid_idx,
pre_adjustments,
requested_split_adjusted_columns
)
post_adjustments_dict = self.collect_post_asof_split_adjustments(
post_adjustments,
requested_qtr_data,
sid,
sid_idx,
sid_estimates,
requested_split_adjusted_columns
)
return pre_adjustments_dict, post_adjustments_dict
def merge_split_adjustments_with_overwrites(
self,
pre,
post,
overwrites,
requested_split_adjusted_columns
):
"""
Merge split adjustments with the dict containing overwrites.
Parameters
----------
pre : dict[str -> dict[int -> list]]
The adjustments that occur before the split-adjusted-asof-date.
post : dict[str -> dict[int -> list]]
The adjustments that occur after the split-adjusted-asof-date.
overwrites : dict[str -> dict[int -> list]]
The overwrites across all time. Adjustments will be merged into
this dictionary.
requested_split_adjusted_columns : list of str
List of names of split adjusted columns that are being requested.
"""
for column_name in requested_split_adjusted_columns:
# We can do a merge here because the timestamps in 'pre' and
# 'post' are guaranteed to not overlap.
if pre:
# Either empty or contains all columns.
for ts in pre[column_name]:
add_new_adjustments(
overwrites,
pre[column_name][ts],
column_name,
ts
)
if post:
# Either empty or contains all columns.
for ts in post[column_name]:
add_new_adjustments(
overwrites,
post[column_name][ts],
column_name,
ts
)
class PreviousSplitAdjustedEarningsEstimatesLoader(
SplitAdjustedEstimatesLoader, PreviousEarningsEstimatesLoader
):
def collect_split_adjustments(self,
adjustments_for_sid,
requested_qtr_data,
dates,
sid,
sid_idx,
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
requested_split_adjusted_columns):
"""
Collect split adjustments for previous quarters and apply them to the
given dictionary of splits for the given sid. Since overwrites just
replace all estimates before the new quarter with NaN, we don't need to
worry about re-applying split adjustments.
Parameters
----------
adjustments_for_sid : dict[str -> dict[int -> list]]
The dictionary of adjustments to which splits need to be added.
Initially it contains only overwrites.
requested_qtr_data : pd.DataFrame
The requested quarter data for each calendar date per sid.
dates : pd.DatetimeIndex
The calendar dates for which estimates data is requested.
sid : int
The sid for which adjustments need to be collected.
sid_idx : int
The index of `sid` in the adjusted array.
sid_estimates : pd.DataFrame
The raw estimates data for the given sid.
split_adjusted_asof_idx : int
The index in `dates` as-of which the data is split adjusted.
pre_adjustments : tuple(list(float), list(int), pd.DatetimeIndex)
The adjustment values and indexes in `dates` for
adjustments that happened before the split-asof-date.
post_adjustments : tuple(list(float), list(int), pd.DatetimeIndex)
The adjustment values, indexes in `dates`, and timestamps for
adjustments that happened after the split-asof-date.
requested_split_adjusted_columns : list of str
List of requested split adjusted column names.
"""
(pre_adjustments_dict,
post_adjustments_dict) = self._collect_adjustments(
requested_qtr_data,
sid,
sid_idx,
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
requested_split_adjusted_columns
)
self.merge_split_adjustments_with_overwrites(
pre_adjustments_dict,
post_adjustments_dict,
adjustments_for_sid,
requested_split_adjusted_columns
)
class NextSplitAdjustedEarningsEstimatesLoader(
SplitAdjustedEstimatesLoader, NextEarningsEstimatesLoader
):
def collect_split_adjustments(self,
adjustments_for_sid,
requested_qtr_data,
dates,
sid,
sid_idx,
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
requested_split_adjusted_columns):
"""
Collect split adjustments for future quarters. Re-apply adjustments
that would be overwritten by overwrites. Merge split adjustments with
overwrites into the given dictionary of splits for the given sid.
Parameters
----------
adjustments_for_sid : dict[str -> dict[int -> list]]
The dictionary of adjustments to which splits need to be added.
Initially it contains only overwrites.
requested_qtr_data : pd.DataFrame
The requested quarter data for each calendar date per sid.
dates : pd.DatetimeIndex
The calendar dates for which estimates data is requested.
sid : int
The sid for which adjustments need to be collected.
sid_idx : int
The index of `sid` in the adjusted array.
sid_estimates : pd.DataFrame
The raw estimates data for the given sid.
split_adjusted_asof_idx : int
The index in `dates` as-of which the data is split adjusted.
pre_adjustments : tuple(list(float), list(int), pd.DatetimeIndex)
The adjustment values and indexes in `dates` for
adjustments that happened before the split-asof-date.
post_adjustments : tuple(list(float), list(int), pd.DatetimeIndex)
The adjustment values, indexes in `dates`, and timestamps for
adjustments that happened after the split-asof-date.
requested_split_adjusted_columns : list of str
List of requested split adjusted column names.
"""
(pre_adjustments_dict,
post_adjustments_dict) = self._collect_adjustments(
requested_qtr_data,
sid,
sid_idx,
sid_estimates,
split_adjusted_asof_idx,
pre_adjustments,
post_adjustments,
requested_split_adjusted_columns,
)
for column_name in requested_split_adjusted_columns:
for overwrite_ts in adjustments_for_sid[column_name]:
# We need to cumulatively re-apply all adjustments up to the
# split-adjusted-asof-date. We might not have any
# pre-adjustments, so we should check for that.
if overwrite_ts <= split_adjusted_asof_idx \
and pre_adjustments_dict:
for split_ts in pre_adjustments_dict[column_name]:
# The split has to have occurred during the span of
# the overwrite.
if split_ts < overwrite_ts:
# Create new adjustments here so that we can
# re-apply all applicable adjustments to ONLY
# the dates being overwritten.
adjustments_for_sid[
column_name
][overwrite_ts].extend([
Float64Multiply(
0,
overwrite_ts - 1,
sid_idx,
sid_idx,
adjustment.value
)
for adjustment
in pre_adjustments_dict[
column_name
][split_ts]
])
# After the split-adjusted-asof-date, we need to re-apply all
# adjustments that occur after that date and within the
# bounds of the overwrite. They need to be applied starting
# from the first date and until an end date. The end date is
# the date of the newest information we get about
# `requested_quarter` that is >= `split_ts`, or if there is no
# new knowledge before `overwrite_ts`, then it is the date
# before `overwrite_ts`.
else:
# Overwrites happen at the first index of a new quarter,
# so determine here which quarter that is.
requested_quarter = requested_qtr_data[
SHIFTED_NORMALIZED_QTRS, sid
].iloc[overwrite_ts]
for adjustment_value, date_index, timestamp in zip(
*post_adjustments
):
if split_adjusted_asof_idx < date_index < overwrite_ts:
# Assume the entire overwrite contains stale data
upper_bound = overwrite_ts - 1
end_idx = self.determine_end_idx_for_adjustment(
timestamp,
dates,
upper_bound,
requested_quarter,
sid_estimates
)
adjustments_for_sid[
column_name
][overwrite_ts].append(
Float64Multiply(
0,
end_idx,
sid_idx,
sid_idx,
adjustment_value
)
)
self.merge_split_adjustments_with_overwrites(
pre_adjustments_dict,
post_adjustments_dict,
adjustments_for_sid,
requested_split_adjusted_columns
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/earnings_estimates.py | earnings_estimates.py |
import numpy as np
import pandas as pd
from interface import implements
from six import viewvalues
from toolz import groupby, merge
from .base import PipelineLoader
from zipline.pipeline.common import (
EVENT_DATE_FIELD_NAME,
SID_FIELD_NAME,
TS_FIELD_NAME,
)
from zipline.pipeline.loaders.frame import DataFrameLoader
from zipline.pipeline.loaders.utils import (
next_event_indexer,
previous_event_indexer,
)
def required_event_fields(next_value_columns, previous_value_columns):
"""
Compute the set of resource columns required to serve
``next_value_columns`` and ``previous_value_columns``.
"""
# These metadata columns are used to align event indexers.
return {
TS_FIELD_NAME,
SID_FIELD_NAME,
EVENT_DATE_FIELD_NAME,
}.union(
# We also expect any of the field names that our loadable columns
# are mapped to.
viewvalues(next_value_columns),
viewvalues(previous_value_columns),
)
def validate_column_specs(events, next_value_columns, previous_value_columns):
"""
Verify that the columns of ``events`` can be used by an EventsLoader to
serve the BoundColumns described by ``next_value_columns`` and
``previous_value_columns``.
"""
required = required_event_fields(next_value_columns,
previous_value_columns)
received = set(events.columns)
missing = required - received
if missing:
raise ValueError(
"EventsLoader missing required columns {missing}.\n"
"Got Columns: {received}\n"
"Expected Columns: {required}".format(
missing=sorted(missing),
received=sorted(received),
required=sorted(required),
)
)
class EventsLoader(implements(PipelineLoader)):
"""
Base class for PipelineLoaders that supports loading the next and previous
value of an event field.
Does not currently support adjustments.
Parameters
----------
events : pd.DataFrame
A DataFrame representing events (e.g. share buybacks or
earnings announcements) associated with particular companies.
``events`` must contain at least three columns::
sid : int64
The asset id associated with each event.
event_date : datetime64[ns]
The date on which the event occurred.
timestamp : datetime64[ns]
The date on which we learned about the event.
next_value_columns : dict[BoundColumn -> str]
Map from dataset columns to raw field names that should be used when
searching for a next event value.
previous_value_columns : dict[BoundColumn -> str]
Map from dataset columns to raw field names that should be used when
searching for a previous event value.
"""
def __init__(self,
events,
next_value_columns,
previous_value_columns):
validate_column_specs(
events,
next_value_columns,
previous_value_columns,
)
events = events[events[EVENT_DATE_FIELD_NAME].notnull()]
# We always work with entries from ``events`` directly as numpy arrays,
# so we coerce from a frame to a dict of arrays here.
self.events = {
name: np.asarray(series)
for name, series in (
events.sort_values(EVENT_DATE_FIELD_NAME).iteritems()
)
}
# Columns to load with self.load_next_events.
self.next_value_columns = next_value_columns
# Columns to load with self.load_previous_events.
self.previous_value_columns = previous_value_columns
def split_next_and_previous_event_columns(self, requested_columns):
"""
Split requested columns into columns that should load the next known
value and columns that should load the previous known value.
Parameters
----------
requested_columns : iterable[BoundColumn]
Returns
-------
next_cols, previous_cols : iterable[BoundColumn], iterable[BoundColumn]
``requested_columns``, partitioned into sub-sequences based on
whether the column should produce values from the next event or the
previous event
"""
def next_or_previous(c):
if c in self.next_value_columns:
return 'next'
elif c in self.previous_value_columns:
return 'previous'
raise ValueError(
"{c} not found in next_value_columns "
"or previous_value_columns".format(c=c)
)
groups = groupby(next_or_previous, requested_columns)
return groups.get('next', ()), groups.get('previous', ())
def next_event_indexer(self, dates, data_query_cutoff, sids):
return next_event_indexer(
dates,
data_query_cutoff,
sids,
self.events[EVENT_DATE_FIELD_NAME],
self.events[TS_FIELD_NAME],
self.events[SID_FIELD_NAME],
)
def previous_event_indexer(self, data_query_time, sids):
return previous_event_indexer(
data_query_time,
sids,
self.events[EVENT_DATE_FIELD_NAME],
self.events[TS_FIELD_NAME],
self.events[SID_FIELD_NAME],
)
def load_next_events(self,
domain,
columns,
dates,
data_query_time,
sids,
mask):
if not columns:
return {}
return self._load_events(
name_map=self.next_value_columns,
indexer=self.next_event_indexer(dates, data_query_time, sids),
domain=domain,
columns=columns,
dates=dates,
sids=sids,
mask=mask,
)
def load_previous_events(self,
domain,
columns,
dates,
data_query_time,
sids,
mask):
if not columns:
return {}
return self._load_events(
name_map=self.previous_value_columns,
indexer=self.previous_event_indexer(data_query_time, sids),
domain=domain,
columns=columns,
dates=dates,
sids=sids,
mask=mask,
)
def _load_events(self,
name_map,
indexer,
domain,
columns,
dates,
sids,
mask):
def to_frame(array):
return pd.DataFrame(array, index=dates, columns=sids)
assert indexer.shape == (len(dates), len(sids))
out = {}
for c in columns:
# Array holding the value for column `c` for every event we have.
col_array = self.events[name_map[c]]
if not len(col_array):
# We don't have **any** events, so return col.missing_value
# every day for every sid. We have to special case empty events
# because in normal branch we depend on being able to index
# with -1 for missing values, which fails if there are no
# events at all.
raw = np.full(
(len(dates), len(sids)),
c.missing_value,
dtype=c.dtype,
)
else:
# Slot event values into sid/date locations using `indexer`.
# This produces a 2D array of the same shape as `indexer`,
# which must be (len(dates), len(sids))`.
raw = col_array[indexer]
# indexer will be -1 for locations where we don't have a known
# value. Overwrite those locations with c.missing_value.
raw[indexer < 0] = c.missing_value
# Delegate the actual array formatting logic to a DataFrameLoader.
loader = DataFrameLoader(c, to_frame(raw), adjustments=None)
out[c] = loader.load_adjusted_array(
domain, [c], dates, sids, mask,
)[c]
return out
def load_adjusted_array(self, domain, columns, dates, sids, mask):
data_query = domain.data_query_cutoff_for_sessions(dates)
n, p = self.split_next_and_previous_event_columns(columns)
return merge(
self.load_next_events(domain, n, dates, data_query, sids, mask),
self.load_previous_events(domain, p, dates, data_query, sids, mask)
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/events.py | events.py |
from __future__ import division, absolute_import
from abc import ABCMeta, abstractproperty
from functools import partial
from itertools import count
import warnings
from weakref import WeakKeyDictionary
import blaze as bz
from datashape import (
Date,
DateTime,
Option,
String,
isrecord,
isscalar,
integral,
)
from interface import implements
import numpy as np
from odo import odo
import pandas as pd
from six import with_metaclass, PY2, itervalues, iteritems
from toolz import (
complement,
compose,
first,
flip,
groupby,
memoize,
merge,
)
import toolz.curried.operator as op
from toolz.curried.operator import getitem
from zipline.pipeline.common import (
AD_FIELD_NAME,
SID_FIELD_NAME,
TS_FIELD_NAME
)
from zipline.pipeline.data.dataset import DataSet, Column
from zipline.pipeline.domain import GENERIC
from zipline.pipeline.loaders.base import PipelineLoader
from zipline.pipeline.sentinels import NotSpecified
from zipline.lib.adjusted_array import can_represent_dtype
from zipline.utils.input_validation import expect_element
from zipline.utils.pandas_utils import ignore_pandas_nan_categorical_warning
from zipline.utils.pool import SequentialPool
try:
from ._core import ( # noqa
adjusted_arrays_from_rows_with_assets,
adjusted_arrays_from_rows_without_assets,
baseline_arrays_from_rows_with_assets, # reexport
baseline_arrays_from_rows_without_assets, # reexport
getname,
)
except ImportError:
def getname(column):
return column.get('blaze_column_name', column.name)
def barf(*args, **kwargs):
raise RuntimeError(
"zipline.pipeline.loaders.blaze._core failed to import"
)
adjusted_arrays_from_rows_with_assets = barf
adjusted_arrays_from_rows_without_assets = barf
baseline_arrays_from_rows_with_assets = barf
baseline_arrays_from_rows_without_assets = barf
valid_deltas_node_types = (
bz.expr.Field,
bz.expr.ReLabel,
bz.expr.Symbol,
)
traversable_nodes = (
bz.expr.Field,
bz.expr.Label,
)
is_invalid_deltas_node = complement(flip(isinstance, valid_deltas_node_types))
get__name__ = op.attrgetter('__name__')
class InvalidField(with_metaclass(ABCMeta)):
"""A field that raises an exception indicating that the
field was invalid.
Parameters
----------
field : str
The name of the field.
type_ : dshape
The shape of the field.
"""
@abstractproperty
def error_format(self): # pragma: no cover
raise NotImplementedError('error_format')
def __init__(self, field, type_):
self._field = field
self._type = type_
def __get__(self, instance, owner):
raise AttributeError(
self.error_format.format(field=self._field, type_=self._type),
)
class NonNumpyField(InvalidField):
error_format = (
"field '{field}' was a non numpy compatible type: '{type_}'"
)
class NonPipelineField(InvalidField):
error_format = (
"field '{field}' was a non Pipeline API compatible type: '{type_}'"
)
_new_names = ('BlazeDataSet_%d' % n for n in count())
def datashape_type_to_numpy(type_):
"""
Given a datashape type, return the associated numpy type. Maps
datashape's DateTime type to numpy's `datetime64[ns]` dtype, since the
numpy datetime returned by datashape isn't supported by pipeline.
Parameters
----------
type_: datashape.coretypes.Type
The datashape type.
Returns
-------
type_ np.dtype
The numpy dtype.
"""
if isinstance(type_, Option):
type_ = type_.ty
if isinstance(type_, DateTime):
return np.dtype('datetime64[ns]')
if isinstance(type_, String):
return np.dtype(object)
if type_ in integral:
return np.dtype('int64')
else:
return type_.to_numpy_dtype()
@memoize
def new_dataset(expr, missing_values, domain):
"""
Creates or returns a dataset from a blaze expression.
Parameters
----------
expr : Expr
The blaze expression representing the values.
missing_values : frozenset((name, value) pairs
Association pairs column name and missing_value for that column.
This needs to be a frozenset rather than a dict or tuple of tuples
because we want a collection that's unordered but still hashable.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
Returns
-------
ds : type
A new dataset type.
Notes
-----
This function is memoized. repeated calls with the same inputs will return
the same type.
"""
missing_values = dict(missing_values)
class_dict = {'ndim': 2 if SID_FIELD_NAME in expr.fields else 1}
for name, type_ in expr.dshape.measure.fields:
# Don't generate a column for sid or timestamp, since they're
# implicitly the labels if the arrays that will be passed to pipeline
# Terms.
if name in (SID_FIELD_NAME, TS_FIELD_NAME):
continue
type_ = datashape_type_to_numpy(type_)
if can_represent_dtype(type_):
col = Column(
type_,
missing_values.get(name, NotSpecified),
)
else:
col = NonPipelineField(name, type_)
class_dict[name] = col
if 'domain' in class_dict:
raise ValueError("Got a column named 'domain' in new_dataset(). "
"'domain' is reserved.")
class_dict['domain'] = domain
name = expr._name
if name is None:
name = next(_new_names)
# unicode is a name error in py3 but the branch is only hit
# when we are in python 2.
if PY2 and isinstance(name, unicode): # pragma: no cover # noqa
name = name.encode('utf-8')
return type(name, (DataSet,), class_dict)
def _check_resources(name, expr, resources):
"""Validate that the expression and resources passed match up.
Parameters
----------
name : str
The name of the argument we are checking.
expr : Expr
The potentially bound expr.
resources
The explicitly passed resources to compute expr.
Raises
------
ValueError
If the resources do not match for an expression.
"""
if expr is None:
return
bound = expr._resources()
if not bound and resources is None:
raise ValueError('no resources provided to compute %s' % name)
if bound and resources:
raise ValueError(
'explicit and implicit resources provided to compute %s' % name,
)
def _check_datetime_field(name, measure):
"""Check that a field is a datetime inside some measure.
Parameters
----------
name : str
The name of the field to check.
measure : Record
The record to check the field of.
Raises
------
TypeError
If the field is not a datetime inside ``measure``.
"""
if not isinstance(measure[name], (Date, DateTime)):
raise TypeError(
"'{name}' field must be a '{dt}', not: '{dshape}'".format(
name=name,
dt=DateTime(),
dshape=measure[name],
),
)
class NoMetaDataWarning(UserWarning):
"""Warning used to signal that no deltas or checkpoints could be found and
none were provided.
Parameters
----------
expr : Expr
The expression that was searched.
field : {'deltas', 'checkpoints'}
The field that was looked up.
"""
def __init__(self, expr, field):
self._expr = expr
self._field = field
def __str__(self):
return 'No %s could be inferred from expr: %s' % (
self._field,
self._expr,
)
no_metadata_rules = frozenset({'warn', 'raise', 'ignore'})
def _get_metadata(field, expr, metadata_expr, no_metadata_rule):
"""Find the correct metadata expression for the expression.
Parameters
----------
field : {'deltas', 'checkpoints'}
The kind of metadata expr to lookup.
expr : Expr
The baseline expression.
metadata_expr : Expr, 'auto', or None
The metadata argument. If this is 'auto', then the metadata table will
be searched for by walking up the expression tree. If this cannot be
reflected, then an action will be taken based on the
``no_metadata_rule``.
no_metadata_rule : {'warn', 'raise', 'ignore'}
How to handle the case where the metadata_expr='auto' but no expr
could be found.
Returns
-------
metadata : Expr or None
The deltas or metadata table to use.
"""
if isinstance(metadata_expr, bz.Expr) or metadata_expr is None:
return metadata_expr
try:
# The error produced by expr[field_name] when field_name doesn't exist
# is very expensive. Avoid that cost by doing the check ourselves.
field_name = '_'.join(((expr._name or ''), field))
child = expr._child
if field_name not in child.fields:
raise AttributeError(field_name)
return child[field_name]
except (ValueError, AttributeError):
if no_metadata_rule == 'raise':
raise ValueError(
"no %s table could be reflected for %s" % (field, expr)
)
elif no_metadata_rule == 'warn':
warnings.warn(NoMetaDataWarning(expr, field), stacklevel=4)
return None
def _ad_as_ts(expr):
"""Duplicate the asof_date column as the timestamp column.
Parameters
----------
expr : Expr or None
The expression to change the columns of.
Returns
-------
transformed : Expr or None
The transformed expression or None if ``expr`` is None.
"""
return (
None
if expr is None else
bz.transform(expr, **{TS_FIELD_NAME: expr[AD_FIELD_NAME]})
)
def _ensure_timestamp_field(dataset_expr, deltas, checkpoints):
"""Verify that the baseline and deltas expressions have a timestamp field.
If there is not a ``TS_FIELD_NAME`` on either of the expressions, it will
be copied from the ``AD_FIELD_NAME``. If one is provided, then we will
verify that it is the correct dshape.
Parameters
----------
dataset_expr : Expr
The baseline expression.
deltas : Expr or None
The deltas expression if any was provided.
checkpoints : Expr or None
The checkpoints expression if any was provided.
Returns
-------
dataset_expr, deltas : Expr
The new baseline and deltas expressions to use.
"""
measure = dataset_expr.dshape.measure
if TS_FIELD_NAME not in measure.names:
dataset_expr = bz.transform(
dataset_expr,
**{TS_FIELD_NAME: dataset_expr[AD_FIELD_NAME]}
)
deltas = _ad_as_ts(deltas)
checkpoints = _ad_as_ts(checkpoints)
else:
_check_datetime_field(TS_FIELD_NAME, measure)
return dataset_expr, deltas, checkpoints
@expect_element(
no_deltas_rule=no_metadata_rules,
no_checkpoints_rule=no_metadata_rules,
)
def from_blaze(expr,
deltas='auto',
checkpoints='auto',
loader=None,
resources=None,
odo_kwargs=None,
missing_values=None,
domain=GENERIC,
no_deltas_rule='warn',
no_checkpoints_rule='warn'):
"""Create a Pipeline API object from a blaze expression.
Parameters
----------
expr : Expr
The blaze expression to use.
deltas : Expr, 'auto' or None, optional
The expression to use for the point in time adjustments.
If the string 'auto' is passed, a deltas expr will be looked up
by stepping up the expression tree and looking for another field
with the name of ``expr._name`` + '_deltas'. If None is passed, no
deltas will be used.
checkpoints : Expr, 'auto' or None, optional
The expression to use for the forward fill checkpoints.
If the string 'auto' is passed, a checkpoints expr will be looked up
by stepping up the expression tree and looking for another field
with the name of ``expr._name`` + '_checkpoints'. If None is passed,
no checkpoints will be used.
loader : BlazeLoader, optional
The blaze loader to attach this pipeline dataset to. If None is passed,
the global blaze loader is used.
resources : dict or any, optional
The data to execute the blaze expressions against. This is used as the
scope for ``bz.compute``.
odo_kwargs : dict, optional
The keyword arguments to pass to odo when evaluating the expressions.
domain : zipline.pipeline.domain.Domain
Domain of the dataset to be created.
missing_values : dict[str -> any], optional
A dict mapping column names to missing values for those columns.
Missing values are required for integral columns.
no_deltas_rule : {'warn', 'raise', 'ignore'}, optional
What should happen if ``deltas='auto'`` but no deltas can be found.
'warn' says to raise a warning but continue.
'raise' says to raise an exception if no deltas can be found.
'ignore' says take no action and proceed with no deltas.
no_checkpoints_rule : {'warn', 'raise', 'ignore'}, optional
What should happen if ``checkpoints='auto'`` but no checkpoints can be
found. 'warn' says to raise a warning but continue.
'raise' says to raise an exception if no deltas can be found.
'ignore' says take no action and proceed with no deltas.
Returns
-------
pipeline_api_obj : DataSet or BoundColumn
Either a new dataset or bound column based on the shape of the expr
passed in. If a table shaped expression is passed, this will return
a ``DataSet`` that represents the whole table. If an array-like shape
is passed, a ``BoundColumn`` on the dataset that would be constructed
from passing the parent is returned.
"""
if 'auto' in {deltas, checkpoints}:
invalid_nodes = tuple(filter(is_invalid_deltas_node, expr._subterms()))
if invalid_nodes:
raise TypeError(
'expression with auto %s may only contain (%s) nodes,'
" found: %s" % (
' or '.join(
['deltas'] if deltas is not None else [] +
['checkpoints'] if checkpoints is not None else [],
),
', '.join(map(get__name__, valid_deltas_node_types)),
', '.join(
set(map(compose(get__name__, type), invalid_nodes)),
),
),
)
deltas = _get_metadata(
'deltas',
expr,
deltas,
no_deltas_rule,
)
checkpoints = _get_metadata(
'checkpoints',
expr,
checkpoints,
no_checkpoints_rule,
)
# Check if this is a single column out of a dataset.
if bz.ndim(expr) != 1:
raise TypeError(
'expression was not tabular or array-like,'
' %s dimensions: %d' % (
'too many' if bz.ndim(expr) > 1 else 'not enough',
bz.ndim(expr),
),
)
single_column = None
if isscalar(expr.dshape.measure):
# This is a single column. Record which column we are to return
# but create the entire dataset.
single_column = rename = expr._name
field_hit = False
if not isinstance(expr, traversable_nodes):
raise TypeError(
"expression '%s' was array-like but not a simple field of"
" some larger table" % str(expr),
)
while isinstance(expr, traversable_nodes):
if isinstance(expr, bz.expr.Field):
if not field_hit:
field_hit = True
else:
break
rename = expr._name
expr = expr._child
dataset_expr = expr.relabel({rename: single_column})
else:
dataset_expr = expr
measure = dataset_expr.dshape.measure
if not isrecord(measure) or AD_FIELD_NAME not in measure.names:
raise TypeError(
"The dataset must be a collection of records with at least an"
" '{ad}' field. Fields provided: '{fields}'\nhint: maybe you need"
" to use `relabel` to change your field names".format(
ad=AD_FIELD_NAME,
fields=measure,
),
)
_check_datetime_field(AD_FIELD_NAME, measure)
dataset_expr, deltas, checkpoints = _ensure_timestamp_field(
dataset_expr,
deltas,
checkpoints,
)
if deltas is not None and (sorted(deltas.dshape.measure.fields) !=
sorted(measure.fields)):
raise TypeError(
'baseline measure != deltas measure:\n%s != %s' % (
measure,
deltas.dshape.measure,
),
)
if (checkpoints is not None and
(sorted(checkpoints.dshape.measure.fields) !=
sorted(measure.fields))):
raise TypeError(
'baseline measure != checkpoints measure:\n%s != %s' % (
measure,
checkpoints.dshape.measure,
),
)
# Ensure that we have a data resource to execute the query against.
_check_resources('expr', dataset_expr, resources)
_check_resources('deltas', deltas, resources)
_check_resources('checkpoints', checkpoints, resources)
# Create or retrieve the Pipeline API dataset.
if missing_values is None:
missing_values = {}
ds = new_dataset(dataset_expr, frozenset(missing_values.items()), domain)
# Register our new dataset with the loader.
(loader if loader is not None else global_loader).register_dataset(
ds,
bind_expression_to_resources(dataset_expr, resources),
bind_expression_to_resources(deltas, resources)
if deltas is not None else
None,
bind_expression_to_resources(checkpoints, resources)
if checkpoints is not None else
None,
odo_kwargs=odo_kwargs,
)
if single_column is not None:
# We were passed a single column, extract and return it.
return getattr(ds, single_column)
return ds
getdataset = op.attrgetter('dataset')
class ExprData(object):
"""A pair of expressions and data resources. The expressions will be
computed using the resources as the starting scope.
Parameters
----------
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
"""
def __init__(self,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
self.expr = expr
self.deltas = deltas
self.checkpoints = checkpoints
self._odo_kwargs = odo_kwargs
def replace(self, **kwargs):
base_kwargs = {
'expr': self.expr,
'deltas': self.deltas,
'checkpoints': self.checkpoints,
'odo_kwargs': self._odo_kwargs,
}
invalid_kwargs = set(kwargs) - set(base_kwargs)
if invalid_kwargs:
raise TypeError('invalid param(s): %s' % sorted(invalid_kwargs))
base_kwargs.update(kwargs)
return type(self)(**base_kwargs)
def __iter__(self):
yield self.expr
yield self.deltas
yield self.checkpoints
yield self.odo_kwargs
@property
def odo_kwargs(self):
out = self._odo_kwargs
if out is None:
out = {}
return out
def __repr__(self):
# If the expressions have _resources() then the repr will
# drive computation so we take the str here.
return (
'ExprData(expr=%s, deltas=%s, checkpoints=%s, odo_kwargs=%r)' % (
self.expr,
self.deltas,
self.checkpoints,
self.odo_kwargs,
)
)
@staticmethod
def _expr_eq(a, b):
return a is b is None or a.isidentical(b)
def __hash__(self):
return hash((
self.expr,
self.deltas,
self.checkpoints,
id(self._odo_kwargs),
))
def __eq__(self, other):
if not isinstance(other, ExprData):
return NotImplemented
return (
self._expr_eq(self.expr, other.expr) and
self._expr_eq(self.deltas, other.deltas) and
self._expr_eq(self.checkpoints, other.checkpoints) and
self._odo_kwargs is other._odo_kwargs
)
class BlazeLoader(implements(PipelineLoader)):
"""A PipelineLoader for datasets constructed with ``from_blaze``.
Parameters
----------
dsmap : mapping, optional
An initial mapping of datasets to ``ExprData`` objects.
NOTE: Further mutations to this map will not be reflected by this
object.
pool : Pool, optional
The pool to use to run blaze queries concurrently. This object must
support ``imap_unordered``, ``apply`` and ``apply_async`` methods.
Attributes
----------
pool : Pool
The pool to use to run blaze queries concurrently. This object must
support ``imap_unordered``, ``apply`` and ``apply_async`` methods.
It is possible to change the pool after the loader has been
constructed. This allows us to set a new pool for the ``global_loader``
like: ``global_loader.pool = multiprocessing.Pool(4)``.
See Also
--------
:class:`zipline.utils.pool.SequentialPool`
:class:`multiprocessing.Pool`
"""
def __init__(self, dsmap=None, pool=SequentialPool()):
# explicitly public
self.pool = pool
self._table_expressions = (dsmap or {}).copy()
@classmethod
@memoize(cache=WeakKeyDictionary())
def global_instance(cls):
return cls()
def __hash__(self):
return id(self)
def __contains__(self, column):
return column in self._table_expressions
def __getitem__(self, column):
return self._table_expressions[column]
def __iter__(self):
return iter(self._table_expressions)
def __len__(self):
return len(self._table_expressions)
def __call__(self, column):
if column in self:
return self
raise KeyError(column)
def register_dataset(self,
dataset,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
"""Explicitly map a datset to a collection of blaze expressions.
Parameters
----------
dataset : DataSet
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
See Also
--------
:func:`zipline.pipeline.loaders.blaze.from_blaze`
"""
expr_data = ExprData(
expr,
deltas,
checkpoints,
odo_kwargs,
)
for column in dataset.columns:
self._table_expressions[column] = expr_data
def register_column(self,
column,
expr,
deltas=None,
checkpoints=None,
odo_kwargs=None):
"""Explicitly map a single bound column to a collection of blaze
expressions. The expressions need to have ``timestamp`` and ``as_of``
columns.
Parameters
----------
column : BoundColumn
The pipeline dataset to map to the given expressions.
expr : Expr
The baseline values.
deltas : Expr, optional
The deltas for the data.
checkpoints : Expr, optional
The forward fill checkpoints for the data.
odo_kwargs : dict, optional
The keyword arguments to forward to the odo calls internally.
See Also
--------
:func:`zipline.pipeline.loaders.blaze.from_blaze`
"""
self._table_expressions[column] = ExprData(
expr,
deltas,
checkpoints,
odo_kwargs,
)
def load_adjusted_array(self, domain, columns, dates, sids, mask):
data_query_cutoff_times = domain.data_query_cutoff_for_sessions(
dates,
)
return merge(
self.pool.imap_unordered(
partial(
self._load_dataset,
dates,
data_query_cutoff_times,
sids,
mask,
),
itervalues(groupby(getitem(self._table_expressions), columns)),
),
)
def _load_dataset(self,
dates,
data_query_cutoff_times,
assets,
mask,
columns):
try:
(expr_data,) = {self._table_expressions[c] for c in columns}
except ValueError:
raise AssertionError(
'all columns must share the same expression data',
)
expr, deltas, checkpoints, odo_kwargs = expr_data
have_sids = (first(columns).dataset.ndim == 2)
added_query_fields = {AD_FIELD_NAME, TS_FIELD_NAME} | (
{SID_FIELD_NAME} if have_sids else set()
)
requested_columns = set(map(getname, columns))
colnames = sorted(added_query_fields | requested_columns)
lower_dt, upper_dt = data_query_cutoff_times[[0, -1]]
def collect_expr(e, lower):
"""Materialize the expression as a dataframe.
Parameters
----------
e : Expr
The baseline or deltas expression.
lower : datetime
The lower time bound to query.
Returns
-------
result : pd.DataFrame
The resulting dataframe.
Notes
-----
This can return more data than needed. The in memory reindex will
handle this.
"""
predicate = e[TS_FIELD_NAME] < upper_dt
if lower is not None:
predicate &= e[TS_FIELD_NAME] >= lower
return odo(e[predicate][colnames], pd.DataFrame, **odo_kwargs)
lower, materialized_checkpoints = get_materialized_checkpoints(
checkpoints, colnames, lower_dt, odo_kwargs
)
materialized_expr_deferred = self.pool.apply_async(
collect_expr,
(expr, lower),
)
materialized_deltas = (
self.pool.apply(collect_expr, (deltas, lower))
if deltas is not None else
None
)
# If the rows that come back from the blaze backend are constructed
# from LabelArrays with Nones in the categories, pandas
# complains. Ignore those warnings for now until we have a story for
# updating our categorical missing values to NaN.
with ignore_pandas_nan_categorical_warning():
all_rows = pd.concat(
filter(
lambda df: df is not None, (
materialized_checkpoints,
materialized_expr_deferred.get(),
materialized_deltas,
),
),
ignore_index=True,
copy=False,
)
all_rows[TS_FIELD_NAME] = all_rows[TS_FIELD_NAME].astype(
'datetime64[ns]',
)
all_rows.sort_values([TS_FIELD_NAME, AD_FIELD_NAME], inplace=True)
if have_sids:
return adjusted_arrays_from_rows_with_assets(
dates,
data_query_cutoff_times,
assets,
columns,
all_rows,
)
else:
return adjusted_arrays_from_rows_without_assets(
dates,
data_query_cutoff_times,
columns,
all_rows,
)
global_loader = BlazeLoader.global_instance()
def bind_expression_to_resources(expr, resources):
"""
Bind a Blaze expression to resources.
Parameters
----------
expr : bz.Expr
The expression to which we want to bind resources.
resources : dict[bz.Symbol -> any]
Mapping from the loadable terms of ``expr`` to actual data resources.
Returns
-------
bound_expr : bz.Expr
``expr`` with bound resources.
"""
# bind the resources into the expression
if resources is None:
resources = {}
# _subs stands for substitute. It's not actually private, blaze just
# prefixes symbol-manipulation methods with underscores to prevent
# collisions with data column names.
return expr._subs({
k: bz.data(v, dshape=k.dshape) for k, v in iteritems(resources)
})
def get_materialized_checkpoints(checkpoints, colnames, lower_dt, odo_kwargs):
"""
Computes a lower bound and a DataFrame checkpoints.
Parameters
----------
checkpoints : Expr
Bound blaze expression for a checkpoints table from which to get a
computed lower bound.
colnames : iterable of str
The names of the columns for which checkpoints should be computed.
lower_dt : pd.Timestamp
The lower date being queried for that serves as an upper bound for
checkpoints.
odo_kwargs : dict, optional
The extra keyword arguments to pass to ``odo``.
"""
if checkpoints is not None:
ts = checkpoints[TS_FIELD_NAME]
checkpoints_ts = odo(
ts[ts < lower_dt].max(),
pd.Timestamp,
**odo_kwargs
)
if pd.isnull(checkpoints_ts):
# We don't have a checkpoint for before our start date so just
# don't constrain the lower date.
materialized_checkpoints = pd.DataFrame(columns=colnames)
lower = None
else:
materialized_checkpoints = odo(
checkpoints[ts == checkpoints_ts][colnames],
pd.DataFrame,
**odo_kwargs
)
lower = checkpoints_ts
else:
materialized_checkpoints = pd.DataFrame(columns=colnames)
lower = None # we don't have a good lower date constraint
return lower, materialized_checkpoints
def ffill_query_in_range(expr,
lower,
upper,
checkpoints=None,
odo_kwargs=None,
ts_field=TS_FIELD_NAME):
"""Query a blaze expression in a given time range properly forward filling
from values that fall before the lower date.
Parameters
----------
expr : Expr
Bound blaze expression.
lower : datetime
The lower date to query for.
upper : datetime
The upper date to query for.
checkpoints : Expr, optional
Bound blaze expression for a checkpoints table from which to get a
computed lower bound.
odo_kwargs : dict, optional
The extra keyword arguments to pass to ``odo``.
ts_field : str, optional
The name of the timestamp field in the given blaze expression.
Returns
-------
raw : pd.DataFrame
A strict dataframe for the data in the given date range. This may
start before the requested start date if a value is needed to ffill.
"""
odo_kwargs = odo_kwargs or {}
computed_lower, materialized_checkpoints = get_materialized_checkpoints(
checkpoints,
expr.fields,
lower,
odo_kwargs,
)
pred = expr[ts_field] <= upper
if computed_lower is not None:
# only constrain the lower date if we computed a new lower date
pred &= expr[ts_field] >= computed_lower
raw = pd.concat(
(
materialized_checkpoints,
odo(
expr[pred],
pd.DataFrame,
**odo_kwargs
),
),
ignore_index=True,
)
raw.loc[:, ts_field] = raw.loc[:, ts_field].astype('datetime64[ns]')
return raw | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/blaze/core.py | core.py |
from interface import implements
from datashape import istabular
from .core import (
bind_expression_to_resources,
)
from zipline.pipeline.common import (
EVENT_DATE_FIELD_NAME,
FISCAL_QUARTER_FIELD_NAME,
FISCAL_YEAR_FIELD_NAME,
SID_FIELD_NAME,
TS_FIELD_NAME,
)
from zipline.pipeline.loaders.base import PipelineLoader
from zipline.pipeline.loaders.blaze.utils import load_raw_data
from zipline.pipeline.loaders.earnings_estimates import (
NextEarningsEstimatesLoader,
PreviousEarningsEstimatesLoader,
required_estimates_fields,
metadata_columns,
PreviousSplitAdjustedEarningsEstimatesLoader,
NextSplitAdjustedEarningsEstimatesLoader,
)
class BlazeEstimatesLoader(implements(PipelineLoader)):
"""An abstract pipeline loader for the estimates datasets that loads
data from a blaze expression.
Parameters
----------
expr : Expr
The expression representing the data to load.
columns : dict[str -> str]
A dict mapping BoundColumn names to the associated names in `expr`.
resources : dict, optional
Mapping from the loadable terms of ``expr`` to actual data resources.
odo_kwargs : dict, optional
Extra keyword arguments to pass to odo when executing the expression.
checkpoints : Expr, optional
The expression representing checkpointed data to be used for faster
forward-filling of data from `expr`.
Notes
-----
The expression should have a tabular dshape of::
Dim * {{
{SID_FIELD_NAME}: int64,
{TS_FIELD_NAME}: datetime,
{FISCAL_YEAR_FIELD_NAME}: float64,
{FISCAL_QUARTER_FIELD_NAME}: float64,
{EVENT_DATE_FIELD_NAME}: datetime,
}}
And other dataset-specific fields, where each row of the table is a
record including the sid to identify the company, the timestamp where we
learned about the announcement, and the date of the event.
If the '{TS_FIELD_NAME}' field is not included it is assumed that we
start the backtest with knowledge of all announcements.
"""
__doc__ = __doc__.format(
SID_FIELD_NAME=SID_FIELD_NAME,
TS_FIELD_NAME=TS_FIELD_NAME,
FISCAL_YEAR_FIELD_NAME=FISCAL_YEAR_FIELD_NAME,
FISCAL_QUARTER_FIELD_NAME=FISCAL_QUARTER_FIELD_NAME,
EVENT_DATE_FIELD_NAME=EVENT_DATE_FIELD_NAME,
)
def __init__(self,
expr,
columns,
resources=None,
odo_kwargs=None,
checkpoints=None):
dshape = expr.dshape
if not istabular(dshape):
raise ValueError(
'expression dshape must be tabular, got: %s' % dshape,
)
required_cols = list(
required_estimates_fields(columns)
)
self._expr = bind_expression_to_resources(
expr[required_cols],
resources,
)
self._columns = columns
self._odo_kwargs = odo_kwargs if odo_kwargs is not None else {}
self._checkpoints = checkpoints
def load_adjusted_array(self, domain, columns, dates, sids, mask):
# Only load requested columns.
requested_column_names = [self._columns[column.name]
for column in columns]
raw = load_raw_data(
sids,
dates,
self._expr[sorted(metadata_columns.union(requested_column_names))],
self._odo_kwargs,
checkpoints=self._checkpoints,
)
return self.loader(
raw,
{column.name: self._columns[column.name] for column in columns},
).load_adjusted_array(
domain,
columns,
dates,
sids,
mask,
)
class BlazeNextEstimatesLoader(BlazeEstimatesLoader):
loader = NextEarningsEstimatesLoader
class BlazePreviousEstimatesLoader(BlazeEstimatesLoader):
loader = PreviousEarningsEstimatesLoader
class BlazeSplitAdjustedEstimatesLoader(BlazeEstimatesLoader):
def __init__(self,
expr,
columns,
split_adjustments_loader,
split_adjusted_column_names,
split_adjusted_asof,
**kwargs):
self._split_adjustments = split_adjustments_loader
self._split_adjusted_column_names = split_adjusted_column_names
self._split_adjusted_asof = split_adjusted_asof
super(BlazeSplitAdjustedEstimatesLoader, self).__init__(
expr,
columns,
**kwargs
)
def load_adjusted_array(self, domain, columns, dates, sids, mask):
# Only load requested columns.
requested_column_names = [self._columns[column.name]
for column in columns]
requested_spilt_adjusted_columns = [
column_name
for column_name in self._split_adjusted_column_names
if column_name in requested_column_names
]
raw = load_raw_data(
sids,
domain.data_query_cutoff_for_sessions(dates),
self._expr[sorted(metadata_columns.union(requested_column_names))],
self._odo_kwargs,
checkpoints=self._checkpoints,
)
return self.loader(
raw,
{column.name: self._columns[column.name] for column in columns},
self._split_adjustments,
requested_spilt_adjusted_columns,
self._split_adjusted_asof,
).load_adjusted_array(
domain,
columns,
dates,
sids,
mask,
)
class BlazeNextSplitAdjustedEstimatesLoader(BlazeSplitAdjustedEstimatesLoader):
loader = NextSplitAdjustedEarningsEstimatesLoader
class BlazePreviousSplitAdjustedEstimatesLoader(
BlazeSplitAdjustedEstimatesLoader
):
loader = PreviousSplitAdjustedEarningsEstimatesLoader | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/blaze/estimates.py | estimates.py |
from interface import implements
from datashape import istabular
from .core import (
bind_expression_to_resources,
)
from zipline.pipeline.common import (
SID_FIELD_NAME,
TS_FIELD_NAME,
EVENT_DATE_FIELD_NAME,
)
from zipline.pipeline.loaders.base import PipelineLoader
from zipline.pipeline.loaders.blaze.utils import load_raw_data
from zipline.pipeline.loaders.events import (
EventsLoader,
required_event_fields,
)
class BlazeEventsLoader(implements(PipelineLoader)):
"""An abstract pipeline loader for the events datasets that loads
data from a blaze expression.
Parameters
----------
expr : Expr
The expression representing the data to load.
next_value_columns : dict[BoundColumn -> raw column name]
A dict mapping 'next' BoundColumns to their column names in `expr`.
previous_value_columns : dict[BoundColumn -> raw column name]
A dict mapping 'previous' BoundColumns to their column names in `expr`.
resources : dict, optional
Mapping from the loadable terms of ``expr`` to actual data resources.
odo_kwargs : dict, optional
Extra keyword arguments to pass to odo when executing the expression.
Notes
-----
The expression should have a tabular dshape of::
Dim * {{
{SID_FIELD_NAME}: int64,
{TS_FIELD_NAME}: datetime,
{EVENT_DATE_FIELD_NAME}: datetime,
}}
And other dataset-specific fields, where each row of the table is a
record including the sid to identify the company, the timestamp where we
learned about the announcement, and the event date.
If the '{TS_FIELD_NAME}' field is not included it is assumed that we
start the backtest with knowledge of all announcements.
"""
__doc__ = __doc__.format(SID_FIELD_NAME=SID_FIELD_NAME,
TS_FIELD_NAME=TS_FIELD_NAME,
EVENT_DATE_FIELD_NAME=EVENT_DATE_FIELD_NAME)
def __init__(self,
expr,
next_value_columns,
previous_value_columns,
resources=None,
odo_kwargs=None):
dshape = expr.dshape
if not istabular(dshape):
raise ValueError(
'expression dshape must be tabular, got: %s' % dshape,
)
required_cols = list(
required_event_fields(next_value_columns, previous_value_columns)
)
self._expr = bind_expression_to_resources(
expr[required_cols],
resources,
)
self._next_value_columns = next_value_columns
self._previous_value_columns = previous_value_columns
self._odo_kwargs = odo_kwargs if odo_kwargs is not None else {}
def load_adjusted_array(self, domain, columns, dates, sids, mask):
raw = load_raw_data(
sids,
domain.data_query_cutoff_for_sessions(dates),
self._expr,
self._odo_kwargs,
)
return EventsLoader(
events=raw,
next_value_columns=self._next_value_columns,
previous_value_columns=self._previous_value_columns,
).load_adjusted_array(
domain,
columns,
dates,
sids,
mask,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/pipeline/loaders/blaze/events.py | events.py |
import pandas as pd
from sklearn.model_selection import train_test_split
from zipline.data import bundles
import dateutil.parser
import pandas_datareader.data as yahoo_reader
from zipline.pipeline.loaders import USEquityPricingLoader
from zipline.data.data_portal import DataPortal
from zipline.utils.calendars import get_calendar
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.engine import SimplePipelineEngine
BUNDLE_DATA = None
PRICING_LOADER = None
END_DT = None
def set_bundle_data(bundle_name='alpaca_api'):
global BUNDLE_DATA, PRICING_LOADER
BUNDLE_DATA = bundles.load(bundle_name)
PRICING_LOADER = USEquityPricingLoader.without_fx(BUNDLE_DATA.equity_daily_bar_reader,
BUNDLE_DATA.adjustment_reader)
def choose_loader(column):
""" Define the function for the get_loader parameter
Set the dataloader"""
if column not in USEquityPricing.columns:
raise Exception('Column not in USEquityPricing')
return PRICING_LOADER
def create_data_portal(_bundle_name, _trading_calendar, start_date):
global BUNDLE_DATA
if not BUNDLE_DATA:
set_bundle_data(_bundle_name)
# Create a data portal
data_portal = DataPortal(BUNDLE_DATA.asset_finder,
trading_calendar=_trading_calendar,
first_trading_day=start_date,
equity_daily_reader=BUNDLE_DATA.equity_daily_bar_reader,
adjustment_reader=BUNDLE_DATA.adjustment_reader)
return data_portal
def get_pricing(data_portal, trading_calendar, assets, start_date, end_date, field='close'):
# Set the given start and end dates to Timestamps. The frequency string C is used to
# indicate that a CustomBusinessDay DateOffset is used
global END_DT
END_DT = end_date
start_dt = start_date
# Get the locations of the start and end dates
end_loc = trading_calendar.closes.index.get_loc(END_DT)
start_loc = trading_calendar.closes.index.get_loc(start_dt)
# return the historical data for the given window
return data_portal.get_history_window(assets=assets, end_dt=END_DT, bar_count=end_loc - start_loc,
frequency='1d',
field=field,
data_frequency='daily')
def create_pipeline_engine(bundle_name='alpaca_api'):
global BUNDLE_DATA
if not BUNDLE_DATA:
set_bundle_data(bundle_name)
# Create a Pipeline engine
engine = SimplePipelineEngine(get_loader=choose_loader,
asset_finder=BUNDLE_DATA.asset_finder)
return engine
def get_equity(symbol):
return BUNDLE_DATA.asset_finder.lookup_symbol(symbol, END_DT)
def get_pipeline_output_for_equity(df, symbol, drop_level=False):
"""
pipeline output contains many equities, if you want to view the pipeline for jsut one equity
you could use this method which slices a multiindex df (dates and equities)
:param df:
:param symbol:
:param drop_level: if True it will drop the equity (level 1) index and return df with 1 level index.
:return:
"""
equity = get_equity(symbol)
df = df[df.index.get_level_values(1) == equity]
if drop_level:
df.index = df.index.droplevel(1)
return df
def pipeline_train_test_split(X,
y,
test_size=0.3,
validate_size=0.3,
should_validate=False):
"""
sklearn train_test_split
:param df:
:return:
"""
a, b, c, d = train_test_split(X.index.levels[0],
y.index.levels[0],
test_size=test_size,
random_state=101)
X_train = X.loc[list(a)]
X_test = X.loc[list(b)]
y_train = y.loc[list(c)]
y_test = y.loc[list(d)]
if should_validate:
a, b, c, d = train_test_split(X_train.index.levels[0],
y_train.index.levels[0],
test_size=validate_size,
random_state=101)
X_train = X.loc[list(a)]
X_validate = X.loc[list(b)]
y_train = y.loc[list(c)]
y_validate = y.loc[list(d)]
return X_train, X_validate, X_test, y_train, y_validate, y_test
return X_train, X_test, y_train, y_test
class DATE(str):
"""
date string in the format YYYY-MM-DD
"""
def __new__(cls, value):
if not value:
raise ValueError('Unexpected empty string')
if not isinstance(value, str):
raise TypeError(f'Unexpected type for DATE: "{type(value)}"')
if value.count("-") != 2:
raise ValueError(f'Unexpected date structure. expected '
f'"YYYY-MM-DD" got {value}')
try:
dateutil.parser.parse(value)
except Exception as e:
msg = f"{value} is not a valid date string: {e}"
raise Exception(msg)
return str.__new__(cls, value)
def get_benchmark(symbol=None, start: DATE = None, end: DATE = None, other_file_path=None):
bm = yahoo_reader.DataReader(symbol,
'yahoo',
pd.Timestamp(DATE(start)),
pd.Timestamp(DATE(end)))['Close']
bm.index = bm.index.tz_localize('UTC')
return bm.pct_change(periods=1).fillna(0) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/research/utils.py | utils.py |
from zipline.utils.calendars import get_calendar
class ExchangeInfo(object):
"""An exchange where assets are traded.
Parameters
----------
name : str or None
The full name of the exchange, for example 'NEW YORK STOCK EXCHANGE' or
'NASDAQ GLOBAL MARKET'.
canonical_name : str
The canonical name of the exchange, for example 'NYSE' or 'NASDAQ'. If
None this will be the same as the name.
country_code : str
The country code where the exchange is located.
Attributes
----------
name : str or None
The full name of the exchange, for example 'NEW YORK STOCK EXCHANGE' or
'NASDAQ GLOBAL MARKET'.
canonical_name : str
The canonical name of the exchange, for example 'NYSE' or 'NASDAQ'. If
None this will be the same as the name.
country_code : str
The country code where the exchange is located.
calendar : TradingCalendar
The trading calendar the exchange uses.
"""
def __init__(self, name, canonical_name, country_code):
self.name = name
if canonical_name is None:
canonical_name = name
self.canonical_name = canonical_name
self.country_code = country_code.upper()
def __repr__(self):
return '%s(%r, %r, %r)' % (
type(self).__name__,
self.name,
self.canonical_name,
self.country_code,
)
@property
def calendar(self):
"""The trading calendar that this exchange uses.
"""
return get_calendar(self.canonical_name)
def __eq__(self, other):
if not isinstance(other, ExchangeInfo):
return NotImplemented
return all(
getattr(self, attr) == getattr(other, attr)
for attr in ('name', 'canonical_name', 'country_code')
)
def __ne__(self, other):
eq = self == other
if eq is NotImplemented:
return NotImplemented
return not eq | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/assets/exchange_info.py | exchange_info.py |
from abc import ABCMeta
import array
import binascii
from collections import deque, namedtuple
from functools import partial
from numbers import Integral
from operator import itemgetter, attrgetter
import struct
from logbook import Logger
import numpy as np
import pandas as pd
from pandas import isnull
from six import with_metaclass, string_types, viewkeys, iteritems
import sqlalchemy as sa
from sqlalchemy.sql import text
from toolz import (
compose,
concat,
concatv,
curry,
groupby,
merge,
partition_all,
sliding_window,
valmap,
)
from zipline.errors import (
EquitiesNotFound,
FutureContractsNotFound,
MultipleSymbolsFound,
MultipleSymbolsFoundForFuzzySymbol,
MultipleValuesFoundForField,
MultipleValuesFoundForSid,
NoValueForSid,
ValueNotFoundForField,
SameSymbolUsedAcrossCountries,
SidsNotFound,
SymbolNotFound,
)
from . import (
Asset, Equity, Future,
)
from . continuous_futures import (
ADJUSTMENT_STYLES,
CHAIN_PREDICATES,
ContinuousFuture,
OrderedContracts,
)
from .asset_writer import (
check_version_info,
split_delimited_symbol,
asset_db_table_names,
symbol_columns,
SQLITE_MAX_VARIABLE_NUMBER,
)
from .asset_db_schema import (
ASSET_DB_VERSION
)
from .exchange_info import ExchangeInfo
from zipline.utils.functional import invert
from zipline.utils.memoize import lazyval
from zipline.utils.numpy_utils import as_column
from zipline.utils.preprocess import preprocess
from zipline.utils.db_utils import group_into_chunks, coerce_string_to_eng
log = Logger('assets.py')
# A set of fields that need to be converted to strings before building an
# Asset to avoid unicode fields
_asset_str_fields = frozenset({
'symbol',
'asset_name',
'exchange',
})
# A set of fields that need to be converted to timestamps in UTC
_asset_timestamp_fields = frozenset({
'start_date',
'end_date',
'first_traded',
'notice_date',
'expiration_date',
'auto_close_date',
})
OwnershipPeriod = namedtuple('OwnershipPeriod', 'start end sid value')
def merge_ownership_periods(mappings):
"""
Given a dict of mappings where the values are lists of
OwnershipPeriod objects, returns a dict with the same structure with
new OwnershipPeriod objects adjusted so that the periods have no
gaps.
Orders the periods chronologically, and pushes forward the end date
of each period to match the start date of the following period. The
end date of the last period pushed forward to the max Timestamp.
"""
return valmap(
lambda v: tuple(
OwnershipPeriod(
a.start,
b.start,
a.sid,
a.value,
) for a, b in sliding_window(
2,
concatv(
sorted(v),
# concat with a fake ownership object to make the last
# end date be max timestamp
[OwnershipPeriod(
pd.Timestamp.max.tz_localize('utc'),
None,
None,
None,
)],
),
)
),
mappings,
)
def _build_ownership_map_from_rows(rows, key_from_row, value_from_row):
mappings = {}
for row in rows:
mappings.setdefault(
key_from_row(row),
[],
).append(
OwnershipPeriod(
pd.Timestamp(row.start_date, unit='ns', tz='utc'),
pd.Timestamp(row.end_date, unit='ns', tz='utc'),
row.sid,
value_from_row(row),
),
)
return merge_ownership_periods(mappings)
def build_ownership_map(table, key_from_row, value_from_row):
"""
Builds a dict mapping to lists of OwnershipPeriods, from a db table.
"""
return _build_ownership_map_from_rows(
sa.select(table.c).execute().fetchall(),
key_from_row,
value_from_row,
)
def build_grouped_ownership_map(table,
key_from_row,
value_from_row,
group_key):
"""
Builds a dict mapping group keys to maps of keys to lists of
OwnershipPeriods, from a db table.
"""
grouped_rows = groupby(
group_key,
sa.select(table.c).execute().fetchall(),
)
return {
key: _build_ownership_map_from_rows(
rows,
key_from_row,
value_from_row,
)
for key, rows in grouped_rows.items()
}
@curry
def _filter_kwargs(names, dict_):
"""Filter out kwargs from a dictionary.
Parameters
----------
names : set[str]
The names to select from ``dict_``.
dict_ : dict[str, any]
The dictionary to select from.
Returns
-------
kwargs : dict[str, any]
``dict_`` where the keys intersect with ``names`` and the values are
not None.
"""
return {k: v for k, v in dict_.items() if k in names and v is not None}
_filter_future_kwargs = _filter_kwargs(Future._kwargnames)
_filter_equity_kwargs = _filter_kwargs(Equity._kwargnames)
def _convert_asset_timestamp_fields(dict_):
"""
Takes in a dict of Asset init args and converts dates to pd.Timestamps
"""
for key in _asset_timestamp_fields & viewkeys(dict_):
value = pd.Timestamp(dict_[key], tz='UTC')
dict_[key] = None if isnull(value) else value
return dict_
SID_TYPE_IDS = {
# Asset would be 0,
ContinuousFuture: 1,
}
CONTINUOUS_FUTURE_ROLL_STYLE_IDS = {
'calendar': 0,
'volume': 1,
}
CONTINUOUS_FUTURE_ADJUSTMENT_STYLE_IDS = {
None: 0,
'div': 1,
'add': 2,
}
def _encode_continuous_future_sid(root_symbol,
offset,
roll_style,
adjustment_style):
s = struct.Struct("B 2B B B B 2B")
# B - sid type
# 2B - root symbol
# B - offset (could be packed smaller since offsets of greater than 12 are
# probably unneeded.)
# B - roll type
# B - adjustment
# 2B - empty space left for parameterized roll types
# The root symbol currently supports 2 characters. If 3 char root symbols
# are needed, the size of the root symbol does not need to change, however
# writing the string directly will need to change to a scheme of writing
# the A-Z values in 5-bit chunks.
a = array.array('B', [0] * s.size)
rs = bytearray(root_symbol, 'ascii')
values = (SID_TYPE_IDS[ContinuousFuture],
rs[0],
rs[1],
offset,
CONTINUOUS_FUTURE_ROLL_STYLE_IDS[roll_style],
CONTINUOUS_FUTURE_ADJUSTMENT_STYLE_IDS[adjustment_style],
0, 0)
s.pack_into(a, 0, *values)
return int(binascii.hexlify(a), 16)
Lifetimes = namedtuple('Lifetimes', 'sid start end')
class AssetFinder(object):
"""
An AssetFinder is an interface to a database of Asset metadata written by
an ``AssetDBWriter``.
This class provides methods for looking up assets by unique integer id or
by symbol. For historical reasons, we refer to these unique ids as 'sids'.
Parameters
----------
engine : str or SQLAlchemy.engine
An engine with a connection to the asset database to use, or a string
that can be parsed by SQLAlchemy as a URI.
future_chain_predicates : dict
A dict mapping future root symbol to a predicate function which accepts
a contract as a parameter and returns whether or not the contract should be
included in the chain.
See Also
--------
:class:`zipline.assets.AssetDBWriter`
"""
@preprocess(engine=coerce_string_to_eng(require_exists=True))
def __init__(self, engine, future_chain_predicates=CHAIN_PREDICATES):
self.engine = engine
metadata = sa.MetaData(bind=engine)
metadata.reflect(only=asset_db_table_names)
for table_name in asset_db_table_names:
setattr(self, table_name, metadata.tables[table_name])
# Check the version info of the db for compatibility
check_version_info(engine, self.version_info, ASSET_DB_VERSION)
# Cache for lookup of assets by sid, the objects in the asset lookup
# may be shared with the results from equity and future lookup caches.
#
# The top level cache exists to minimize lookups on the asset type
# routing.
#
# The caches are read through, i.e. accessing an asset through
# retrieve_asset will populate the cache on first retrieval.
self._asset_cache = {}
self._asset_type_cache = {}
self._caches = (self._asset_cache, self._asset_type_cache)
self._future_chain_predicates = future_chain_predicates \
if future_chain_predicates is not None else {}
self._ordered_contracts = {}
# Populated on first call to `lifetimes`.
self._asset_lifetimes = {}
@lazyval
def exchange_info(self):
es = sa.select(self.exchanges.c).execute().fetchall()
return {
name: ExchangeInfo(name, canonical_name, country_code)
for name, canonical_name, country_code in es
}
@lazyval
def symbol_ownership_map(self):
out = {}
for mappings in self.symbol_ownership_maps_by_country_code.values():
for key, ownership_periods in mappings.items():
out.setdefault(key, []).extend(ownership_periods)
return out
@lazyval
def symbol_ownership_maps_by_country_code(self):
sid_to_country_code = dict(
sa.select((
self.equities.c.sid,
self.exchanges.c.country_code,
)).where(
self.equities.c.exchange == self.exchanges.c.exchange
).execute().fetchall(),
)
return build_grouped_ownership_map(
table=self.equity_symbol_mappings,
key_from_row=(
lambda row: (row.company_symbol, row.share_class_symbol)
),
value_from_row=lambda row: row.symbol,
group_key=lambda row: sid_to_country_code[row.sid],
)
@lazyval
def country_codes(self):
return tuple(self.symbol_ownership_maps_by_country_code)
@staticmethod
def _fuzzify_symbol_ownership_map(ownership_map):
fuzzy_mappings = {}
for (cs, scs), owners in iteritems(ownership_map):
fuzzy_owners = fuzzy_mappings.setdefault(
cs + scs,
[],
)
fuzzy_owners.extend(owners)
fuzzy_owners.sort()
return fuzzy_mappings
@lazyval
def fuzzy_symbol_ownership_map(self):
return self._fuzzify_symbol_ownership_map(self.symbol_ownership_map)
@lazyval
def fuzzy_symbol_ownership_maps_by_country_code(self):
return valmap(
self._fuzzify_symbol_ownership_map,
self.symbol_ownership_maps_by_country_code,
)
@lazyval
def equity_supplementary_map(self):
return build_ownership_map(
table=self.equity_supplementary_mappings,
key_from_row=lambda row: (row.field, row.value),
value_from_row=lambda row: row.value,
)
@lazyval
def equity_supplementary_map_by_sid(self):
return build_ownership_map(
table=self.equity_supplementary_mappings,
key_from_row=lambda row: (row.field, row.sid),
value_from_row=lambda row: row.value,
)
def lookup_asset_types(self, sids):
"""
Retrieve asset types for a list of sids.
Parameters
----------
sids : list[int]
Returns
-------
types : dict[sid -> str or None]
Asset types for the provided sids.
"""
found = {}
missing = set()
for sid in sids:
try:
found[sid] = self._asset_type_cache[sid]
except KeyError:
missing.add(sid)
if not missing:
return found
router_cols = self.asset_router.c
for assets in group_into_chunks(missing):
query = sa.select((router_cols.sid, router_cols.asset_type)).where(
self.asset_router.c.sid.in_(map(int, assets))
)
for sid, type_ in query.execute().fetchall():
missing.remove(sid)
found[sid] = self._asset_type_cache[sid] = type_
for sid in missing:
found[sid] = self._asset_type_cache[sid] = None
return found
def group_by_type(self, sids):
"""
Group a list of sids by asset type.
Parameters
----------
sids : list[int]
Returns
-------
types : dict[str or None -> list[int]]
A dict mapping unique asset types to lists of sids drawn from sids.
If we fail to look up an asset, we assign it a key of None.
"""
return invert(self.lookup_asset_types(sids))
def retrieve_asset(self, sid, default_none=False):
"""
Retrieve the Asset for a given sid.
"""
try:
asset = self._asset_cache[sid]
if asset is None and not default_none:
raise SidsNotFound(sids=[sid])
return asset
except KeyError:
return self.retrieve_all((sid,), default_none=default_none)[0]
def retrieve_all(self, sids, default_none=False):
"""
Retrieve all assets in `sids`.
Parameters
----------
sids : iterable of int
Assets to retrieve.
default_none : bool
If True, return None for failed lookups.
If False, raise `SidsNotFound`.
Returns
-------
assets : list[Asset or None]
A list of the same length as `sids` containing Assets (or Nones)
corresponding to the requested sids.
Raises
------
SidsNotFound
When a requested sid is not found and default_none=False.
"""
sids = list(sids)
hits, missing, failures = {}, set(), []
for sid in sids:
try:
asset = self._asset_cache[sid]
if not default_none and asset is None:
# Bail early if we've already cached that we don't know
# about an asset.
raise SidsNotFound(sids=[sid])
hits[sid] = asset
except KeyError:
missing.add(sid)
# All requests were cache hits. Return requested sids in order.
if not missing:
return [hits[sid] for sid in sids]
update_hits = hits.update
# Look up cache misses by type.
type_to_assets = self.group_by_type(missing)
# Handle failures
failures = {failure: None for failure in type_to_assets.pop(None, ())}
update_hits(failures)
self._asset_cache.update(failures)
if failures and not default_none:
raise SidsNotFound(sids=list(failures))
# We don't update the asset cache here because it should already be
# updated by `self.retrieve_equities`.
update_hits(self.retrieve_equities(type_to_assets.pop('equity', ())))
update_hits(
self.retrieve_futures_contracts(type_to_assets.pop('future', ()))
)
# We shouldn't know about any other asset types.
if type_to_assets:
raise AssertionError(
"Found asset types: %s" % list(type_to_assets.keys())
)
return [hits[sid] for sid in sids]
def retrieve_equities(self, sids):
"""
Retrieve Equity objects for a list of sids.
Users generally shouldn't need to this method (instead, they should
prefer the more general/friendly `retrieve_assets`), but it has a
documented interface and tests because it's used upstream.
Parameters
----------
sids : iterable[int]
Returns
-------
equities : dict[int -> Equity]
Raises
------
EquitiesNotFound
When any requested asset isn't found.
"""
return self._retrieve_assets(sids, self.equities, Equity)
def _retrieve_equity(self, sid):
return self.retrieve_equities((sid,))[sid]
def retrieve_futures_contracts(self, sids):
"""
Retrieve Future objects for an iterable of sids.
Users generally shouldn't need to this method (instead, they should
prefer the more general/friendly `retrieve_assets`), but it has a
documented interface and tests because it's used upstream.
Parameters
----------
sids : iterable[int]
Returns
-------
equities : dict[int -> Equity]
Raises
------
EquitiesNotFound
When any requested asset isn't found.
"""
return self._retrieve_assets(sids, self.futures_contracts, Future)
@staticmethod
def _select_assets_by_sid(asset_tbl, sids):
return sa.select([asset_tbl]).where(
asset_tbl.c.sid.in_(map(int, sids))
)
@staticmethod
def _select_asset_by_symbol(asset_tbl, symbol):
return sa.select([asset_tbl]).where(asset_tbl.c.symbol == symbol)
def _select_most_recent_symbols_chunk(self, sid_group):
"""Retrieve the most recent symbol for a set of sids.
Parameters
----------
sid_group : iterable[int]
The sids to lookup. The length of this sequence must be less than
or equal to SQLITE_MAX_VARIABLE_NUMBER because the sids will be
passed in as sql bind params.
Returns
-------
sel : Selectable
The sqlalchemy selectable that will query for the most recent
symbol for each sid.
Notes
-----
We search for the values with the biggest end-date to get most recent info about symbol.
First, we select the max end-date and sid then we join again to get
information for this specific enddate and sid
"""
cols = self.equity_symbol_mappings.c
# These are the columns we actually want.
data_cols = [str(cols.sid)] + [str(cols[name]) for name in symbol_columns]
# To be compatible with postgres we can't simple do a max and get all wanted fields
# from the same row that the maximum came from. Instead we solve this by a subquery.
# Sadly sqlalchemy in version < 1.4 does not support subquerys yet natively, we
# construct it with string-interpolation for now
max_cols = ','.join([str(cols.sid) + ' AS sid', 'MAX(' + str(cols.end_date) + ') AS max_date'])
to_select = ','.join(data_cols) + ',max_dates.max_date'
sids = ','.join([str(sid) for sid in sid_group])
max_date_select = f'(SELECT {max_cols} FROM {self.equity_symbol_mappings} GROUP BY {cols.sid}) AS max_dates'
query = text(f'SELECT {to_select} FROM {max_date_select} '
f'JOIN {self.equity_symbol_mappings} ON {cols.sid} = max_dates.sid '
f' WHERE {cols.sid} IN ({sids}) AND {cols.end_date} = max_dates.max_date')
return query
def _lookup_most_recent_symbols(self, sids):
return {
row.sid: {c: row[c] for c in symbol_columns}
for row in concat(
self.engine.execute(
self._select_most_recent_symbols_chunk(sid_group),
).fetchall()
for sid_group in partition_all(
SQLITE_MAX_VARIABLE_NUMBER,
sids
)
)
}
def _retrieve_asset_dicts(self, sids, asset_tbl, querying_equities):
if not sids:
return
if querying_equities:
def mkdict(row,
exchanges=self.exchange_info,
symbols=self._lookup_most_recent_symbols(sids)):
d = dict(row)
d['exchange_info'] = exchanges[d.pop('exchange')]
# we are not required to have a symbol for every asset, if
# we don't have any symbols we will just use the empty string
return merge(d, symbols.get(row['sid'], {}))
else:
def mkdict(row, exchanges=self.exchange_info):
d = dict(row)
d['exchange_info'] = exchanges[d.pop('exchange')]
return d
for assets in group_into_chunks(sids):
# Load misses from the db.
query = self._select_assets_by_sid(asset_tbl, assets)
for row in query.execute().fetchall():
yield _convert_asset_timestamp_fields(mkdict(row))
def _retrieve_assets(self, sids, asset_tbl, asset_type):
"""
Internal function for loading assets from a table.
This should be the only method of `AssetFinder` that writes Assets into
self._asset_cache.
Parameters
---------
sids : iterable of int
Asset ids to look up.
asset_tbl : sqlalchemy.Table
Table from which to query assets.
asset_type : type
Type of asset to be constructed.
Returns
-------
assets : dict[int -> Asset]
Dict mapping requested sids to the retrieved assets.
"""
# Fastpath for empty request.
if not sids:
return {}
cache = self._asset_cache
hits = {}
querying_equities = issubclass(asset_type, Equity)
filter_kwargs = (
_filter_equity_kwargs
if querying_equities else
_filter_future_kwargs
)
rows = self._retrieve_asset_dicts(sids, asset_tbl, querying_equities)
for row in rows:
sid = row['sid']
asset = asset_type(**filter_kwargs(row))
hits[sid] = cache[sid] = asset
# If we get here, it means something in our code thought that a
# particular sid was an equity/future and called this function with a
# concrete type, but we couldn't actually resolve the asset. This is
# an error in our code, not a user-input error.
misses = tuple(set(sids) - viewkeys(hits))
if misses:
if querying_equities:
raise EquitiesNotFound(sids=misses)
else:
raise FutureContractsNotFound(sids=misses)
return hits
def _lookup_symbol_strict(self,
ownership_map,
multi_country,
symbol,
as_of_date):
"""
Resolve a symbol to an asset object without fuzzy matching.
Parameters
----------
ownership_map : dict[(str, str), list[OwnershipPeriod]]
The mapping from split symbols to ownership periods.
multi_country : bool
Does this mapping span multiple countries?
symbol : str
The symbol to look up.
as_of_date : datetime or None
If multiple assets have held this sid, which day should the
resolution be checked against? If this value is None and multiple
sids have held the ticker, then a MultipleSymbolsFound error will
be raised.
Returns
-------
asset : Asset
The asset that held the given symbol.
Raises
------
SymbolNotFound
Raised when the symbol or symbol as_of_date pair do not map to
any assets.
MultipleSymbolsFound
Raised when multiple assets held the symbol. This happens if
multiple assets held the symbol at disjoint times and
``as_of_date`` is None, or if multiple assets held the symbol at
the same time and``multi_country`` is True.
Notes
-----
The resolution algorithm is as follows:
- Split the symbol into the company and share class component.
- Do a dictionary lookup of the
``(company_symbol, share_class_symbol)`` in the provided ownership
map.
- If there is no entry in the dictionary, we don't know about this
symbol so raise a ``SymbolNotFound`` error.
- If ``as_of_date`` is None:
- If more there is more than one owner, raise
``MultipleSymbolsFound``
- Otherwise, because the list mapped to a symbol cannot be empty,
return the single asset.
- Iterate through all of the owners:
- If the ``as_of_date`` is between the start and end of the ownership
period:
- If multi_country is False, return the found asset.
- Otherwise, put the asset in a list.
- At the end of the loop, if there are no candidate assets, raise a
``SymbolNotFound``.
- If there is exactly one candidate, return it.
- Othewise, raise ``MultipleSymbolsFound`` because the ticker is not
unique across countries.
"""
# split the symbol into the components, if there are no
# company/share class parts then share_class_symbol will be empty
company_symbol, share_class_symbol = split_delimited_symbol(symbol)
try:
owners = ownership_map[company_symbol, share_class_symbol]
assert owners, 'empty owners list for %r' % symbol
except KeyError:
# no equity has ever held this symbol
raise SymbolNotFound(symbol=symbol)
if not as_of_date:
# exactly one equity has ever held this symbol, we may resolve
# without the date
if len(owners) == 1:
return self.retrieve_asset(owners[0].sid)
options = {self.retrieve_asset(owner.sid) for owner in owners}
if multi_country:
country_codes = map(attrgetter('country_code'), options)
if len(set(country_codes)) > 1:
raise SameSymbolUsedAcrossCountries(
symbol=symbol,
options=dict(zip(country_codes, options))
)
# more than one equity has held this ticker, this
# is ambiguous without the date
raise MultipleSymbolsFound(symbol=symbol, options=options)
options = []
country_codes = []
for start, end, sid, _ in owners:
if start <= as_of_date < end:
# find the equity that owned it on the given asof date
asset = self.retrieve_asset(sid)
# if this asset owned the symbol on this asof date and we are
# only searching one country, return that asset
if not multi_country:
return asset
else:
options.append(asset)
country_codes.append(asset.country_code)
if not options:
# no equity held the ticker on the given asof date
raise SymbolNotFound(symbol=symbol)
# if there is one valid option given the asof date, return that option
if len(options) == 1:
return options[0]
# if there's more than one option given the asof date, a country code
# must be passed to resolve the symbol to an asset
raise SameSymbolUsedAcrossCountries(
symbol=symbol,
options=dict(zip(country_codes, options))
)
def _lookup_symbol_fuzzy(self,
ownership_map,
multi_country,
symbol,
as_of_date):
symbol = symbol.upper()
company_symbol, share_class_symbol = split_delimited_symbol(symbol)
try:
owners = ownership_map[company_symbol + share_class_symbol]
assert owners, 'empty owners list for %r' % symbol
except KeyError:
# no equity has ever held a symbol matching the fuzzy symbol
raise SymbolNotFound(symbol=symbol)
if not as_of_date:
if len(owners) == 1:
# only one valid match
return self.retrieve_asset(owners[0].sid)
options = []
for _, _, sid, sym in owners:
if sym == symbol:
# there are multiple options, look for exact matches
options.append(self.retrieve_asset(sid))
if len(options) == 1:
# there was only one exact match
return options[0]
# there is more than one exact match for this fuzzy symbol
raise MultipleSymbolsFoundForFuzzySymbol(
symbol=symbol,
options=self.retrieve_all(owner.sid for owner in owners),
)
options = {}
for start, end, sid, sym in owners:
if start <= as_of_date < end:
# see which fuzzy symbols were owned on the asof date.
options[sid] = sym
if not options:
# no equity owned the fuzzy symbol on the date requested
raise SymbolNotFound(symbol=symbol)
sid_keys = list(options.keys())
# If there was only one owner, or there is a fuzzy and non-fuzzy which
# map to the same sid, return it.
if len(options) == 1:
return self.retrieve_asset(sid_keys[0])
exact_options = []
for sid, sym in options.items():
# Possible to have a scenario where multiple fuzzy matches have the
# same date. Want to find the one where symbol and share class
# match.
if ((company_symbol, share_class_symbol) ==
split_delimited_symbol(sym)):
asset = self.retrieve_asset(sid)
if not multi_country:
return asset
else:
exact_options.append(asset)
if len(exact_options) == 1:
return exact_options[0]
# multiple equities held tickers matching the fuzzy ticker but
# there are no exact matches
raise MultipleSymbolsFoundForFuzzySymbol(
symbol=symbol,
options=self.retrieve_all(owner.sid for owner in owners),
)
def _choose_fuzzy_symbol_ownership_map(self, country_code):
if country_code is None:
return self.fuzzy_symbol_ownership_map
return self.fuzzy_symbol_ownership_maps_by_country_code.get(
country_code,
)
def _choose_symbol_ownership_map(self, country_code):
if country_code is None:
return self.symbol_ownership_map
return self.symbol_ownership_maps_by_country_code.get(country_code)
def lookup_symbol(self,
symbol,
as_of_date,
fuzzy=False,
country_code=None):
"""Lookup an equity by symbol.
Parameters
----------
symbol : str
The ticker symbol to resolve.
as_of_date : datetime.datetime or None
Look up the last owner of this symbol as of this datetime.
If ``as_of_date`` is None, then this can only resolve the equity
if exactly one equity has ever owned the ticker.
fuzzy : bool, optional
Should fuzzy symbol matching be used? Fuzzy symbol matching
attempts to resolve differences in representations for
shareclasses. For example, some people may represent the ``A``
shareclass of ``BRK`` as ``BRK.A``, where others could write
``BRK_A``.
country_code : str or None, optional
The country to limit searches to. If not provided, the search will
span all countries which increases the likelihood of an ambiguous
lookup.
Returns
-------
equity : Equity
The equity that held ``symbol`` on the given ``as_of_date``, or the
only equity to hold ``symbol`` if ``as_of_date`` is None.
Raises
------
SymbolNotFound
Raised when no equity has ever held the given symbol.
MultipleSymbolsFound
Raised when no ``as_of_date`` is given and more than one equity
has held ``symbol``. This is also raised when ``fuzzy=True`` and
there are multiple candidates for the given ``symbol`` on the
``as_of_date``. Also raised when no ``country_code`` is given and
the symbol is ambiguous across multiple countries.
"""
if symbol is None:
raise TypeError("Cannot lookup asset for symbol of None for "
"as of date %s." % as_of_date)
if fuzzy:
f = self._lookup_symbol_fuzzy
mapping = self._choose_fuzzy_symbol_ownership_map(country_code)
else:
f = self._lookup_symbol_strict
mapping = self._choose_symbol_ownership_map(country_code)
if mapping is None:
raise SymbolNotFound(symbol=symbol)
return f(
mapping,
country_code is None,
symbol,
as_of_date,
)
def get_max_sid(self):
table = self.equity_symbol_mappings
max_id = pd.read_sql(f'SELECT MAX(sid) max_id FROM {table}', self.engine)
if len(max_id) == 0 or max_id['max_id'][0] == None:
return -1
return max_id['max_id'][0]
def lookup_symbols(self,
symbols,
as_of_date,
fuzzy=False,
country_code=None):
"""
Lookup a list of equities by symbol.
Equivalent to::
[finder.lookup_symbol(s, as_of, fuzzy) for s in symbols]
but potentially faster because repeated lookups are memoized.
Parameters
----------
symbols : sequence[str]
Sequence of ticker symbols to resolve.
as_of_date : pd.Timestamp
Forwarded to ``lookup_symbol``.
fuzzy : bool, optional
Forwarded to ``lookup_symbol``.
country_code : str or None, optional
The country to limit searches to. If not provided, the search will
span all countries which increases the likelihood of an ambiguous
lookup.
Returns
-------
equities : list[Equity]
"""
if not symbols:
return []
multi_country = country_code is None
if fuzzy:
f = self._lookup_symbol_fuzzy
mapping = self._choose_fuzzy_symbol_ownership_map(country_code)
else:
f = self._lookup_symbol_strict
mapping = self._choose_symbol_ownership_map(country_code)
if mapping is None:
raise SymbolNotFound(symbol=symbols[0])
memo = {}
out = []
append_output = out.append
for sym in symbols:
if sym in memo:
append_output(memo[sym])
else:
equity = memo[sym] = f(
mapping,
multi_country,
sym,
as_of_date,
)
append_output(equity)
return out
def lookup_future_symbol(self, symbol):
"""Lookup a future contract by symbol.
Parameters
----------
symbol : str
The symbol of the desired contract.
Returns
-------
future : Future
The future contract referenced by ``symbol``.
Raises
------
SymbolNotFound
Raised when no contract named 'symbol' is found.
"""
data = self._select_asset_by_symbol(self.futures_contracts, symbol)\
.execute().fetchone()
# If no data found, raise an exception
if not data:
raise SymbolNotFound(symbol=symbol)
return self.retrieve_asset(data['sid'])
def lookup_by_supplementary_field(self, field_name, value, as_of_date):
try:
owners = self.equity_supplementary_map[
field_name,
value,
]
assert owners, 'empty owners list for %r' % (field_name, value)
except KeyError:
# no equity has ever held this value
raise ValueNotFoundForField(field=field_name, value=value)
if not as_of_date:
if len(owners) > 1:
# more than one equity has held this value, this is ambigious
# without the date
raise MultipleValuesFoundForField(
field=field_name,
value=value,
options=set(map(
compose(self.retrieve_asset, attrgetter('sid')),
owners,
)),
)
# exactly one equity has ever held this value, we may resolve
# without the date
return self.retrieve_asset(owners[0].sid)
for start, end, sid, _ in owners:
if start <= as_of_date < end:
# find the equity that owned it on the given asof date
return self.retrieve_asset(sid)
# no equity held the value on the given asof date
raise ValueNotFoundForField(field=field_name, value=value)
def get_supplementary_field(self, sid, field_name, as_of_date):
"""Get the value of a supplementary field for an asset.
Parameters
----------
sid : int
The sid of the asset to query.
field_name : str
Name of the supplementary field.
as_of_date : pd.Timestamp, None
The last known value on this date is returned. If None, a
value is returned only if we've only ever had one value for
this sid. If None and we've had multiple values,
MultipleValuesFoundForSid is raised.
Raises
------
NoValueForSid
If we have no values for this asset, or no values was known
on this as_of_date.
MultipleValuesFoundForSid
If we have had multiple values for this asset over time, and
None was passed for as_of_date.
"""
try:
periods = self.equity_supplementary_map_by_sid[
field_name,
sid,
]
assert periods, 'empty periods list for %r' % (field_name, sid)
except KeyError:
raise NoValueForSid(field=field_name, sid=sid)
if not as_of_date:
if len(periods) > 1:
# This equity has held more than one value, this is ambigious
# without the date
raise MultipleValuesFoundForSid(
field=field_name,
sid=sid,
options={p.value for p in periods},
)
# this equity has only ever held this value, we may resolve
# without the date
return periods[0].value
for start, end, _, value in periods:
if start <= as_of_date < end:
return value
# Could not find a value for this sid on the as_of_date.
raise NoValueForSid(field=field_name, sid=sid)
def _get_contract_sids(self, root_symbol):
fc_cols = self.futures_contracts.c
return [r.sid for r in
list(sa.select((fc_cols.sid,)).where(
(fc_cols.root_symbol == root_symbol) &
(fc_cols.start_date != pd.NaT.value)).order_by(
fc_cols.sid).execute().fetchall())]
def _get_root_symbol_exchange(self, root_symbol):
fc_cols = self.futures_root_symbols.c
fields = (fc_cols.exchange,)
exchange = sa.select(fields).where(
fc_cols.root_symbol == root_symbol).execute().scalar()
if exchange is not None:
return exchange
else:
raise SymbolNotFound(symbol=root_symbol)
def get_ordered_contracts(self, root_symbol):
try:
return self._ordered_contracts[root_symbol]
except KeyError:
contract_sids = self._get_contract_sids(root_symbol)
contracts = deque(self.retrieve_all(contract_sids))
chain_predicate = self._future_chain_predicates.get(root_symbol,
None)
oc = OrderedContracts(root_symbol, contracts, chain_predicate)
self._ordered_contracts[root_symbol] = oc
return oc
def create_continuous_future(self,
root_symbol,
offset,
roll_style,
adjustment):
if adjustment not in ADJUSTMENT_STYLES:
raise ValueError(
'Invalid adjustment style {!r}. Allowed adjustment styles are '
'{}.'.format(adjustment, list(ADJUSTMENT_STYLES))
)
oc = self.get_ordered_contracts(root_symbol)
exchange = self._get_root_symbol_exchange(root_symbol)
sid = _encode_continuous_future_sid(root_symbol, offset,
roll_style,
None)
mul_sid = _encode_continuous_future_sid(root_symbol, offset,
roll_style,
'div')
add_sid = _encode_continuous_future_sid(root_symbol, offset,
roll_style,
'add')
cf_template = partial(
ContinuousFuture,
root_symbol=root_symbol,
offset=offset,
roll_style=roll_style,
start_date=oc.start_date,
end_date=oc.end_date,
exchange_info=self.exchange_info[exchange],
)
cf = cf_template(sid=sid)
mul_cf = cf_template(sid=mul_sid, adjustment='mul')
add_cf = cf_template(sid=add_sid, adjustment='add')
self._asset_cache[cf.sid] = cf
self._asset_cache[mul_cf.sid] = mul_cf
self._asset_cache[add_cf.sid] = add_cf
return {None: cf, 'mul': mul_cf, 'add': add_cf}[adjustment]
def _make_sids(tblattr):
def _(self):
return tuple(map(
itemgetter('sid'),
sa.select((
getattr(self, tblattr).c.sid,
)).execute().fetchall(),
))
return _
sids = property(
_make_sids('asset_router'),
doc='All the sids in the asset finder.',
)
equities_sids = property(
_make_sids('equities'),
doc='All of the sids for equities in the asset finder.',
)
futures_sids = property(
_make_sids('futures_contracts'),
doc='All of the sids for futures consracts in the asset finder.',
)
del _make_sids
def _lookup_generic_scalar(self,
obj,
as_of_date,
country_code,
matches,
missing):
"""
Convert asset_convertible to an asset.
On success, append to matches.
On failure, append to missing.
"""
result = self._lookup_generic_scalar_helper(
obj, as_of_date, country_code,
)
if result is not None:
matches.append(result)
else:
missing.append(obj)
def _lookup_generic_scalar_helper(self, obj, as_of_date, country_code):
if isinstance(obj, (Asset, ContinuousFuture)):
return obj
if isinstance(obj, Integral):
try:
return self.retrieve_asset(int(obj))
except SidsNotFound:
return None
if isinstance(obj, string_types):
# Try to look up as an equity first.
try:
return self.lookup_symbol(
symbol=obj,
as_of_date=as_of_date,
country_code=country_code
)
except SymbolNotFound:
# Fall back to lookup as a Future
try:
# TODO: Support country_code for future_symbols?
return self.lookup_future_symbol(obj)
except SymbolNotFound:
return None
raise NotAssetConvertible("Input was %s, not AssetConvertible." % obj)
def lookup_generic(self, obj, as_of_date, country_code):
"""
Convert an object into an Asset or sequence of Assets.
This method exists primarily as a convenience for implementing
user-facing APIs that can handle multiple kinds of input. It should
not be used for internal code where we already know the expected types
of our inputs.
Parameters
----------
obj : int, str, Asset, ContinuousFuture, or iterable
The object to be converted into one or more Assets.
Integers are interpreted as sids. Strings are interpreted as
tickers. Assets and ContinuousFutures are returned unchanged.
as_of_date : pd.Timestamp or None
Timestamp to use to disambiguate ticker lookups. Has the same
semantics as in `lookup_symbol`.
country_code : str or None
ISO-3166 country code to use to disambiguate ticker lookups. Has
the same semantics as in `lookup_symbol`.
Returns
-------
matches, missing : tuple
``matches`` is the result of the conversion. ``missing`` is a list
containing any values that couldn't be resolved. If ``obj`` is not
an iterable, ``missing`` will be an empty list.
"""
matches = []
missing = []
# Interpret input as scalar.
if isinstance(obj, (AssetConvertible, ContinuousFuture)):
self._lookup_generic_scalar(
obj=obj,
as_of_date=as_of_date,
country_code=country_code,
matches=matches,
missing=missing,
)
try:
return matches[0], missing
except IndexError:
if hasattr(obj, '__int__'):
raise SidsNotFound(sids=[obj])
else:
raise SymbolNotFound(symbol=obj)
# Interpret input as iterable.
try:
iterator = iter(obj)
except TypeError:
raise NotAssetConvertible(
"Input was not a AssetConvertible "
"or iterable of AssetConvertible."
)
for obj in iterator:
self._lookup_generic_scalar(
obj=obj,
as_of_date=as_of_date,
country_code=country_code,
matches=matches,
missing=missing,
)
return matches, missing
def _compute_asset_lifetimes(self, country_codes):
"""
Compute and cache a recarray of asset lifetimes.
"""
sids = starts = ends = []
equities_cols = self.equities.c
# if country_codes:
# results = sa.select((
# equities_cols.sid,
# equities_cols.start_date,
# equities_cols.end_date,
# )).where(
# (self.exchanges.c.exchange == equities_cols.exchange) &
# (self.exchanges.c.country_code.in_(country_codes))
# ).execute().fetchall()
# if results:
# sids, starts, ends = zip(*results)
# TODO Domain bypass
if country_codes:
results = sa.select((
equities_cols.sid,
equities_cols.start_date,
equities_cols.end_date,
)).execute().fetchall()
if results:
sids, starts, ends = zip(*results)
sid = np.array(sids, dtype='i8')
start = np.array(starts, dtype='f8')
end = np.array(ends, dtype='f8')
start[np.isnan(start)] = 0 # convert missing starts to 0
end[np.isnan(end)] = np.iinfo(int).max # convert missing end to INTMAX
return Lifetimes(sid, start.astype('i8'), end.astype('i8'))
def lifetimes(self, dates, include_start_date, country_codes):
"""
Compute a DataFrame representing asset lifetimes for the specified date
range.
Parameters
----------
dates : pd.DatetimeIndex
The dates for which to compute lifetimes.
include_start_date : bool
Whether or not to count the asset as alive on its start_date.
This is useful in a backtesting context where `lifetimes` is being
used to signify "do I have data for this asset as of the morning of
this date?" For many financial metrics, (e.g. daily close), data
isn't available for an asset until the end of the asset's first
day.
country_codes : iterable[str]
The country codes to get lifetimes for.
Returns
-------
lifetimes : pd.DataFrame
A frame of dtype bool with `dates` as index and an Int64Index of
assets as columns. The value at `lifetimes.loc[date, asset]` will
be True iff `asset` existed on `date`. If `include_start_date` is
False, then lifetimes.loc[date, asset] will be false when date ==
asset.start_date.
See Also
--------
numpy.putmask
zipline.pipeline.engine.SimplePipelineEngine._compute_root_mask
"""
if isinstance(country_codes, string_types):
raise TypeError(
"Got string {!r} instead of an iterable of strings in "
"AssetFinder.lifetimes.".format(country_codes),
)
# normalize to a cache-key so that we can memoize results.
country_codes = frozenset(country_codes)
lifetimes = self._asset_lifetimes.get(country_codes)
if lifetimes is None:
self._asset_lifetimes[country_codes] = lifetimes = (
self._compute_asset_lifetimes(country_codes)
)
raw_dates = as_column(dates.asi8)
if include_start_date:
mask = lifetimes.start <= raw_dates
else:
mask = lifetimes.start < raw_dates
mask &= (raw_dates <= lifetimes.end)
return pd.DataFrame(mask, index=dates, columns=lifetimes.sid)
def equities_sids_for_country_code(self, country_code):
"""Return all of the sids for a given country.
Parameters
----------
country_code : str
An ISO 3166 alpha-2 country code.
Returns
-------
tuple[int]
The sids whose exchanges are in this country.
"""
sids = self._compute_asset_lifetimes([country_code]).sid
return tuple(sids.tolist())
class AssetConvertible(with_metaclass(ABCMeta)):
"""
ABC for types that are convertible to integer-representations of
Assets.
Includes Asset, six.string_types, and Integral
"""
pass
AssetConvertible.register(Integral)
AssetConvertible.register(Asset)
# Use six.string_types for Python2/3 compatibility
for _type in string_types:
AssetConvertible.register(_type)
class NotAssetConvertible(ValueError):
pass
class PricingDataAssociable(with_metaclass(ABCMeta)):
"""
ABC for types that can be associated with pricing data.
Includes Asset, Future, ContinuousFuture
"""
pass
PricingDataAssociable.register(Asset)
PricingDataAssociable.register(Future)
PricingDataAssociable.register(ContinuousFuture)
def was_active(reference_date_value, asset):
"""
Whether or not `asset` was active at the time corresponding to
`reference_date_value`.
Parameters
----------
reference_date_value : int
Date, represented as nanoseconds since EPOCH, for which we want to know
if `asset` was alive. This is generally the result of accessing the
`value` attribute of a pandas Timestamp.
asset : Asset
The asset object to check.
Returns
-------
was_active : bool
Whether or not the `asset` existed at the specified time.
"""
return (
asset.start_date.value
<= reference_date_value
<= asset.end_date.value
)
def only_active_assets(reference_date_value, assets):
"""
Filter an iterable of Asset objects down to just assets that were alive at
the time corresponding to `reference_date_value`.
Parameters
----------
reference_date_value : int
Date, represented as nanoseconds since EPOCH, for which we want to know
if `asset` was alive. This is generally the result of accessing the
`value` attribute of a pandas Timestamp.
assets : iterable[Asset]
The assets to filter.
Returns
-------
active_assets : list
List of the active assets from `assets` on the requested date.
"""
return [a for a in assets if was_active(reference_date_value, a)] | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/assets/assets.py | assets.py |
from itertools import product
from string import ascii_uppercase
import pandas as pd
from pandas.tseries.offsets import MonthBegin
from .futures import CMES_CODE_TO_MONTH
def make_rotating_equity_info(num_assets,
first_start,
frequency,
periods_between_starts,
asset_lifetime,
exchange='TEST'):
"""
Create a DataFrame representing lifetimes of assets that are constantly
rotating in and out of existence.
Parameters
----------
num_assets : int
How many assets to create.
first_start : pd.Timestamp
The start date for the first asset.
frequency : str or pd.tseries.offsets.Offset (e.g. trading_day)
Frequency used to interpret next two arguments.
periods_between_starts : int
Create a new asset every `frequency` * `periods_between_new`
asset_lifetime : int
Each asset exists for `frequency` * `asset_lifetime` days.
exchange : str, optional
The exchange name.
Returns
-------
info : pd.DataFrame
DataFrame representing newly-created assets.
"""
return pd.DataFrame(
{
'symbol': [chr(ord('A') + i) for i in range(num_assets)],
# Start a new asset every `periods_between_starts` days.
'start_date': pd.date_range(
first_start,
freq=(periods_between_starts * frequency),
periods=num_assets,
),
# Each asset lasts for `asset_lifetime` days.
'end_date': pd.date_range(
first_start + (asset_lifetime * frequency),
freq=(periods_between_starts * frequency),
periods=num_assets,
),
'exchange': exchange,
},
index=range(num_assets),
)
def make_simple_equity_info(sids,
start_date,
end_date,
symbols=None,
names=None,
exchange='TEST'):
"""
Create a DataFrame representing assets that exist for the full duration
between `start_date` and `end_date`.
Parameters
----------
sids : array-like of int
start_date : pd.Timestamp, optional
end_date : pd.Timestamp, optional
symbols : list, optional
Symbols to use for the assets.
If not provided, symbols are generated from the sequence 'A', 'B', ...
names : list, optional
Names to use for the assets.
If not provided, names are generated by adding " INC." to each of the
symbols (which might also be auto-generated).
exchange : str, optional
The exchange name.
Returns
-------
info : pd.DataFrame
DataFrame representing newly-created assets.
"""
num_assets = len(sids)
if symbols is None:
symbols = list(ascii_uppercase[:num_assets])
else:
symbols = list(symbols)
if names is None:
names = [str(s) + " INC." for s in symbols]
return pd.DataFrame(
{
'symbol': symbols,
'start_date': pd.to_datetime([start_date] * num_assets),
'end_date': pd.to_datetime([end_date] * num_assets),
'asset_name': list(names),
'exchange': exchange,
},
index=sids,
columns=(
'start_date',
'end_date',
'symbol',
'exchange',
'asset_name',
),
)
def make_simple_multi_country_equity_info(countries_to_sids,
countries_to_exchanges,
start_date,
end_date):
"""Create a DataFrame representing assets that exist for the full duration
between `start_date` and `end_date`, from multiple countries.
"""
sids = []
symbols = []
exchanges = []
for country, country_sids in countries_to_sids.items():
exchange = countries_to_exchanges[country]
for i, sid in enumerate(country_sids):
sids.append(sid)
symbols.append('-'.join([country, str(i)]))
exchanges.append(exchange)
return pd.DataFrame(
{
'symbol': symbols,
'start_date': start_date,
'end_date': end_date,
'asset_name': symbols,
'exchange': exchanges,
},
index=sids,
columns=(
'start_date',
'end_date',
'symbol',
'exchange',
'asset_name',
),
)
def make_jagged_equity_info(num_assets,
start_date,
first_end,
frequency,
periods_between_ends,
auto_close_delta):
"""
Create a DataFrame representing assets that all begin at the same start
date, but have cascading end dates.
Parameters
----------
num_assets : int
How many assets to create.
start_date : pd.Timestamp
The start date for all the assets.
first_end : pd.Timestamp
The date at which the first equity will end.
frequency : str or pd.tseries.offsets.Offset (e.g. trading_day)
Frequency used to interpret the next argument.
periods_between_ends : int
Starting after the first end date, end each asset every
`frequency` * `periods_between_ends`.
Returns
-------
info : pd.DataFrame
DataFrame representing newly-created assets.
"""
frame = pd.DataFrame(
{
'symbol': [chr(ord('A') + i) for i in range(num_assets)],
'start_date': start_date,
'end_date': pd.date_range(
first_end,
freq=(periods_between_ends * frequency),
periods=num_assets,
),
'exchange': 'TEST',
},
index=range(num_assets),
)
# Explicitly pass None to disable setting the auto_close_date column.
if auto_close_delta is not None:
frame['auto_close_date'] = frame['end_date'] + auto_close_delta
return frame
def make_future_info(first_sid,
root_symbols,
years,
notice_date_func,
expiration_date_func,
start_date_func,
month_codes=None,
multiplier=500):
"""
Create a DataFrame representing futures for `root_symbols` during `year`.
Generates a contract per triple of (symbol, year, month) supplied to
`root_symbols`, `years`, and `month_codes`.
Parameters
----------
first_sid : int
The first sid to use for assigning sids to the created contracts.
root_symbols : list[str]
A list of root symbols for which to create futures.
years : list[int or str]
Years (e.g. 2014), for which to produce individual contracts.
notice_date_func : (Timestamp) -> Timestamp
Function to generate notice dates from first of the month associated
with asset month code. Return NaT to simulate futures with no notice
date.
expiration_date_func : (Timestamp) -> Timestamp
Function to generate expiration dates from first of the month
associated with asset month code.
start_date_func : (Timestamp) -> Timestamp, optional
Function to generate start dates from first of the month associated
with each asset month code. Defaults to a start_date one year prior
to the month_code date.
month_codes : dict[str -> [1..12]], optional
Dictionary of month codes for which to create contracts. Entries
should be strings mapped to values from 1 (January) to 12 (December).
Default is zipline.futures.CMES_CODE_TO_MONTH
multiplier : int
The contract multiplier.
Returns
-------
futures_info : pd.DataFrame
DataFrame of futures data suitable for passing to an AssetDBWriter.
"""
if month_codes is None:
month_codes = CMES_CODE_TO_MONTH
year_strs = list(map(str, years))
years = [pd.Timestamp(s, tz='UTC') for s in year_strs]
# Pairs of string/date like ('K06', 2006-05-01) sorted by year/month
# `MonthBegin(month_num - 1)` since the year already starts at month 1.
contract_suffix_to_beginning_of_month = tuple(
(month_code + year_str[-2:], year + MonthBegin(month_num - 1))
for ((year, year_str), (month_code, month_num))
in product(
zip(years, year_strs),
sorted(list(month_codes.items()), key=lambda item: item[1]),
)
)
contracts = []
parts = product(root_symbols, contract_suffix_to_beginning_of_month)
for sid, (root_sym, (suffix, month_begin)) in enumerate(parts, first_sid):
contracts.append({
'sid': sid,
'root_symbol': root_sym,
'symbol': root_sym + suffix,
'start_date': start_date_func(month_begin),
'notice_date': notice_date_func(month_begin),
'expiration_date': expiration_date_func(month_begin),
'multiplier': multiplier,
'exchange': "TEST",
})
return pd.DataFrame.from_records(contracts, index='sid')
def make_commodity_future_info(first_sid,
root_symbols,
years,
month_codes=None,
multiplier=500):
"""
Make futures testing data that simulates the notice/expiration date
behavior of physical commodities like oil.
Parameters
----------
first_sid : int
The first sid to use for assigning sids to the created contracts.
root_symbols : list[str]
A list of root symbols for which to create futures.
years : list[int or str]
Years (e.g. 2014), for which to produce individual contracts.
month_codes : dict[str -> [1..12]], optional
Dictionary of month codes for which to create contracts. Entries
should be strings mapped to values from 1 (January) to 12 (December).
Default is zipline.futures.CMES_CODE_TO_MONTH
multiplier : int
The contract multiplier.
Expiration dates are on the 20th of the month prior to the month code.
Notice dates are are on the 20th two months prior to the month code.
Start dates are one year before the contract month.
See Also
--------
make_future_info
"""
nineteen_days = pd.Timedelta(days=19)
one_year = pd.Timedelta(days=365)
return make_future_info(
first_sid=first_sid,
root_symbols=root_symbols,
years=years,
notice_date_func=lambda dt: dt - MonthBegin(2) + nineteen_days,
expiration_date_func=lambda dt: dt - MonthBegin(1) + nineteen_days,
start_date_func=lambda dt: dt - one_year,
month_codes=month_codes,
multiplier=multiplier,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/assets/synthetic.py | synthetic.py |
from collections import namedtuple
import re
import numpy as np
import pandas as pd
import sqlalchemy as sa
from toolz import first
from zipline.errors import AssetDBVersionError
from zipline.assets.asset_db_schema import (
ASSET_DB_VERSION,
asset_db_table_names,
asset_router,
equities as equities_table,
equity_symbol_mappings,
equity_supplementary_mappings as equity_supplementary_mappings_table,
futures_contracts as futures_contracts_table,
exchanges as exchanges_table,
futures_root_symbols,
metadata,
version_info,
)
from sqlalchemy.exc import IntegrityError
from zipline.utils.compat import ExitStack
from zipline.utils.preprocess import preprocess
from zipline.utils.range import from_tuple, intersecting_ranges
from zipline.utils.db_utils import coerce_string_to_eng
# Define a namedtuple for use with the load_data and _load_data methods
AssetData = namedtuple(
'AssetData', (
'equities',
'equities_mappings',
'futures',
'exchanges',
'root_symbols',
'equity_supplementary_mappings',
),
)
SQLITE_MAX_VARIABLE_NUMBER = 999
symbol_columns = frozenset({
'symbol',
'company_symbol',
'share_class_symbol',
})
mapping_columns = symbol_columns | {'start_date', 'end_date'}
_index_columns = {
'equities': 'sid',
'equity_supplementary_mappings': 'sid',
'futures': 'sid',
'exchanges': 'exchange',
'root_symbols': 'root_symbol',
}
def _normalize_index_columns_in_place(equities,
equity_supplementary_mappings,
futures,
exchanges,
root_symbols):
"""
Update dataframes in place to set indentifier columns as indices.
For each input frame, if the frame has a column with the same name as its
associated index column, set that column as the index.
Otherwise, assume the index already contains identifiers.
If frames are passed as None, they're ignored.
"""
for frame, column_name in ((equities, 'sid'),
(equity_supplementary_mappings, 'sid'),
(futures, 'sid'),
(exchanges, 'exchange'),
(root_symbols, 'root_symbol')):
if frame is not None and column_name in frame:
frame.set_index(column_name, inplace=True)
if frame is not None:
frame.index.rename(column_name, inplace=True)
def _default_none(df, column):
return None
def _no_default(df, column):
if not df.empty:
raise ValueError('no default value for column %r' % column)
# Default values for the equities DataFrame
_equities_defaults = {
'symbol': _default_none,
'asset_name': _default_none,
'start_date': lambda df, col: 0,
'end_date': lambda df, col: np.iinfo(np.int64).max,
'first_traded': _default_none,
'auto_close_date': _default_none,
# the full exchange name
'exchange': _no_default,
}
# the defaults for ``equities`` in ``write_direct``
_direct_equities_defaults = _equities_defaults.copy()
del _direct_equities_defaults['symbol']
# Default values for the futures DataFrame
_futures_defaults = {
'symbol': _default_none,
'root_symbol': _default_none,
'asset_name': _default_none,
'start_date': lambda df, col: 0,
'end_date': lambda df, col: np.iinfo(np.int64).max,
'first_traded': _default_none,
'exchange': _default_none,
'notice_date': _default_none,
'expiration_date': _default_none,
'auto_close_date': _default_none,
'tick_size': _default_none,
'multiplier': lambda df, col: 1,
}
# Default values for the exchanges DataFrame
_exchanges_defaults = {
'canonical_name': lambda df, col: df.index,
'country_code': lambda df, col: '??',
}
# Default values for the root_symbols DataFrame
_root_symbols_defaults = {
'sector': _default_none,
'description': _default_none,
'exchange': _default_none,
}
# Default values for the equity_supplementary_mappings DataFrame
_equity_supplementary_mappings_defaults = {
'value': _default_none,
'field': _default_none,
'start_date': lambda df, col: 0,
'end_date': lambda df, col: np.iinfo(np.int64).max,
}
# Default values for the equity_symbol_mappings DataFrame
_equity_symbol_mappings_defaults = {
'sid': _no_default,
'company_symbol': _default_none,
'share_class_symbol': _default_none,
'symbol': _default_none,
'start_date': lambda df, col: 0,
'end_date': lambda df, col: np.iinfo(np.int64).max,
}
# Fuzzy symbol delimiters that may break up a company symbol and share class
_delimited_symbol_delimiters_regex = re.compile(r'[./\-_]')
_delimited_symbol_default_triggers = frozenset({np.nan, None, ''})
def split_delimited_symbol(symbol):
"""
Takes in a symbol that may be delimited and splits it in to a company
symbol and share class symbol. Also returns the fuzzy symbol, which is the
symbol without any fuzzy characters at all.
Parameters
----------
symbol : str
The possibly-delimited symbol to be split
Returns
-------
company_symbol : str
The company part of the symbol.
share_class_symbol : str
The share class part of a symbol.
"""
# return blank strings for any bad fuzzy symbols, like NaN or None
if symbol in _delimited_symbol_default_triggers:
return '', ''
symbol = symbol.upper()
split_list = re.split(
pattern=_delimited_symbol_delimiters_regex,
string=symbol,
maxsplit=1,
)
# Break the list up in to its two components, the company symbol and the
# share class symbol
company_symbol = split_list[0]
if len(split_list) > 1:
share_class_symbol = split_list[1]
else:
share_class_symbol = ''
return company_symbol, share_class_symbol
def _generate_output_dataframe(data_subset, defaults):
"""
Generates an output dataframe from the given subset of user-provided
data, the given column names, and the given default values.
Parameters
----------
data_subset : DataFrame
A DataFrame, usually from an AssetData object,
that contains the user's input metadata for the asset type being
processed
defaults : dict
A dict where the keys are the names of the columns of the desired
output DataFrame and the values are a function from dataframe and
column name to the default values to insert in the DataFrame if no user
data is provided
Returns
-------
DataFrame
A DataFrame containing all user-provided metadata, and default values
wherever user-provided metadata was missing
"""
# The columns provided.
cols = set(data_subset.columns)
desired_cols = set(defaults)
# Drop columns with unrecognised headers.
data_subset.drop(cols - desired_cols,
axis=1,
inplace=True)
# Get those columns which we need but
# for which no data has been supplied.
for col in desired_cols - cols:
# write the default value for any missing columns
data_subset[col] = defaults[col](data_subset, col)
return data_subset
def _check_asset_group(group):
row = group.sort_values('end_date').iloc[-1]
row.start_date = group.start_date.min()
row.end_date = group.end_date.max()
row.drop(list(symbol_columns), inplace=True)
return row
def _format_range(r):
return (
str(pd.Timestamp(r.start, unit='ns')),
str(pd.Timestamp(r.stop, unit='ns')),
)
def _check_symbol_mappings(df, exchanges, asset_exchange):
"""Check that there are no cases where multiple symbols resolve to the same
asset at the same time in the same country.
Parameters
----------
df : pd.DataFrame
The equity symbol mappings table.
exchanges : pd.DataFrame
The exchanges table.
asset_exchange : pd.Series
A series that maps sids to the exchange the asset is in.
Raises
------
ValueError
Raised when there are ambiguous symbol mappings.
"""
mappings = df.set_index('sid')[list(mapping_columns)].copy()
if not exchanges.empty and not exchanges.index.name == 'exchange':
exchanges.index = exchanges['exchange']
mappings['country_code'] = exchanges['country_code'][
asset_exchange.loc[df['sid']]
].values
ambigious = {}
def check_intersections(persymbol):
intersections = list(intersecting_ranges(map(
from_tuple,
zip(persymbol.start_date, persymbol.end_date),
)))
if intersections:
data = persymbol[
['start_date', 'end_date']
].astype('datetime64[ns]')
# indent the dataframe string, also compute this early because
# ``persymbol`` is a view and ``astype`` doesn't copy the index
# correctly in pandas 0.22
msg_component = '\n '.join(str(data).splitlines())
ambigious[persymbol.name] = intersections, msg_component
mappings.groupby(['symbol', 'country_code']).apply(check_intersections)
if ambigious:
raise ValueError(
'Ambiguous ownership for %d symbol%s, multiple assets held the'
' following symbols:\n%s' % (
len(ambigious),
'' if len(ambigious) == 1 else 's',
'\n'.join(
'%s (%s):\n intersections: %s\n %s' % (
symbol,
country_code,
tuple(map(_format_range, intersections)),
cs,
)
for (symbol, country_code), (intersections, cs) in sorted(
ambigious.items(),
key=first,
)
),
)
)
def _split_symbol_mappings(df, exchanges):
"""Split out the symbol: sid mappings from the raw data.
Parameters
----------
df : pd.DataFrame
The dataframe with multiple rows for each symbol: sid pair.
exchanges : pd.DataFrame
The exchanges table.
Returns
-------
asset_info : pd.DataFrame
The asset info with one row per asset.
symbol_mappings : pd.DataFrame
The dataframe of just symbol: sid mappings. The index will be
the sid, then there will be three columns: symbol, start_date, and
end_date.
"""
mappings = df[list(mapping_columns)]
with pd.option_context('mode.chained_assignment', None):
mappings['sid'] = mappings.index
mappings.reset_index(drop=True, inplace=True)
# take the most recent sid->exchange mapping based on end date
asset_exchange = df[
['exchange', 'end_date']
].sort_values('end_date').groupby(level=0)['exchange'].nth(-1)
_check_symbol_mappings(mappings, exchanges, asset_exchange)
return (
df.groupby(level=0).apply(_check_asset_group),
mappings,
)
def _dt_to_epoch_ns(dt_series):
"""Convert a timeseries into an Int64Index of nanoseconds since the epoch.
Parameters
----------
dt_series : pd.Series
The timeseries to convert.
Returns
-------
idx : pd.Int64Index
The index converted to nanoseconds since the epoch.
"""
index = pd.to_datetime(dt_series.values)
if index.tzinfo is None:
index = index.tz_localize('UTC')
else:
index = index.tz_convert('UTC')
return index.view(np.int64)
def check_version_info(conn, version_table, expected_version):
"""
Checks for a version value in the version table.
Parameters
----------
conn : sa.Connection
The connection to use to perform the check.
version_table : sa.Table
The version table of the asset database
expected_version : int
The expected version of the asset database
Raises
------
AssetDBVersionError
If the version is in the table and not equal to ASSET_DB_VERSION.
"""
# Read the version out of the table
version_from_table = conn.execute(
sa.select((version_table.c.version,)),
).scalar()
# A db without a version is considered v0
if version_from_table is None:
version_from_table = 0
# Raise an error if the versions do not match
if (version_from_table != expected_version):
raise AssetDBVersionError(db_version=version_from_table,
expected_version=expected_version)
def write_version_info(conn, version_table, version_value):
"""
Inserts the version value in to the version table.
Parameters
----------
conn : sa.Connection
The connection to use to execute the insert.
version_table : sa.Table
The version table of the asset database
version_value : int
The version to write in to the database
"""
conn.execute(sa.insert(version_table, values={'version': version_value}))
class _empty(object):
columns = ()
class AssetDBWriter(object):
"""Class used to write data to an assets db.
Parameters
----------
engine : Engine or str
An SQLAlchemy engine or path to a SQL database.
"""
DEFAULT_CHUNK_SIZE = SQLITE_MAX_VARIABLE_NUMBER
@preprocess(engine=coerce_string_to_eng(require_exists=False))
def __init__(self, engine, asset_finder=None):
self.asset_finder = asset_finder
self.engine = engine
def _real_write(self,
equities,
equity_symbol_mappings,
equity_supplementary_mappings,
futures,
exchanges,
root_symbols,
chunk_size):
with self.engine.connect() as conn:
# Create SQL tables if they do not exist.
self.init_db(conn)
if exchanges is not None:
self._write_df_to_table(
exchanges_table,
exchanges,
conn,
chunk_size,
)
if root_symbols is not None:
self._write_df_to_table(
futures_root_symbols,
root_symbols,
conn,
chunk_size,
)
if equity_supplementary_mappings is not None:
self._write_df_to_table(
equity_supplementary_mappings_table,
equity_supplementary_mappings,
conn,
chunk_size,
)
if futures is not None:
self._write_assets(
'future',
futures,
conn,
chunk_size,
)
if equities is not None:
self._write_assets(
'equity',
equities,
conn,
chunk_size,
mapping_data=equity_symbol_mappings,
)
def write_direct(self,
equities=None,
equity_symbol_mappings=None,
equity_supplementary_mappings=None,
futures=None,
exchanges=None,
root_symbols=None,
chunk_size=DEFAULT_CHUNK_SIZE):
"""Write asset metadata to a sqlite database in the format that it is
stored in the assets db.
Parameters
----------
equities : pd.DataFrame, optional
The equity metadata. The columns for this dataframe are:
symbol : str
The ticker symbol for this equity.
asset_name : str
The full name for this asset.
start_date : datetime
The date when this asset was created.
end_date : datetime, optional
The last date we have trade data for this asset.
first_traded : datetime, optional
The first date we have trade data for this asset.
auto_close_date : datetime, optional
The date on which to close any positions in this asset.
exchange : str
The exchange where this asset is traded.
The index of this dataframe should contain the sids.
futures : pd.DataFrame, optional
The future contract metadata. The columns for this dataframe are:
symbol : str
The ticker symbol for this futures contract.
root_symbol : str
The root symbol, or the symbol with the expiration stripped
out.
asset_name : str
The full name for this asset.
start_date : datetime, optional
The date when this asset was created.
end_date : datetime, optional
The last date we have trade data for this asset.
first_traded : datetime, optional
The first date we have trade data for this asset.
exchange : str
The exchange where this asset is traded.
notice_date : datetime
The date when the owner of the contract may be forced
to take physical delivery of the contract's asset.
expiration_date : datetime
The date when the contract expires.
auto_close_date : datetime
The date when the broker will automatically close any
positions in this contract.
tick_size : float
The minimum price movement of the contract.
multiplier: float
The amount of the underlying asset represented by this
contract.
exchanges : pd.DataFrame, optional
The exchanges where assets can be traded. The columns of this
dataframe are:
exchange : str
The full name of the exchange.
canonical_name : str
The canonical name of the exchange.
country_code : str
The ISO 3166 alpha-2 country code of the exchange.
root_symbols : pd.DataFrame, optional
The root symbols for the futures contracts. The columns for this
dataframe are:
root_symbol : str
The root symbol name.
root_symbol_id : int
The unique id for this root symbol.
sector : string, optional
The sector of this root symbol.
description : string, optional
A short description of this root symbol.
exchange : str
The exchange where this root symbol is traded.
equity_supplementary_mappings : pd.DataFrame, optional
Additional mappings from values of abitrary type to assets.
chunk_size : int, optional
The amount of rows to write to the SQLite table at once.
This defaults to the default number of bind params in sqlite.
If you have compiled sqlite3 with more bind or less params you may
want to pass that value here.
"""
if equities is not None:
equities = _generate_output_dataframe(
equities,
_direct_equities_defaults,
)
if equity_symbol_mappings is None:
raise ValueError(
'equities provided with no symbol mapping data',
)
equity_symbol_mappings = _generate_output_dataframe(
equity_symbol_mappings,
_equity_symbol_mappings_defaults,
)
_check_symbol_mappings(
equity_symbol_mappings,
exchanges,
equities['exchange'],
)
if equity_supplementary_mappings is not None:
equity_supplementary_mappings = _generate_output_dataframe(
equity_supplementary_mappings,
_equity_supplementary_mappings_defaults,
)
if futures is not None:
futures = _generate_output_dataframe(_futures_defaults, futures)
if exchanges is not None:
exchanges = _generate_output_dataframe(
exchanges.set_index('exchange'),
_exchanges_defaults,
)
if root_symbols is not None:
root_symbols = _generate_output_dataframe(
root_symbols,
_root_symbols_defaults,
)
# Set named identifier columns as indices, if provided.
_normalize_index_columns_in_place(
equities=equities,
equity_supplementary_mappings=equity_supplementary_mappings,
futures=futures,
exchanges=exchanges,
root_symbols=root_symbols,
)
self._real_write(
equities=equities,
equity_symbol_mappings=equity_symbol_mappings,
equity_supplementary_mappings=equity_supplementary_mappings,
futures=futures,
exchanges=exchanges,
root_symbols=root_symbols,
chunk_size=chunk_size,
)
def write(self,
equities=None,
futures=None,
exchanges=None,
root_symbols=None,
equity_supplementary_mappings=None,
chunk_size=DEFAULT_CHUNK_SIZE):
"""Write asset metadata to a sqlite database.
Parameters
----------
equities : pd.DataFrame, optional
The equity metadata. The columns for this dataframe are:
symbol : str
The ticker symbol for this equity.
asset_name : str
The full name for this asset.
start_date : datetime
The date when this asset was created.
end_date : datetime, optional
The last date we have trade data for this asset.
first_traded : datetime, optional
The first date we have trade data for this asset.
auto_close_date : datetime, optional
The date on which to close any positions in this asset.
exchange : str
The exchange where this asset is traded.
The index of this dataframe should contain the sids.
futures : pd.DataFrame, optional
The future contract metadata. The columns for this dataframe are:
symbol : str
The ticker symbol for this futures contract.
root_symbol : str
The root symbol, or the symbol with the expiration stripped
out.
asset_name : str
The full name for this asset.
start_date : datetime, optional
The date when this asset was created.
end_date : datetime, optional
The last date we have trade data for this asset.
first_traded : datetime, optional
The first date we have trade data for this asset.
exchange : str
The exchange where this asset is traded.
notice_date : datetime
The date when the owner of the contract may be forced
to take physical delivery of the contract's asset.
expiration_date : datetime
The date when the contract expires.
auto_close_date : datetime
The date when the broker will automatically close any
positions in this contract.
tick_size : float
The minimum price movement of the contract.
multiplier: float
The amount of the underlying asset represented by this
contract.
exchanges : pd.DataFrame, optional
The exchanges where assets can be traded. The columns of this
dataframe are:
exchange : str
The full name of the exchange.
canonical_name : str
The canonical name of the exchange.
country_code : str
The ISO 3166 alpha-2 country code of the exchange.
root_symbols : pd.DataFrame, optional
The root symbols for the futures contracts. The columns for this
dataframe are:
root_symbol : str
The root symbol name.
root_symbol_id : int
The unique id for this root symbol.
sector : string, optional
The sector of this root symbol.
description : string, optional
A short description of this root symbol.
exchange : str
The exchange where this root symbol is traded.
equity_supplementary_mappings : pd.DataFrame, optional
Additional mappings from values of abitrary type to assets.
chunk_size : int, optional
The amount of rows to write to the SQLite table at once.
This defaults to the default number of bind params in sqlite.
If you have compiled sqlite3 with more bind or less params you may
want to pass that value here.
See Also
--------
zipline.assets.asset_finder
"""
if exchanges is None:
exchange_names = [
df['exchange']
for df in (equities, futures, root_symbols)
if df is not None
]
if exchange_names:
exchanges = pd.DataFrame({
'exchange': pd.concat(exchange_names).unique(),
})
data = self._load_data(
equities if equities is not None else pd.DataFrame(),
futures if futures is not None else pd.DataFrame(),
exchanges if exchanges is not None else pd.DataFrame(),
root_symbols if root_symbols is not None else pd.DataFrame(),
(
equity_supplementary_mappings
if equity_supplementary_mappings is not None
else pd.DataFrame()
),
)
self._real_write(
equities=data.equities,
equity_symbol_mappings=data.equities_mappings,
equity_supplementary_mappings=data.equity_supplementary_mappings,
futures=data.futures,
root_symbols=data.root_symbols,
exchanges=data.exchanges,
chunk_size=chunk_size,
)
def _write_df_to_table(self, tbl, df, txn, chunk_size):
df = df.copy()
for column, dtype in df.dtypes.iteritems():
if dtype.kind == 'M':
df[column] = _dt_to_epoch_ns(df[column])
try:
df.to_sql(
tbl.name,
txn.connection,
index=True,
index_label=first(tbl.primary_key.columns).name,
if_exists='append',
chunksize=chunk_size,
)
except:
df.reset_index(inplace=True)
for i, row in df.iterrows():
values = {}
for column in list(df.columns):
# skip raw index, get set by backend
if column == 'index':
continue
values[column] = row[column]
try:
ins = tbl.insert().values(values)
txn.execute(ins)
except IntegrityError:
pkey_column = first(tbl.primary_key.columns)
upd = tbl.update().where(pkey_column == values[pkey_column.name]).values(values)
txn.execute(upd)
# print(f'Skipping duplicate for table {tbl.name}: {values}')
def _write_assets(self,
asset_type,
assets,
txn,
chunk_size,
mapping_data=None):
if asset_type == 'future':
tbl = futures_contracts_table
if mapping_data is not None:
raise TypeError('no mapping data expected for futures')
elif asset_type == 'equity':
tbl = equities_table
if mapping_data is None:
raise TypeError('mapping data required for equities')
else:
raise ValueError(
"asset_type must be in {'future', 'equity'}, got: %s" %
asset_type,
)
self._write_df_to_table(tbl, assets, txn, chunk_size)
# if repeated but we need to write data to equities-table first,
# otherwise we'll fail because of non-matched constraints
if asset_type == 'equity':
# write the symbol mapping data.
self._write_df_to_table(
equity_symbol_mappings,
mapping_data,
txn,
chunk_size,
)
router_df = pd.DataFrame({
asset_router.c.sid.name: assets.index.values,
asset_router.c.asset_type.name: asset_type,
})
self._write_df_to_table(asset_router, router_df, txn, chunk_size)
def _all_tables_present(self, txn):
"""
Checks if any tables are present in the current assets database.
Parameters
----------
txn : Transaction
The open transaction to check in.
Returns
-------
has_tables : bool
True if any tables are present, otherwise False.
"""
conn = txn.connect()
for table_name in asset_db_table_names:
if txn.dialect.has_table(conn, table_name):
return True
return False
def init_db(self, txn=None):
"""Connect to database and create tables.
Parameters
----------
txn : sa.engine.Connection, optional
The transaction to execute in. If this is not provided, a new
transaction will be started with the engine provided.
Returns
-------
metadata : sa.MetaData
The metadata that describes the new assets db.
"""
with ExitStack() as stack:
if txn is None:
txn = stack.enter_context(self.engine.connect())
tables_already_exist = self._all_tables_present(txn)
# Create the SQL tables if they do not already exist.
metadata.create_all(txn, checkfirst=True)
if tables_already_exist:
check_version_info(txn, version_info, ASSET_DB_VERSION)
else:
write_version_info(txn, version_info, ASSET_DB_VERSION)
def _normalize_equities(self, equities, exchanges):
# HACK: If 'company_name' is provided, map it to asset_name
if ('company_name' in equities.columns and
'asset_name' not in equities.columns):
equities['asset_name'] = equities['company_name']
# remap 'file_name' to 'symbol' if provided
if 'file_name' in equities.columns:
equities['symbol'] = equities['file_name']
equities_output = _generate_output_dataframe(
data_subset=equities,
defaults=_equities_defaults,
)
# Split symbols to company_symbols and share_class_symbols
tuple_series = equities_output['symbol'].apply(split_delimited_symbol)
split_symbols = pd.DataFrame(
tuple_series.tolist(),
columns=['company_symbol', 'share_class_symbol'],
index=tuple_series.index
)
equities_output = pd.concat((equities_output, split_symbols), axis=1)
# Upper-case all symbol data
for col in symbol_columns:
equities_output[col] = equities_output[col].str.upper()
# Convert date columns to UNIX Epoch integers (nanoseconds)
for col in ('start_date',
'end_date',
'first_traded',
'auto_close_date'):
equities_output[col] = _dt_to_epoch_ns(equities_output[col])
equities_output.index.rename('sid', inplace=True)
return _split_symbol_mappings(equities_output, exchanges)
def _normalize_futures(self, futures):
futures_output = _generate_output_dataframe(
data_subset=futures,
defaults=_futures_defaults,
)
for col in ('symbol', 'root_symbol'):
futures_output[col] = futures_output[col].str.upper()
for col in ('start_date',
'end_date',
'first_traded',
'notice_date',
'expiration_date',
'auto_close_date'):
futures_output[col] = _dt_to_epoch_ns(futures_output[col])
return futures_output
def _normalize_equity_supplementary_mappings(self, mappings):
mappings_output = _generate_output_dataframe(
data_subset=mappings,
defaults=_equity_supplementary_mappings_defaults,
)
for col in ('start_date', 'end_date'):
mappings_output[col] = _dt_to_epoch_ns(mappings_output[col])
mappings_output.index.rename('sid', inplace=True)
return mappings_output
def _load_data(self,
equities,
futures,
exchanges,
root_symbols,
equity_supplementary_mappings):
"""
Returns a standard set of pandas.DataFrames:
equities, futures, exchanges, root_symbols
"""
# Set named identifier columns as indices, if provided.
_normalize_index_columns_in_place(
equities=equities,
equity_supplementary_mappings=equity_supplementary_mappings,
futures=futures,
exchanges=exchanges,
root_symbols=root_symbols,
)
futures_output = self._normalize_futures(futures)
equity_supplementary_mappings_output = (
self._normalize_equity_supplementary_mappings(
equity_supplementary_mappings,
)
)
exchanges_output = _generate_output_dataframe(
data_subset=exchanges,
defaults=_exchanges_defaults,
)
equities_output, equities_mappings = self._normalize_equities(
equities,
exchanges_output,
)
root_symbols_output = _generate_output_dataframe(
data_subset=root_symbols,
defaults=_root_symbols_defaults,
)
return AssetData(
equities=equities_output,
equities_mappings=equities_mappings,
futures=futures_output,
exchanges=exchanges_output,
root_symbols=root_symbols_output,
equity_supplementary_mappings=equity_supplementary_mappings_output,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/assets/asset_writer.py | asset_writer.py |
from alembic.migration import MigrationContext
from alembic.operations import Operations
import sqlalchemy as sa
from toolz.curried import do, operator
from zipline.assets.asset_writer import write_version_info
from zipline.utils.compat import wraps
from zipline.errors import AssetDBImpossibleDowngrade
from zipline.utils.preprocess import preprocess
from zipline.utils.db_utils import coerce_string_to_eng
def alter_columns(op, name, *columns, **kwargs):
"""Alter columns from a table.
Parameters
----------
name : str
The name of the table.
*columns
The new columns to have.
selection_string : str, optional
The string to use in the selection. If not provided, it will select all
of the new columns from the old table.
Notes
-----
The columns are passed explicitly because this should only be used in a
downgrade where ``zipline.assets.asset_db_schema`` could change.
"""
selection_string = kwargs.pop('selection_string', None)
if kwargs:
raise TypeError(
'alter_columns received extra arguments: %r' % sorted(kwargs),
)
if selection_string is None:
selection_string = ', '.join(column.name for column in columns)
tmp_name = '_alter_columns_' + name
op.rename_table(name, tmp_name)
for column in columns:
# Clear any indices that already exist on this table, otherwise we will
# fail to create the table because the indices will already be present.
# When we create the table below, the indices that we want to preserve
# will just get recreated.
for table in name, tmp_name:
try:
op.drop_index('ix_%s_%s' % (table, column.name))
except sa.exc.OperationalError:
pass
op.create_table(name, *columns)
op.execute(
'insert into %s select %s from %s' % (
name,
selection_string,
tmp_name,
),
)
op.drop_table(tmp_name)
@preprocess(engine=coerce_string_to_eng(require_exists=True))
def downgrade(engine, desired_version):
"""Downgrades the assets db at the given engine to the desired version.
Parameters
----------
engine : Engine
An SQLAlchemy engine to the assets database.
desired_version : int
The desired resulting version for the assets database.
"""
# Check the version of the db at the engine
with engine.begin() as conn:
metadata = sa.MetaData(conn)
metadata.reflect()
version_info_table = metadata.tables['version_info']
starting_version = sa.select((version_info_table.c.version,)).scalar()
# Check for accidental upgrade
if starting_version < desired_version:
raise AssetDBImpossibleDowngrade(db_version=starting_version,
desired_version=desired_version)
# Check if the desired version is already the db version
if starting_version == desired_version:
# No downgrade needed
return
# Create alembic context
ctx = MigrationContext.configure(conn)
op = Operations(ctx)
# Integer keys of downgrades to run
# E.g.: [5, 4, 3, 2] would downgrade v6 to v2
downgrade_keys = range(desired_version, starting_version)[::-1]
# Disable foreign keys until all downgrades are complete
_pragma_foreign_keys(conn, False)
# Execute the downgrades in order
for downgrade_key in downgrade_keys:
_downgrade_methods[downgrade_key](op, conn, version_info_table)
# Re-enable foreign keys
_pragma_foreign_keys(conn, True)
def _pragma_foreign_keys(connection, on):
"""Sets the PRAGMA foreign_keys state of the SQLite database. Disabling
the pragma allows for batch modification of tables with foreign keys.
Parameters
----------
connection : Connection
A SQLAlchemy connection to the db
on : bool
If true, PRAGMA foreign_keys will be set to ON. Otherwise, the PRAGMA
foreign_keys will be set to OFF.
"""
connection.execute("PRAGMA foreign_keys=%s" % ("ON" if on else "OFF"))
# This dict contains references to downgrade methods that can be applied to an
# assets db. The resulting db's version is the key.
# e.g. The method at key '0' is the downgrade method from v1 to v0
_downgrade_methods = {}
def downgrades(src):
"""Decorator for marking that a method is a downgrade to a version to the
previous version.
Parameters
----------
src : int
The version this downgrades from.
Returns
-------
decorator : callable[(callable) -> callable]
The decorator to apply.
"""
def _(f):
destination = src - 1
@do(operator.setitem(_downgrade_methods, destination))
@wraps(f)
def wrapper(op, conn, version_info_table):
conn.execute(version_info_table.delete()) # clear the version
f(op)
write_version_info(conn, version_info_table, destination)
return wrapper
return _
@downgrades(1)
def _downgrade_v1(op):
"""
Downgrade assets db by removing the 'tick_size' column and renaming the
'multiplier' column.
"""
# Drop indices before batch
# This is to prevent index collision when creating the temp table
op.drop_index('ix_futures_contracts_root_symbol')
op.drop_index('ix_futures_contracts_symbol')
# Execute batch op to allow column modification in SQLite
with op.batch_alter_table('futures_contracts') as batch_op:
# Rename 'multiplier'
batch_op.alter_column(column_name='multiplier',
new_column_name='contract_multiplier')
# Delete 'tick_size'
batch_op.drop_column('tick_size')
# Recreate indices after batch
op.create_index('ix_futures_contracts_root_symbol',
table_name='futures_contracts',
columns=['root_symbol'])
op.create_index('ix_futures_contracts_symbol',
table_name='futures_contracts',
columns=['symbol'],
unique=True)
@downgrades(2)
def _downgrade_v2(op):
"""
Downgrade assets db by removing the 'auto_close_date' column.
"""
# Drop indices before batch
# This is to prevent index collision when creating the temp table
op.drop_index('ix_equities_fuzzy_symbol')
op.drop_index('ix_equities_company_symbol')
# Execute batch op to allow column modification in SQLite
with op.batch_alter_table('equities') as batch_op:
batch_op.drop_column('auto_close_date')
# Recreate indices after batch
op.create_index('ix_equities_fuzzy_symbol',
table_name='equities',
columns=['fuzzy_symbol'])
op.create_index('ix_equities_company_symbol',
table_name='equities',
columns=['company_symbol'])
@downgrades(3)
def _downgrade_v3(op):
"""
Downgrade assets db by adding a not null constraint on
``equities.first_traded``
"""
op.create_table(
'_new_equities',
sa.Column(
'sid',
sa.Integer,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('symbol', sa.Text),
sa.Column('company_symbol', sa.Text),
sa.Column('share_class_symbol', sa.Text),
sa.Column('fuzzy_symbol', sa.Text),
sa.Column('asset_name', sa.Text),
sa.Column('start_date', sa.Integer, default=0, nullable=False),
sa.Column('end_date', sa.Integer, nullable=False),
sa.Column('first_traded', sa.Integer, nullable=False),
sa.Column('auto_close_date', sa.Integer),
sa.Column('exchange', sa.Text),
)
op.execute(
"""
insert into _new_equities
select * from equities
where equities.first_traded is not null
""",
)
op.drop_table('equities')
op.rename_table('_new_equities', 'equities')
# we need to make sure the indices have the proper names after the rename
op.create_index(
'ix_equities_company_symbol',
'equities',
['company_symbol'],
)
op.create_index(
'ix_equities_fuzzy_symbol',
'equities',
['fuzzy_symbol'],
)
@downgrades(4)
def _downgrade_v4(op):
"""
Downgrades assets db by copying the `exchange_full` column to `exchange`,
then dropping the `exchange_full` column.
"""
op.drop_index('ix_equities_fuzzy_symbol')
op.drop_index('ix_equities_company_symbol')
op.execute("UPDATE equities SET exchange = exchange_full")
with op.batch_alter_table('equities') as batch_op:
batch_op.drop_column('exchange_full')
op.create_index('ix_equities_fuzzy_symbol',
table_name='equities',
columns=['fuzzy_symbol'])
op.create_index('ix_equities_company_symbol',
table_name='equities',
columns=['company_symbol'])
@downgrades(5)
def _downgrade_v5(op):
op.create_table(
'_new_equities',
sa.Column(
'sid',
sa.Integer,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('symbol', sa.Text),
sa.Column('company_symbol', sa.Text),
sa.Column('share_class_symbol', sa.Text),
sa.Column('fuzzy_symbol', sa.Text),
sa.Column('asset_name', sa.Text),
sa.Column('start_date', sa.Integer, default=0, nullable=False),
sa.Column('end_date', sa.Integer, nullable=False),
sa.Column('first_traded', sa.Integer),
sa.Column('auto_close_date', sa.Integer),
sa.Column('exchange', sa.Text),
sa.Column('exchange_full', sa.Text)
)
op.execute(
"""
insert into _new_equities
select
equities.sid as sid,
sym.symbol as symbol,
sym.company_symbol as company_symbol,
sym.share_class_symbol as share_class_symbol,
sym.company_symbol || sym.share_class_symbol as fuzzy_symbol,
equities.asset_name as asset_name,
equities.start_date as start_date,
equities.end_date as end_date,
equities.first_traded as first_traded,
equities.auto_close_date as auto_close_date,
equities.exchange as exchange,
equities.exchange_full as exchange_full
from
equities
inner join
-- Select the last held symbol for each equity sid from the
-- symbol_mappings table. Selecting max(end_date) causes
-- SQLite to take the other values from the same row that contained
-- the max end_date. See https://www.sqlite.org/lang_select.html#resultset. # noqa
(select
sid, symbol, company_symbol, share_class_symbol, max(end_date)
from
equity_symbol_mappings
group by sid) as 'sym'
on
equities.sid == sym.sid
""",
)
op.drop_table('equity_symbol_mappings')
op.drop_table('equities')
op.rename_table('_new_equities', 'equities')
# we need to make sure the indicies have the proper names after the rename
op.create_index(
'ix_equities_company_symbol',
'equities',
['company_symbol'],
)
op.create_index(
'ix_equities_fuzzy_symbol',
'equities',
['fuzzy_symbol'],
)
@downgrades(6)
def _downgrade_v6(op):
op.drop_table('equity_supplementary_mappings')
@downgrades(7)
def _downgrade_v7(op):
tmp_name = '_new_equities'
op.create_table(
tmp_name,
sa.Column(
'sid',
sa.Integer,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('asset_name', sa.Text),
sa.Column('start_date', sa.Integer, default=0, nullable=False),
sa.Column('end_date', sa.Integer, nullable=False),
sa.Column('first_traded', sa.Integer),
sa.Column('auto_close_date', sa.Integer),
# remove foreign key to exchange
sa.Column('exchange', sa.Text),
# add back exchange full column
sa.Column('exchange_full', sa.Text),
)
op.execute(
"""
insert into
_new_equities
select
eq.sid,
eq.asset_name,
eq.start_date,
eq.end_date,
eq.first_traded,
eq.auto_close_date,
ex.canonical_name,
ex.exchange
from
equities eq
inner join
exchanges ex
on
eq.exchange == ex.exchange
where
ex.country_code in ('US', '??')
""",
)
op.drop_table('equities')
op.rename_table(tmp_name, 'equities')
# rebuild all tables without a foreign key to ``exchanges``
alter_columns(
op,
'futures_root_symbols',
sa.Column(
'root_symbol',
sa.Text,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('root_symbol_id', sa.Integer),
sa.Column('sector', sa.Text),
sa.Column('description', sa.Text),
sa.Column('exchange', sa.Text),
)
alter_columns(
op,
'futures_contracts',
sa.Column(
'sid',
sa.Integer,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('symbol', sa.Text, unique=True, index=True),
sa.Column('root_symbol', sa.Text, index=True),
sa.Column('asset_name', sa.Text),
sa.Column('start_date', sa.Integer, default=0, nullable=False),
sa.Column('end_date', sa.Integer, nullable=False),
sa.Column('first_traded', sa.Integer),
sa.Column('exchange', sa.Text),
sa.Column('notice_date', sa.Integer, nullable=False),
sa.Column('expiration_date', sa.Integer, nullable=False),
sa.Column('auto_close_date', sa.Integer, nullable=False),
sa.Column('multiplier', sa.Float),
sa.Column('tick_size', sa.Float),
)
# drop the ``country_code`` and ``canonical_name`` columns
alter_columns(
op,
'exchanges',
sa.Column(
'exchange',
sa.Text,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('timezone', sa.Text),
# Set the timezone to NULL because we don't know what it was before.
# Nothing in zipline reads the timezone so it doesn't matter.
selection_string="exchange, NULL",
)
op.rename_table('exchanges', 'futures_exchanges')
# add back the foreign keys that previously existed
alter_columns(
op,
'futures_root_symbols',
sa.Column(
'root_symbol',
sa.Text,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('root_symbol_id', sa.Integer),
sa.Column('sector', sa.Text),
sa.Column('description', sa.Text),
sa.Column(
'exchange',
sa.Text,
sa.ForeignKey('futures_exchanges.exchange'),
),
)
alter_columns(
op,
'futures_contracts',
sa.Column(
'sid',
sa.Integer,
unique=True,
nullable=False,
primary_key=True,
),
sa.Column('symbol', sa.Text, unique=True, index=True),
sa.Column(
'root_symbol',
sa.Text,
sa.ForeignKey('futures_root_symbols.root_symbol'),
index=True
),
sa.Column('asset_name', sa.Text),
sa.Column('start_date', sa.Integer, default=0, nullable=False),
sa.Column('end_date', sa.Integer, nullable=False),
sa.Column('first_traded', sa.Integer),
sa.Column(
'exchange',
sa.Text,
sa.ForeignKey('futures_exchanges.exchange'),
),
sa.Column('notice_date', sa.Integer, nullable=False),
sa.Column('expiration_date', sa.Integer, nullable=False),
sa.Column('auto_close_date', sa.Integer, nullable=False),
sa.Column('multiplier', sa.Float),
sa.Column('tick_size', sa.Float),
)
# Delete equity_symbol_mappings records that no longer refer to valid sids.
op.execute(
"""
DELETE FROM
equity_symbol_mappings
WHERE
sid NOT IN (SELECT sid FROM equities);
"""
)
# Delete asset_router records that no longer refer to valid sids.
op.execute(
"""
DELETE FROM
asset_router
WHERE
sid
NOT IN (
SELECT sid FROM equities
UNION
SELECT sid FROM futures_contracts
);
"""
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/assets/asset_db_migrations.py | asset_db_migrations.py |
from abc import ABCMeta, abstractmethod
from six import with_metaclass
# Number of days over which to compute rolls when finding the current contract
# for a volume-rolling contract chain. For more details on why this is needed,
# see `VolumeRollFinder.get_contract_center`.
ROLL_DAYS_FOR_CURRENT_CONTRACT = 90
class RollFinder(with_metaclass(ABCMeta, object)):
"""
Abstract base class for calculating when futures contracts are the active
contract.
"""
@abstractmethod
def _active_contract(self, oc, front, back, dt):
raise NotImplementedError
def _get_active_contract_at_offset(self, root_symbol, dt, offset):
"""
For the given root symbol, find the contract that is considered active
on a specific date at a specific offset.
"""
oc = self.asset_finder.get_ordered_contracts(root_symbol)
session = self.trading_calendar.minute_to_session_label(dt)
front = oc.contract_before_auto_close(session.value)
back = oc.contract_at_offset(front, 1, dt.value)
if back is None:
return front
primary = self._active_contract(oc, front, back, session)
return oc.contract_at_offset(primary, offset, session.value)
def get_contract_center(self, root_symbol, dt, offset):
"""
Parameters
----------
root_symbol : str
The root symbol for the contract chain.
dt : Timestamp
The datetime for which to retrieve the current contract.
offset : int
The offset from the primary contract.
0 is the primary, 1 is the secondary, etc.
Returns
-------
Future
The active future contract at the given dt.
"""
return self._get_active_contract_at_offset(root_symbol, dt, offset)
def get_rolls(self, root_symbol, start, end, offset):
"""
Get the rolls, i.e. the session at which to hop from contract to
contract in the chain.
Parameters
----------
root_symbol : str
The root symbol for which to calculate rolls.
start : Timestamp
Start of the date range.
end : Timestamp
End of the date range.
offset : int
Offset from the primary.
Returns
-------
rolls - list[tuple(sid, roll_date)]
A list of rolls, where first value is the first active `sid`,
and the `roll_date` on which to hop to the next contract.
The last pair in the chain has a value of `None` since the roll
is after the range.
"""
oc = self.asset_finder.get_ordered_contracts(root_symbol)
front = self._get_active_contract_at_offset(root_symbol, end, 0)
back = oc.contract_at_offset(front, 1, end.value)
if back is not None:
end_session = self.trading_calendar.minute_to_session_label(end)
first = self._active_contract(oc, front, back, end_session)
else:
first = front
first_contract = oc.sid_to_contract[first]
rolls = [((first_contract >> offset).contract.sid, None)]
tc = self.trading_calendar
sessions = tc.sessions_in_range(tc.minute_to_session_label(start),
tc.minute_to_session_label(end))
freq = sessions.freq
if first == front:
# This is a bit tricky to grasp. Once we have the active contract
# on the given end date, we want to start walking backwards towards
# the start date and checking for rolls. For this, we treat the
# previous month's contract as the 'first' contract, and the
# contract we just found to be active as the 'back'. As we walk
# towards the start date, if the 'back' is no longer active, we add
# that date as a roll.
curr = first_contract << 1
else:
curr = first_contract << 2
session = sessions[-1]
while session > start and curr is not None:
front = curr.contract.sid
back = rolls[0][0]
prev_c = curr.prev
while session > start:
prev = session - freq
if prev_c is not None:
if prev < prev_c.contract.auto_close_date:
break
if back != self._active_contract(oc, front, back, prev):
# TODO: Instead of listing each contract with its roll date
# as tuples, create a series which maps every day to the
# active contract on that day.
rolls.insert(0, ((curr >> offset).contract.sid, session))
break
session = prev
curr = curr.prev
if curr is not None:
session = min(session, curr.contract.auto_close_date + freq)
return rolls
class CalendarRollFinder(RollFinder):
"""
The CalendarRollFinder calculates contract rolls based purely on the
contract's auto close date.
"""
def __init__(self, trading_calendar, asset_finder):
self.trading_calendar = trading_calendar
self.asset_finder = asset_finder
def _active_contract(self, oc, front, back, dt):
contract = oc.sid_to_contract[front].contract
auto_close_date = contract.auto_close_date
auto_closed = dt >= auto_close_date
return back if auto_closed else front
class VolumeRollFinder(RollFinder):
"""
The VolumeRollFinder calculates contract rolls based on when
volume activity transfers from one contract to another.
"""
GRACE_DAYS = 7
def __init__(self, trading_calendar, asset_finder, session_reader):
self.trading_calendar = trading_calendar
self.asset_finder = asset_finder
self.session_reader = session_reader
def _active_contract(self, oc, front, back, dt):
r"""
Return the active contract based on the previous trading day's volume.
In the rare case that a double volume switch occurs we treat the first
switch as the roll. Take the following case for example:
| +++++ _____
| + __ / <--- 'G'
| ++/++\++++/++
| _/ \__/ +
| / +
| ____/ + <--- 'F'
|_________|__|___|________
a b c <--- Switches
We should treat 'a' as the roll date rather than 'c' because from the
perspective of 'a', if a switch happens and we are pretty close to the
auto-close date, we would probably assume it is time to roll. This
means that for every date after 'a', `data.current(cf, 'contract')`
should return the 'G' contract.
"""
front_contract = oc.sid_to_contract[front].contract
back_contract = oc.sid_to_contract[back].contract
tc = self.trading_calendar
trading_day = tc.day
prev = dt - trading_day
get_value = self.session_reader.get_value
# If the front contract is past its auto close date it cannot be the
# active contract, so return the back contract. Similarly, if the back
# contract has not even started yet, just return the front contract.
# The reason for using 'prev' to see if the contracts are alive instead
# of using 'dt' is because we need to get each contract's volume on the
# previous day, so we need to make sure that each contract exists on
# 'prev' in order to call 'get_value' below.
if dt > min(front_contract.auto_close_date, front_contract.end_date):
return back
elif front_contract.start_date > prev:
return back
elif dt > min(back_contract.auto_close_date, back_contract.end_date):
return front
elif back_contract.start_date > prev:
return front
front_vol = get_value(front, prev, 'volume')
back_vol = get_value(back, prev, 'volume')
if back_vol > front_vol:
return back
gap_start = max(
back_contract.start_date,
front_contract.auto_close_date - (trading_day * self.GRACE_DAYS),
)
gap_end = prev - trading_day
if dt < gap_start:
return front
# If we are within `self.GRACE_DAYS` of the front contract's auto close
# date, and a volume flip happened during that period, return the back
# contract as the active one.
sessions = tc.sessions_in_range(
tc.minute_to_session_label(gap_start),
tc.minute_to_session_label(gap_end),
)
for session in sessions:
front_vol = get_value(front, session, 'volume')
back_vol = get_value(back, session, 'volume')
if back_vol > front_vol:
return back
return front
def get_contract_center(self, root_symbol, dt, offset):
"""
Parameters
----------
root_symbol : str
The root symbol for the contract chain.
dt : Timestamp
The datetime for which to retrieve the current contract.
offset : int
The offset from the primary contract.
0 is the primary, 1 is the secondary, etc.
Returns
-------
Future
The active future contract at the given dt.
"""
# When determining the center contract on a specific day using volume
# rolls, simply picking the contract with the highest volume could
# cause flip-flopping between active contracts each day if the front
# and back contracts are close in volume. Therefore, information about
# the surrounding rolls is required. The `get_rolls` logic prevents
# contracts from being considered active once they have rolled, so
# incorporating that logic here prevents flip-flopping.
day = self.trading_calendar.day
end_date = min(
dt + (ROLL_DAYS_FOR_CURRENT_CONTRACT * day),
self.session_reader.last_available_dt,
)
rolls = self.get_rolls(
root_symbol=root_symbol, start=dt, end=end_date, offset=offset,
)
sid, acd = rolls[0]
return self.asset_finder.retrieve_asset(sid) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/assets/roll_finder.py | roll_finder.py |
import sys
import logbook
import numpy as np
from zipline.finance import commission, slippage
zipline_logging = logbook.NestedSetup([
logbook.NullHandler(),
logbook.StreamHandler(sys.stdout, level=logbook.INFO),
logbook.StreamHandler(sys.stderr, level=logbook.ERROR),
])
zipline_logging.push_application()
STOCKS = ['AMD', 'CERN', 'COST', 'DELL', 'GPS', 'INTC', 'MMM']
# On-Line Portfolio Moving Average Reversion
# More info can be found in the corresponding paper:
# http://icml.cc/2012/papers/168.pdf
def initialize(algo, eps=1, window_length=5):
algo.stocks = STOCKS
algo.sids = [algo.symbol(symbol) for symbol in algo.stocks]
algo.m = len(algo.stocks)
algo.price = {}
algo.b_t = np.ones(algo.m) / algo.m
algo.last_desired_port = np.ones(algo.m) / algo.m
algo.eps = eps
algo.init = True
algo.days = 0
algo.window_length = window_length
algo.set_commission(commission.PerShare(cost=0, min_trade_cost=1.0))
algo.set_slippage(slippage.VolumeShareSlippage())
def handle_data(algo, data):
algo.days += 1
if algo.days < algo.window_length:
return
if algo.init:
rebalance_portfolio(algo, data, algo.b_t)
algo.init = False
return
m = algo.m
x_tilde = np.zeros(m)
# find relative moving average price for each asset
mavgs = data.history(algo.sids, 'price', algo.window_length, '1d').mean()
for i, sid in enumerate(algo.sids):
price = data.current(sid, "price")
# Relative mean deviation
x_tilde[i] = mavgs[sid] / price
###########################
# Inside of OLMAR (algo 2)
x_bar = x_tilde.mean()
# market relative deviation
mark_rel_dev = x_tilde - x_bar
# Expected return with current portfolio
exp_return = np.dot(algo.b_t, x_tilde)
weight = algo.eps - exp_return
variability = (np.linalg.norm(mark_rel_dev)) ** 2
# test for divide-by-zero case
if variability == 0.0:
step_size = 0
else:
step_size = max(0, weight / variability)
b = algo.b_t + step_size * mark_rel_dev
b_norm = simplex_projection(b)
np.testing.assert_almost_equal(b_norm.sum(), 1)
rebalance_portfolio(algo, data, b_norm)
# update portfolio
algo.b_t = b_norm
def rebalance_portfolio(algo, data, desired_port):
# rebalance portfolio
desired_amount = np.zeros_like(desired_port)
current_amount = np.zeros_like(desired_port)
prices = np.zeros_like(desired_port)
if algo.init:
positions_value = algo.portfolio.starting_cash
else:
positions_value = algo.portfolio.positions_value + \
algo.portfolio.cash
for i, sid in enumerate(algo.sids):
current_amount[i] = algo.portfolio.positions[sid].amount
prices[i] = data.current(sid, "price")
desired_amount = np.round(desired_port * positions_value / prices)
algo.last_desired_port = desired_port
diff_amount = desired_amount - current_amount
for i, sid in enumerate(algo.sids):
algo.order(sid, diff_amount[i])
def simplex_projection(v, b=1):
r"""Projection vectors to the simplex domain
Implemented according to the paper: Efficient projections onto the
l1-ball for learning in high dimensions, John Duchi, et al. ICML 2008.
Implementation Time: 2011 June 17 by Bin@libin AT pmail.ntu.edu.sg
Optimization Problem: min_{w}\| w - v \|_{2}^{2}
s.t. sum_{i=1}^{m}=z, w_{i}\geq 0
Input: A vector v \in R^{m}, and a scalar z > 0 (default=1)
Output: Projection vector w
:Example:
>>> proj = simplex_projection([.4 ,.3, -.4, .5])
>>> proj # doctest: +NORMALIZE_WHITESPACE
array([ 0.33333333, 0.23333333, 0. , 0.43333333])
>>> print(proj.sum())
1.0
Original matlab implementation: John Duchi ([email protected])
Python-port: Copyright 2013 by Thomas Wiecki ([email protected]).
"""
v = np.asarray(v)
p = len(v)
# Sort v into u in descending order
v = (v > 0) * v
u = np.sort(v)[::-1]
sv = np.cumsum(u)
rho = np.where(u > (sv - b) / np.arange(1, p + 1))[0][-1]
theta = np.max([0, (sv[rho] - b) / (rho + 1)])
w = (v - theta)
w[w < 0] = 0
return w
# Note: this function can be removed if running
# this algorithm on quantopian.com
def analyze(context=None, results=None):
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
results.portfolio_value.plot(ax=ax)
ax.set_ylabel('Portfolio value (USD)')
plt.show()
def _test_args():
"""Extra arguments to use when zipline's automated tests run this example.
"""
import pandas as pd
return {
'start': pd.Timestamp('2004', tz='utc'),
'end': pd.Timestamp('2008', tz='utc'),
} | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/examples/olmar.py | olmar.py |
from zipline.api import order, record, symbol
from zipline.finance import commission, slippage
# Import exponential moving average from talib wrapper
try:
from talib import EMA
except ImportError:
msg = "Unable to import module TA-lib. Use `pip install TA-lib` to "\
"install. Note: if installation fails, you might need to install "\
"the underlying TA-lib library (more information can be found in "\
"the zipline installation documentation)."
raise ImportError(msg)
def initialize(context):
context.asset = symbol('AAPL')
# To keep track of whether we invested in the stock or not
context.invested = False
# Explicitly set the commission/slippage to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
context.set_slippage(slippage.VolumeShareSlippage())
def handle_data(context, data):
trailing_window = data.history(context.asset, 'price', 40, '1d')
if trailing_window.isnull().values.any():
return
short_ema = EMA(trailing_window.values, timeperiod=20)
long_ema = EMA(trailing_window.values, timeperiod=40)
buy = False
sell = False
if (short_ema[-1] > long_ema[-1]) and not context.invested:
order(context.asset, 100)
context.invested = True
buy = True
elif (short_ema[-1] < long_ema[-1]) and context.invested:
order(context.asset, -100)
context.invested = False
sell = True
record(AAPL=data.current(context.asset, "price"),
short_ema=short_ema[-1],
long_ema=long_ema[-1],
buy=buy,
sell=sell)
# Note: this function can be removed if running
# this algorithm on quantopian.com
def analyze(context=None, results=None):
import matplotlib.pyplot as plt
import logbook
logbook.StderrHandler().push_application()
log = logbook.Logger('Algorithm')
fig = plt.figure()
ax1 = fig.add_subplot(211)
results.portfolio_value.plot(ax=ax1)
ax1.set_ylabel('Portfolio value (USD)')
ax2 = fig.add_subplot(212)
ax2.set_ylabel('Price (USD)')
# If data has been record()ed, then plot it.
# Otherwise, log the fact that no data has been recorded.
if 'AAPL' in results and 'short_ema' in results and 'long_ema' in results:
results[['AAPL', 'short_ema', 'long_ema']].plot(ax=ax2)
ax2.plot(
results.index[results.buy],
results.loc[results.buy, 'long_ema'],
'^',
markersize=10,
color='m',
)
ax2.plot(
results.index[results.sell],
results.loc[results.sell, 'short_ema'],
'v',
markersize=10,
color='k',
)
plt.legend(loc=0)
plt.gcf().set_size_inches(18, 8)
else:
msg = 'AAPL, short_ema and long_ema data not captured using record().'
ax2.annotate(msg, xy=(0.1, 0.5))
log.info(msg)
plt.show()
def _test_args():
"""Extra arguments to use when zipline's automated tests run this example.
"""
import pandas as pd
return {
'start': pd.Timestamp('2014-01-01', tz='utc'),
'end': pd.Timestamp('2014-11-01', tz='utc'),
} | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/examples/dual_ema_talib.py | dual_ema_talib.py |
from six import viewkeys
from zipline.api import (
attach_pipeline,
date_rules,
order_target_percent,
pipeline_output,
record,
schedule_function,
)
from zipline.finance import commission, slippage
from zipline.pipeline import Pipeline
from zipline.pipeline.factors import RSI
def make_pipeline():
rsi = RSI()
return Pipeline(
columns={
'longs': rsi.top(3),
'shorts': rsi.bottom(3),
},
)
def rebalance(context, data):
# Pipeline data will be a dataframe with boolean columns named 'longs' and
# 'shorts'.
pipeline_data = context.pipeline_data
all_assets = pipeline_data.index
longs = all_assets[pipeline_data.longs]
shorts = all_assets[pipeline_data.shorts]
record(universe_size=len(all_assets))
# Build a 2x-leveraged, equal-weight, long-short portfolio.
one_third = 1.0 / 3.0
for asset in longs:
order_target_percent(asset, one_third)
for asset in shorts:
order_target_percent(asset, -one_third)
# Remove any assets that should no longer be in our portfolio.
portfolio_assets = longs | shorts
positions = context.portfolio.positions
for asset in viewkeys(positions) - set(portfolio_assets):
# This will fail if the asset was removed from our portfolio because it
# was delisted.
if data.can_trade(asset):
order_target_percent(asset, 0)
def initialize(context):
attach_pipeline(make_pipeline(), 'my_pipeline')
# Rebalance each day. In daily mode, this is equivalent to putting
# `rebalance` in our handle_data, but in minute mode, it's equivalent to
# running at the start of the day each day.
schedule_function(rebalance, date_rules.every_day())
# Explicitly set the commission/slippage to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
context.set_slippage(slippage.VolumeShareSlippage())
def before_trading_start(context, data):
context.pipeline_data = pipeline_output('my_pipeline')
def _test_args():
"""
Extra arguments to use when zipline's automated tests run this example.
Notes for testers:
Gross leverage should be roughly 2.0 on every day except the first.
Net leverage should be roughly 2.0 on every day except the first.
Longs Count should always be 3 after the first day.
Shorts Count should be 3 after the first day, except on 2013-10-30, when it
dips to 2 for a day because DELL is delisted.
"""
import pandas as pd
return {
# We run through october of 2013 because DELL is in the test data and
# it went private on 2013-10-29.
'start': pd.Timestamp('2013-10-07', tz='utc'),
'end': pd.Timestamp('2013-11-30', tz='utc'),
'capital_base': 100000,
} | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/examples/momentum_pipeline.py | momentum_pipeline.py |
from importlib import import_module
import os
from toolz import merge
from trading_calendars import register_calendar, get_calendar
from zipline import run_algorithm
# These are used by test_examples.py to discover the examples to run.
def load_example_modules():
example_modules = {}
for f in os.listdir(os.path.dirname(__file__)):
if not f.endswith('.py') or f == '__init__.py':
continue
modname = f[:-len('.py')]
mod = import_module('.' + modname, package=__name__)
example_modules[modname] = mod
globals()[modname] = mod
# Remove noise from loop variables.
del f, modname, mod
return example_modules
# Columns that we expect to be able to reliably deterministic
# Doesn't include fields that have UUIDS.
_cols_to_check = [
'algo_volatility',
'algorithm_period_return',
'alpha',
'benchmark_period_return',
'benchmark_volatility',
'beta',
'capital_used',
'ending_cash',
'ending_exposure',
'ending_value',
'excess_return',
'gross_leverage',
'long_exposure',
'long_value',
'longs_count',
'max_drawdown',
'max_leverage',
'net_leverage',
'period_close',
'period_label',
'period_open',
'pnl',
'portfolio_value',
'positions',
'returns',
'short_exposure',
'short_value',
'shorts_count',
'sortino',
'starting_cash',
'starting_exposure',
'starting_value',
'trading_days',
'treasury_period_return',
]
def run_example(example_modules, example_name, environ,
benchmark_returns=None):
"""
Run an example module from zipline.examples.
"""
mod = example_modules[example_name]
register_calendar("YAHOO", get_calendar("NYSE"), force=True)
return run_algorithm(
initialize=getattr(mod, 'initialize', None),
handle_data=getattr(mod, 'handle_data', None),
before_trading_start=getattr(mod, 'before_trading_start', None),
analyze=getattr(mod, 'analyze', None),
bundle='test',
environ=environ,
benchmark_returns=benchmark_returns,
# Provide a default capital base, but allow the test to override.
**merge({'capital_base': 1e7}, mod._test_args())
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/examples/__init__.py | __init__.py |
from zipline.api import order_target, record, symbol
from zipline.finance import commission, slippage
def initialize(context):
context.sym = symbol('AAPL')
context.i = 0
# Explicitly set the commission/slippage to the "old" value until we can
# rebuild example data.
# github.com/quantopian/zipline/blob/master/tests/resources/
# rebuild_example_data#L105
context.set_commission(commission.PerShare(cost=.0075, min_trade_cost=1.0))
context.set_slippage(slippage.VolumeShareSlippage())
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.sym, 'price', 100, '1d').mean()
long_mavg = data.history(context.sym, 'price', 300, '1d').mean()
# Trading logic
if short_mavg > long_mavg:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.sym, 100)
elif short_mavg < long_mavg:
order_target(context.sym, 0)
# Save values for later inspection
record(AAPL=data.current(context.sym, "price"),
short_mavg=short_mavg,
long_mavg=long_mavg)
# Note: this function can be removed if running
# this algorithm on quantopian.com
def analyze(context=None, results=None):
import matplotlib.pyplot as plt
import logbook
logbook.StderrHandler().push_application()
log = logbook.Logger('Algorithm')
fig = plt.figure()
ax1 = fig.add_subplot(211)
results.portfolio_value.plot(ax=ax1)
ax1.set_ylabel('Portfolio value (USD)')
ax2 = fig.add_subplot(212)
ax2.set_ylabel('Price (USD)')
# If data has been record()ed, then plot it.
# Otherwise, log the fact that no data has been recorded.
if ('AAPL' in results and 'short_mavg' in results and
'long_mavg' in results):
results['AAPL'].plot(ax=ax2)
results[['short_mavg', 'long_mavg']].plot(ax=ax2)
trans = results.loc[[t != [] for t in results.transactions]]
buys = trans.loc[[t[0]['amount'] > 0 for t in
trans.transactions]]
sells = trans.loc[
[t[0]['amount'] < 0 for t in trans.transactions]]
ax2.plot(buys.index, results.short_mavg.loc[buys.index],
'^', markersize=10, color='m')
ax2.plot(sells.index, results.short_mavg.loc[sells.index],
'v', markersize=10, color='k')
plt.legend(loc=0)
else:
msg = 'AAPL, short_mavg & long_mavg data not captured using record().'
ax2.annotate(msg, xy=(0.1, 0.5))
log.info(msg)
plt.show()
def _test_args():
"""Extra arguments to use when zipline's automated tests run this example.
"""
import pandas as pd
return {
'start': pd.Timestamp('2011', tz='utc'),
'end': pd.Timestamp('2013', tz='utc'),
} | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/examples/dual_moving_average.py | dual_moving_average.py |
import pandas as pd
from zipline.errors import (
InvalidBenchmarkAsset,
BenchmarkAssetNotAvailableTooEarly,
BenchmarkAssetNotAvailableTooLate
)
class BenchmarkSource(object):
def __init__(self,
benchmark_asset,
trading_calendar,
sessions,
data_portal,
emission_rate="daily",
benchmark_returns=None):
self.benchmark_asset = benchmark_asset
self.sessions = sessions
self.emission_rate = emission_rate
self.data_portal = data_portal
if len(sessions) == 0:
self._precalculated_series = pd.Series()
elif benchmark_asset is not None:
self._validate_benchmark(benchmark_asset)
(self._precalculated_series,
self._daily_returns) = self._initialize_precalculated_series(
benchmark_asset,
trading_calendar,
sessions,
data_portal
)
elif benchmark_returns is not None:
self._daily_returns = daily_series = benchmark_returns.reindex(
sessions,
).fillna(0)
if self.emission_rate == "minute":
# we need to take the env's benchmark returns, which are daily,
# and resample them to minute
minutes = trading_calendar.minutes_for_sessions_in_range(
sessions[0],
sessions[-1]
)
minute_series = daily_series.reindex(
index=minutes,
method="ffill"
)
self._precalculated_series = minute_series
else:
self._precalculated_series = daily_series
else:
raise Exception("Must provide either benchmark_asset or "
"benchmark_returns.")
def get_value(self, dt):
"""Look up the returns for a given dt.
Parameters
----------
dt : datetime
The label to look up.
Returns
-------
returns : float
The returns at the given dt or session.
See Also
--------
:class:`zipline.sources.benchmark_source.BenchmarkSource.daily_returns`
.. warning::
This method expects minute inputs if ``emission_rate == 'minute'``
and session labels when ``emission_rate == 'daily``.
"""
return self._precalculated_series.loc[dt]
def get_range(self, start_dt, end_dt):
"""Look up the returns for a given period.
Parameters
----------
start_dt : datetime
The inclusive start label.
end_dt : datetime
The inclusive end label.
Returns
-------
returns : pd.Series
The series of returns.
See Also
--------
:class:`zipline.sources.benchmark_source.BenchmarkSource.daily_returns`
.. warning::
This method expects minute inputs if ``emission_rate == 'minute'``
and session labels when ``emission_rate == 'daily``.
"""
return self._precalculated_series.loc[start_dt:end_dt]
def daily_returns(self, start, end=None):
"""Returns the daily returns for the given period.
Parameters
----------
start : datetime
The inclusive starting session label.
end : datetime, optional
The inclusive ending session label. If not provided, treat
``start`` as a scalar key.
Returns
-------
returns : pd.Series or float
The returns in the given period. The index will be the trading
calendar in the range [start, end]. If just ``start`` is provided,
return the scalar value on that day.
"""
if end is None:
return self._daily_returns[start]
return self._daily_returns[start:end]
def _validate_benchmark(self, benchmark_asset):
# check if this security has a stock dividend. if so, raise an
# error suggesting that the user pick a different asset to use
# as benchmark.
stock_dividends = \
self.data_portal.get_stock_dividends(self.benchmark_asset,
self.sessions)
if len(stock_dividends) > 0:
raise InvalidBenchmarkAsset(
sid=str(self.benchmark_asset),
dt=stock_dividends[0]["ex_date"]
)
if benchmark_asset.start_date > self.sessions[0]:
# the asset started trading after the first simulation day
raise BenchmarkAssetNotAvailableTooEarly(
sid=str(self.benchmark_asset),
dt=self.sessions[0],
start_dt=benchmark_asset.start_date
)
if benchmark_asset.end_date < self.sessions[-1]:
# the asset stopped trading before the last simulation day
raise BenchmarkAssetNotAvailableTooLate(
sid=str(self.benchmark_asset),
dt=self.sessions[-1],
end_dt=benchmark_asset.end_date
)
@staticmethod
def _compute_daily_returns(g):
return (g[-1] - g[0]) / g[0]
@classmethod
def downsample_minute_return_series(cls,
trading_calendar,
minutely_returns):
sessions = trading_calendar.minute_index_to_session_labels(
minutely_returns.index,
)
closes = trading_calendar.session_closes_in_range(
sessions[0],
sessions[-1],
)
daily_returns = minutely_returns[closes].pct_change()
daily_returns.index = closes.index
return daily_returns.iloc[1:]
def _initialize_precalculated_series(self,
asset,
trading_calendar,
trading_days,
data_portal):
"""
Internal method that pre-calculates the benchmark return series for
use in the simulation.
Parameters
----------
asset: Asset to use
trading_calendar: TradingCalendar
trading_days: pd.DateTimeIndex
data_portal: DataPortal
Notes
-----
If the benchmark asset started trading after the simulation start,
or finished trading before the simulation end, exceptions are raised.
If the benchmark asset started trading the same day as the simulation
start, the first available minute price on that day is used instead
of the previous close.
We use history to get an adjusted price history for each day's close,
as of the look-back date (the last day of the simulation). Prices are
fully adjusted for dividends, splits, and mergers.
Returns
-------
returns : pd.Series
indexed by trading day, whose values represent the %
change from close to close.
daily_returns : pd.Series
the partial daily returns for each minute
"""
if self.emission_rate == "minute":
minutes = trading_calendar.minutes_for_sessions_in_range(
self.sessions[0], self.sessions[-1]
)
benchmark_series = data_portal.get_history_window(
[asset],
minutes[-1],
bar_count=len(minutes) + 1,
frequency="1m",
field="price",
data_frequency=self.emission_rate,
ffill=True
)[asset]
return (
benchmark_series.pct_change()[1:],
self.downsample_minute_return_series(
trading_calendar,
benchmark_series,
),
)
start_date = asset.start_date
if start_date < trading_days[0]:
# get the window of close prices for benchmark_asset from the
# last trading day of the simulation, going up to one day
# before the simulation start day (so that we can get the %
# change on day 1)
benchmark_series = data_portal.get_history_window(
[asset],
trading_days[-1],
bar_count=len(trading_days) + 1,
frequency="1d",
field="price",
data_frequency=self.emission_rate,
ffill=True
)[asset]
returns = benchmark_series.pct_change()[1:]
return returns, returns
elif start_date == trading_days[0]:
# Attempt to handle case where stock data starts on first
# day, in this case use the open to close return.
benchmark_series = data_portal.get_history_window(
[asset],
trading_days[-1],
bar_count=len(trading_days),
frequency="1d",
field="price",
data_frequency=self.emission_rate,
ffill=True
)[asset]
# get a minute history window of the first day
first_open = data_portal.get_spot_value(
asset,
'open',
trading_days[0],
'daily',
)
first_close = data_portal.get_spot_value(
asset,
'close',
trading_days[0],
'daily',
)
first_day_return = (first_close - first_open) / first_open
returns = benchmark_series.pct_change()[:]
returns[0] = first_day_return
return returns, returns
else:
raise ValueError(
'cannot set benchmark to asset that does not exist during'
' the simulation period (asset start date=%r)' % start_date
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/sources/benchmark_source.py | benchmark_source.py |
from abc import ABCMeta, abstractmethod
from collections import namedtuple
import hashlib
from textwrap import dedent
import warnings
from logbook import Logger
import numpy
import pandas as pd
from pandas import read_csv
import pytz
import requests
from six import StringIO, iteritems, with_metaclass
from zipline.errors import (
MultipleSymbolsFound,
SymbolNotFound,
ZiplineError
)
from zipline.protocol import (
DATASOURCE_TYPE,
Event
)
from zipline.assets import Equity
logger = Logger('Requests Source Logger')
def roll_dts_to_midnight(dts, trading_day):
if len(dts) == 0:
return dts
return pd.DatetimeIndex(
(dts.tz_convert('US/Eastern') - pd.Timedelta(hours=16)).date,
tz='UTC',
) + trading_day
class FetcherEvent(Event):
pass
class FetcherCSVRedirectError(ZiplineError):
msg = dedent(
"""\
Attempt to fetch_csv from a redirected url. {url}
must be changed to {new_url}
"""
)
def __init__(self, *args, **kwargs):
self.url = kwargs["url"]
self.new_url = kwargs["new_url"]
self.extra = kwargs["extra"]
super(FetcherCSVRedirectError, self).__init__(*args, **kwargs)
# The following optional arguments are supported for
# requests backed data sources.
# see https://requests.readthedocs.io/en/latest/api/#main-interface
# for a full list.
ALLOWED_REQUESTS_KWARGS = {
'params',
'headers',
'auth',
'cert'
}
# The following optional arguments are supported for pandas' read_csv
# function, and may be passed as kwargs to the datasource below.
# see https://pandas.pydata.org/
# pandas-docs/stable/generated/pandas.io.parsers.read_csv.html
ALLOWED_READ_CSV_KWARGS = {
'sep',
'dialect',
'doublequote',
'escapechar',
'quotechar',
'quoting',
'skipinitialspace',
'lineterminator',
'header',
'index_col',
'names',
'prefix',
'skiprows',
'skipfooter',
'skip_footer',
'na_values',
'true_values',
'false_values',
'delimiter',
'converters',
'dtype',
'delim_whitespace',
'as_recarray',
'na_filter',
'compact_ints',
'use_unsigned',
'buffer_lines',
'warn_bad_lines',
'error_bad_lines',
'keep_default_na',
'thousands',
'comment',
'decimal',
'keep_date_col',
'nrows',
'chunksize',
'encoding',
'usecols'
}
SHARED_REQUESTS_KWARGS = {
'stream': True,
'allow_redirects': False,
}
def mask_requests_args(url, validating=False, params_checker=None, **kwargs):
requests_kwargs = {key: val for (key, val) in iteritems(kwargs)
if key in ALLOWED_REQUESTS_KWARGS}
if params_checker is not None:
url, s_params = params_checker(url)
if s_params:
if 'params' in requests_kwargs:
requests_kwargs['params'].update(s_params)
else:
requests_kwargs['params'] = s_params
# Giving the connection 30 seconds. This timeout does not
# apply to the download of the response body.
# (Note that Quandl links can take >10 seconds to return their
# first byte on occasion)
requests_kwargs['timeout'] = 1.0 if validating else 30.0
requests_kwargs.update(SHARED_REQUESTS_KWARGS)
request_pair = namedtuple("RequestPair", ("requests_kwargs", "url"))
return request_pair(requests_kwargs, url)
class PandasCSV(with_metaclass(ABCMeta, object)):
def __init__(self,
pre_func,
post_func,
asset_finder,
trading_day,
start_date,
end_date,
date_column,
date_format,
timezone,
symbol,
mask,
symbol_column,
data_frequency,
country_code,
**kwargs):
self.start_date = start_date
self.end_date = end_date
self.date_column = date_column
self.date_format = date_format
self.timezone = timezone
self.mask = mask
self.symbol_column = symbol_column or "symbol"
self.data_frequency = data_frequency
self.country_code = country_code
invalid_kwargs = set(kwargs) - ALLOWED_READ_CSV_KWARGS
if invalid_kwargs:
raise TypeError(
"Unexpected keyword arguments: %s" % invalid_kwargs,
)
self.pandas_kwargs = self.mask_pandas_args(kwargs)
self.symbol = symbol
self.finder = asset_finder
self.trading_day = trading_day
self.pre_func = pre_func
self.post_func = post_func
@property
def fields(self):
return self.df.columns.tolist()
def get_hash(self):
return self.namestring
@abstractmethod
def fetch_data(self):
return
@staticmethod
def parse_date_str_series(format_str, tz, date_str_series, data_frequency,
trading_day):
"""
Efficient parsing for a 1d Pandas/numpy object containing string
representations of dates.
Note: pd.to_datetime is significantly faster when no format string is
passed, and in pandas 0.12.0 the %p strptime directive is not correctly
handled if a format string is explicitly passed, but AM/PM is handled
properly if format=None.
Moreover, we were previously ignoring this parameter unintentionally
because we were incorrectly passing it as a positional. For all these
reasons, we ignore the format_str parameter when parsing datetimes.
"""
# Explicitly ignoring this parameter. See note above.
if format_str is not None:
logger.warn(
"The 'format_str' parameter to fetch_csv is deprecated. "
"Ignoring and defaulting to pandas default date parsing."
)
format_str = None
tz_str = str(tz)
if tz_str == pytz.utc.zone:
parsed = pd.to_datetime(
date_str_series.values,
format=format_str,
utc=True,
errors='coerce',
)
else:
parsed = pd.to_datetime(
date_str_series.values,
format=format_str,
errors='coerce',
).tz_localize(tz_str).tz_convert('UTC')
if data_frequency == 'daily':
parsed = roll_dts_to_midnight(parsed, trading_day)
return parsed
def mask_pandas_args(self, kwargs):
pandas_kwargs = {key: val for (key, val) in iteritems(kwargs)
if key in ALLOWED_READ_CSV_KWARGS}
if 'usecols' in pandas_kwargs:
usecols = pandas_kwargs['usecols']
if usecols and self.date_column not in usecols:
# make a new list so we don't modify user's,
# and to ensure it is mutable
with_date = list(usecols)
with_date.append(self.date_column)
pandas_kwargs['usecols'] = with_date
# No strings in the 'symbol' column should be interpreted as NaNs
pandas_kwargs.setdefault('keep_default_na', False)
pandas_kwargs.setdefault('na_values', {'symbol': []})
return pandas_kwargs
def _lookup_unconflicted_symbol(self, symbol):
"""
Attempt to find a unique asset whose symbol is the given string.
If multiple assets have held the given symbol, return a 0.
If no asset has held the given symbol, return a NaN.
"""
try:
uppered = symbol.upper()
except AttributeError:
# The mapping fails because symbol was a non-string
return numpy.nan
try:
return self.finder.lookup_symbol(
uppered,
as_of_date=None,
country_code=self.country_code,
)
except MultipleSymbolsFound:
# Fill conflicted entries with zeros to mark that they need to be
# resolved by date.
return 0
except SymbolNotFound:
# Fill not found entries with nans.
return numpy.nan
def load_df(self):
df = self.fetch_data()
if self.pre_func:
df = self.pre_func(df)
# Batch-convert the user-specifed date column into timestamps.
df['dt'] = self.parse_date_str_series(
self.date_format,
self.timezone,
df[self.date_column],
self.data_frequency,
self.trading_day,
).values
# ignore rows whose dates we couldn't parse
df = df[df['dt'].notnull()]
if self.symbol is not None:
df['sid'] = self.symbol
elif self.finder:
df.sort_values(by=self.symbol_column, inplace=True)
# Pop the 'sid' column off of the DataFrame, just in case the user
# has assigned it, and throw a warning
try:
df.pop('sid')
warnings.warn(
"Assignment of the 'sid' column of a DataFrame is "
"not supported by Fetcher. The 'sid' column has been "
"overwritten.",
category=UserWarning,
stacklevel=2,
)
except KeyError:
# There was no 'sid' column, so no warning is necessary
pass
# Fill entries for any symbols that don't require a date to
# uniquely identify. Entries for which multiple securities exist
# are replaced with zeroes, while entries for which no asset
# exists are replaced with NaNs.
unique_symbols = df[self.symbol_column].unique()
sid_series = pd.Series(
data=map(self._lookup_unconflicted_symbol, unique_symbols),
index=unique_symbols,
name='sid',
)
df = df.join(sid_series, on=self.symbol_column)
# Fill any zero entries left in our sid column by doing a lookup
# using both symbol and the row date.
conflict_rows = df[df['sid'] == 0]
for row_idx, row in conflict_rows.iterrows():
try:
asset = self.finder.lookup_symbol(
row[self.symbol_column],
# Replacing tzinfo here is necessary because of the
# timezone metadata bug described below.
row['dt'].replace(tzinfo=pytz.utc),
country_code=self.country_code,
# It's possible that no asset comes back here if our
# lookup date is from before any asset held the
# requested symbol. Mark such cases as NaN so that
# they get dropped in the next step.
) or numpy.nan
except SymbolNotFound:
asset = numpy.nan
# Assign the resolved asset to the cell
df.loc[row_idx, 'sid'] = asset
# Filter out rows containing symbols that we failed to find.
length_before_drop = len(df)
df = df[df['sid'].notnull()]
no_sid_count = length_before_drop - len(df)
if no_sid_count:
logger.warn(
"Dropped {} rows from fetched csv.".format(no_sid_count),
no_sid_count,
extra={'syslog': True},
)
else:
df['sid'] = df['symbol']
# Dates are localized to UTC when they come out of
# parse_date_str_series, but we need to re-localize them here because
# of a bug that wasn't fixed until
# https://github.com/pydata/pandas/pull/7092.
# We should be able to remove the call to tz_localize once we're on
# pandas 0.14.0
# We don't set 'dt' as the index until here because the Symbol parsing
# operations above depend on having a unique index for the dataframe,
# and the 'dt' column can contain multiple dates for the same entry.
df.drop_duplicates(["sid", "dt"])
df.set_index(['dt'], inplace=True)
df = df.tz_localize('UTC')
df.sort_index(inplace=True)
cols_to_drop = [self.date_column]
if self.symbol is None:
cols_to_drop.append(self.symbol_column)
df = df[df.columns.drop(cols_to_drop)]
if self.post_func:
df = self.post_func(df)
return df
def __iter__(self):
asset_cache = {}
for dt, series in self.df.iterrows():
if dt < self.start_date:
continue
if dt > self.end_date:
return
event = FetcherEvent()
# when dt column is converted to be the dataframe's index
# the dt column is dropped. So, we need to manually copy
# dt into the event.
event.dt = dt
for k, v in series.iteritems():
# convert numpy integer types to
# int. This assumes we are on a 64bit
# platform that will not lose information
# by casting.
# TODO: this is only necessary on the
# amazon qexec instances. would be good
# to figure out how to use the numpy dtypes
# without this check and casting.
if isinstance(v, numpy.integer):
v = int(v)
setattr(event, k, v)
# If it has start_date, then it's already an Asset
# object from asset_for_symbol, and we don't have to
# transform it any further. Checking for start_date is
# faster than isinstance.
if event.sid in asset_cache:
event.sid = asset_cache[event.sid]
elif hasattr(event.sid, 'start_date'):
# Clone for user algo code, if we haven't already.
asset_cache[event.sid] = event.sid
elif self.finder and isinstance(event.sid, int):
asset = self.finder.retrieve_asset(event.sid,
default_none=True)
if asset:
# Clone for user algo code.
event.sid = asset_cache[asset] = asset
elif self.mask:
# When masking drop all non-mappable values.
continue
elif self.symbol is None:
# If the event's sid property is an int we coerce
# it into an Equity.
event.sid = asset_cache[event.sid] = Equity(event.sid)
event.type = DATASOURCE_TYPE.CUSTOM
event.source_id = self.namestring
yield event
class PandasRequestsCSV(PandasCSV):
# maximum 100 megs to prevent DDoS
MAX_DOCUMENT_SIZE = (1024 * 1024) * 100
# maximum number of bytes to read in at a time
CONTENT_CHUNK_SIZE = 4096
def __init__(self,
url,
pre_func,
post_func,
asset_finder,
trading_day,
start_date,
end_date,
date_column,
date_format,
timezone,
symbol,
mask,
symbol_column,
data_frequency,
country_code,
special_params_checker=None,
**kwargs):
# Peel off extra requests kwargs, forwarding the remaining kwargs to
# the superclass.
# Also returns possible https updated url if sent to http quandl ds
# If url hasn't changed, will just return the original.
self._requests_kwargs, self.url =\
mask_requests_args(url,
params_checker=special_params_checker,
**kwargs)
remaining_kwargs = {
k: v for k, v in iteritems(kwargs)
if k not in self.requests_kwargs
}
self.namestring = type(self).__name__
super(PandasRequestsCSV, self).__init__(
pre_func,
post_func,
asset_finder,
trading_day,
start_date,
end_date,
date_column,
date_format,
timezone,
symbol,
mask,
symbol_column,
data_frequency,
country_code=country_code,
**remaining_kwargs
)
self.fetch_size = None
self.fetch_hash = None
self.df = self.load_df()
self.special_params_checker = special_params_checker
@property
def requests_kwargs(self):
return self._requests_kwargs
def fetch_url(self, url):
info = "checking {url} with {params}"
logger.info(info.format(url=url, params=self.requests_kwargs))
# setting decode_unicode=True sometimes results in a
# UnicodeEncodeError exception, so instead we'll use
# pandas logic for decoding content
try:
response = requests.get(url, **self.requests_kwargs)
except requests.exceptions.ConnectionError:
raise Exception('Could not connect to %s' % url)
if not response.ok:
raise Exception('Problem reaching %s' % url)
elif response.is_redirect:
# On the offchance we don't catch a redirect URL
# in validation, this will catch it.
new_url = response.headers['location']
raise FetcherCSVRedirectError(
url=url,
new_url=new_url,
extra={
'old_url': url,
'new_url': new_url
}
)
content_length = 0
logger.info('{} connection established in {:.1f} seconds'.format(
url, response.elapsed.total_seconds()))
# use the decode_unicode flag to ensure that the output of this is
# a string, and not bytes.
for chunk in response.iter_content(self.CONTENT_CHUNK_SIZE,
decode_unicode=True):
if content_length > self.MAX_DOCUMENT_SIZE:
raise Exception('Document size too big.')
if chunk:
content_length += len(chunk)
yield chunk
return
def fetch_data(self):
# create a data frame directly from the full text of
# the response from the returned file-descriptor.
data = self.fetch_url(self.url)
fd = StringIO()
if isinstance(data, str):
fd.write(data)
else:
for chunk in data:
fd.write(chunk)
self.fetch_size = fd.tell()
fd.seek(0)
try:
# see if pandas can parse csv data
frames = read_csv(fd, **self.pandas_kwargs)
frames_hash = hashlib.md5(str(fd.getvalue()).encode('utf-8'))
self.fetch_hash = frames_hash.hexdigest()
except pd.parser.CParserError:
# could not parse the data, raise exception
raise Exception('Error parsing remote CSV data.')
finally:
fd.close()
return frames | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/sources/requests_csv.py | requests_csv.py |
from abc import (
ABCMeta,
abstractmethod,
abstractproperty,
)
from numpy import concatenate
from lru import LRU
from pandas import isnull
from toolz import sliding_window
from six import with_metaclass
from zipline.assets import Equity, Future
from zipline.assets.continuous_futures import ContinuousFuture
from zipline.lib._int64window import AdjustedArrayWindow as Int64Window
from zipline.lib._float64window import AdjustedArrayWindow as Float64Window
from zipline.lib.adjustment import Float64Multiply, Float64Add
from zipline.utils.cache import ExpiringCache
from zipline.utils.math_utils import number_of_decimal_places
from zipline.utils.memoize import lazyval
from zipline.utils.numpy_utils import float64_dtype
from zipline.utils.pandas_utils import find_in_sorted_index, normalize_date
# Default number of decimal places used for rounding asset prices.
DEFAULT_ASSET_PRICE_DECIMALS = 3
class HistoryCompatibleUSEquityAdjustmentReader(object):
def __init__(self, adjustment_reader):
self._adjustments_reader = adjustment_reader
def load_pricing_adjustments(self, columns, dts, assets):
"""
Returns
-------
adjustments : list[dict[int -> Adjustment]]
A list, where each element corresponds to the `columns`, of
mappings from index to adjustment objects to apply at that index.
"""
out = [None] * len(columns)
for i, column in enumerate(columns):
adjs = {}
for asset in assets:
adjs.update(self._get_adjustments_in_range(
asset, dts, column))
out[i] = adjs
return out
def _get_adjustments_in_range(self, asset, dts, field):
"""
Get the Float64Multiply objects to pass to an AdjustedArrayWindow.
For the use of AdjustedArrayWindow in the loader, which looks back
from current simulation time back to a window of data the dictionary is
structured with:
- the key into the dictionary for adjustments is the location of the
day from which the window is being viewed.
- the start of all multiply objects is always 0 (in each window all
adjustments are overlapping)
- the end of the multiply object is the location before the calendar
location of the adjustment action, making all days before the event
adjusted.
Parameters
----------
asset : Asset
The assets for which to get adjustments.
dts : iterable of datetime64-like
The dts for which adjustment data is needed.
field : str
OHLCV field for which to get the adjustments.
Returns
-------
out : dict[loc -> Float64Multiply]
The adjustments as a dict of loc -> Float64Multiply
"""
sid = int(asset)
start = normalize_date(dts[0])
end = normalize_date(dts[-1])
adjs = {}
if field != 'volume':
mergers = self._adjustments_reader.get_adjustments_for_sid(
'mergers', sid)
for m in mergers:
dt = m[0]
if start < dt <= end:
end_loc = dts.searchsorted(dt)
adj_loc = end_loc
mult = Float64Multiply(0,
end_loc - 1,
0,
0,
m[1])
try:
adjs[adj_loc].append(mult)
except KeyError:
adjs[adj_loc] = [mult]
divs = self._adjustments_reader.get_adjustments_for_sid(
'dividends', sid)
for d in divs:
dt = d[0]
if start < dt <= end:
end_loc = dts.searchsorted(dt)
adj_loc = end_loc
mult = Float64Multiply(0,
end_loc - 1,
0,
0,
d[1])
try:
adjs[adj_loc].append(mult)
except KeyError:
adjs[adj_loc] = [mult]
splits = self._adjustments_reader.get_adjustments_for_sid(
'splits', sid)
for s in splits:
dt = s[0]
if start < dt <= end:
if field == 'volume':
ratio = 1.0 / s[1]
else:
ratio = s[1]
end_loc = dts.searchsorted(dt)
adj_loc = end_loc
mult = Float64Multiply(0,
end_loc - 1,
0,
0,
ratio)
try:
adjs[adj_loc].append(mult)
except KeyError:
adjs[adj_loc] = [mult]
return adjs
class ContinuousFutureAdjustmentReader(object):
"""
Calculates adjustments for continuous futures, based on the
close and open of the contracts on the either side of each roll.
"""
def __init__(self,
trading_calendar,
asset_finder,
bar_reader,
roll_finders,
frequency):
self._trading_calendar = trading_calendar
self._asset_finder = asset_finder
self._bar_reader = bar_reader
self._roll_finders = roll_finders
self._frequency = frequency
def load_pricing_adjustments(self, columns, dts, assets):
"""
Returns
-------
adjustments : list[dict[int -> Adjustment]]
A list, where each element corresponds to the `columns`, of
mappings from index to adjustment objects to apply at that index.
"""
out = [None] * len(columns)
for i, column in enumerate(columns):
adjs = {}
for asset in assets:
adjs.update(self._get_adjustments_in_range(
asset, dts, column))
out[i] = adjs
return out
def _make_adjustment(self,
adjustment_type,
front_close,
back_close,
end_loc):
adj_base = back_close - front_close
if adjustment_type == 'mul':
adj_value = 1.0 + adj_base / front_close
adj_class = Float64Multiply
elif adjustment_type == 'add':
adj_value = adj_base
adj_class = Float64Add
return adj_class(0,
end_loc,
0,
0,
adj_value)
def _get_adjustments_in_range(self, cf, dts, field):
if field == 'volume' or field == 'sid':
return {}
if cf.adjustment is None:
return {}
rf = self._roll_finders[cf.roll_style]
partitions = []
rolls = rf.get_rolls(cf.root_symbol, dts[0], dts[-1],
cf.offset)
tc = self._trading_calendar
adjs = {}
for front, back in sliding_window(2, rolls):
front_sid, roll_dt = front
back_sid = back[0]
dt = tc.previous_session_label(roll_dt)
if self._frequency == 'minute':
dt = tc.open_and_close_for_session(dt)[1]
roll_dt = tc.open_and_close_for_session(roll_dt)[0]
partitions.append((front_sid,
back_sid,
dt,
roll_dt))
for partition in partitions:
front_sid, back_sid, dt, roll_dt = partition
last_front_dt = self._bar_reader.get_last_traded_dt(
self._asset_finder.retrieve_asset(front_sid), dt)
last_back_dt = self._bar_reader.get_last_traded_dt(
self._asset_finder.retrieve_asset(back_sid), dt)
if isnull(last_front_dt) or isnull(last_back_dt):
continue
front_close = self._bar_reader.get_value(
front_sid, last_front_dt, 'close')
back_close = self._bar_reader.get_value(
back_sid, last_back_dt, 'close')
adj_loc = dts.searchsorted(roll_dt)
end_loc = adj_loc - 1
adj = self._make_adjustment(cf.adjustment,
front_close,
back_close,
end_loc)
try:
adjs[adj_loc].append(adj)
except KeyError:
adjs[adj_loc] = [adj]
return adjs
class SlidingWindow(object):
"""
Wrapper around an AdjustedArrayWindow which supports monotonically
increasing (by datetime) requests for a sized window of data.
Parameters
----------
window : AdjustedArrayWindow
Window of pricing data with prefetched values beyond the current
simulation dt.
cal_start : int
Index in the overall calendar at which the window starts.
"""
def __init__(self, window, size, cal_start, offset):
self.window = window
self.cal_start = cal_start
self.current = next(window)
self.offset = offset
self.most_recent_ix = self.cal_start + size
def get(self, end_ix):
"""
Returns
-------
out : A np.ndarray of the equity pricing up to end_ix after adjustments
and rounding have been applied.
"""
if self.most_recent_ix == end_ix:
return self.current
target = end_ix - self.cal_start - self.offset + 1
self.current = self.window.seek(target)
self.most_recent_ix = end_ix
return self.current
class HistoryLoader(with_metaclass(ABCMeta)):
"""
Loader for sliding history windows, with support for adjustments.
Parameters
----------
trading_calendar: TradingCalendar
Contains the grouping logic needed to assign minutes to periods.
reader : DailyBarReader, MinuteBarReader
Reader for pricing bars.
adjustment_reader : SQLiteAdjustmentReader
Reader for adjustment data.
"""
FIELDS = ('open', 'high', 'low', 'close', 'volume', 'sid')
def __init__(self, trading_calendar, reader, equity_adjustment_reader,
asset_finder,
roll_finders=None,
sid_cache_size=1000,
prefetch_length=0):
self.trading_calendar = trading_calendar
self._asset_finder = asset_finder
self._reader = reader
self._adjustment_readers = {}
if equity_adjustment_reader is not None:
self._adjustment_readers[Equity] = \
HistoryCompatibleUSEquityAdjustmentReader(
equity_adjustment_reader)
if roll_finders:
self._adjustment_readers[ContinuousFuture] =\
ContinuousFutureAdjustmentReader(trading_calendar,
asset_finder,
reader,
roll_finders,
self._frequency)
self._window_blocks = {
field: ExpiringCache(LRU(sid_cache_size))
for field in self.FIELDS
}
self._prefetch_length = prefetch_length
@abstractproperty
def _frequency(self):
pass
@abstractproperty
def _calendar(self):
pass
@abstractmethod
def _array(self, start, end, assets, field):
pass
def _decimal_places_for_asset(self, asset, reference_date):
if isinstance(asset, Future) and asset.tick_size:
return number_of_decimal_places(asset.tick_size)
elif isinstance(asset, ContinuousFuture):
# Tick size should be the same for all contracts of a continuous
# future, so arbitrarily get the contract with next upcoming auto
# close date.
oc = self._asset_finder.get_ordered_contracts(asset.root_symbol)
contract_sid = oc.contract_before_auto_close(reference_date.value)
if contract_sid is not None:
contract = self._asset_finder.retrieve_asset(contract_sid)
if contract.tick_size:
return number_of_decimal_places(contract.tick_size)
return DEFAULT_ASSET_PRICE_DECIMALS
def _ensure_sliding_windows(self, assets, dts, field,
is_perspective_after):
"""
Ensure that there is a Float64Multiply window for each asset that can
provide data for the given parameters.
If the corresponding window for the (assets, len(dts), field) does not
exist, then create a new one.
If a corresponding window does exist for (assets, len(dts), field), but
can not provide data for the current dts range, then create a new
one and replace the expired window.
Parameters
----------
assets : iterable of Assets
The assets in the window
dts : iterable of datetime64-like
The datetimes for which to fetch data.
Makes an assumption that all dts are present and contiguous,
in the calendar.
field : str
The OHLCV field for which to retrieve data.
is_perspective_after : bool
see: `PricingHistoryLoader.history`
Returns
-------
out : list of Float64Window with sufficient data so that each asset's
window can provide `get` for the index corresponding with the last
value in `dts`
"""
end = dts[-1]
size = len(dts)
asset_windows = {}
needed_assets = []
cal = self._calendar
assets = self._asset_finder.retrieve_all(assets)
end_ix = find_in_sorted_index(cal, end)
for asset in assets:
try:
window = self._window_blocks[field].get(
(asset, size, is_perspective_after), end)
except KeyError:
needed_assets.append(asset)
else:
if end_ix < window.most_recent_ix:
# Window needs reset. Requested end index occurs before the
# end index from the previous history call for this window.
# Grab new window instead of rewinding adjustments.
needed_assets.append(asset)
else:
asset_windows[asset] = window
if needed_assets:
offset = 0
start_ix = find_in_sorted_index(cal, dts[0])
prefetch_end_ix = min(end_ix + self._prefetch_length, len(cal) - 1)
prefetch_end = cal[prefetch_end_ix]
prefetch_dts = cal[start_ix:prefetch_end_ix + 1]
if is_perspective_after:
adj_end_ix = min(prefetch_end_ix + 1, len(cal) - 1)
adj_dts = cal[start_ix:adj_end_ix + 1]
else:
adj_dts = prefetch_dts
prefetch_len = len(prefetch_dts)
array = self._array(prefetch_dts, needed_assets, field)
if field == 'sid':
window_type = Int64Window
else:
window_type = Float64Window
view_kwargs = {}
if field == 'volume':
array = array.astype(float64_dtype)
for i, asset in enumerate(needed_assets):
adj_reader = None
try:
adj_reader = self._adjustment_readers[type(asset)]
except KeyError:
adj_reader = None
if adj_reader is not None:
adjs = adj_reader.load_pricing_adjustments(
[field], adj_dts, [asset])[0]
else:
adjs = {}
window = window_type(
array[:, i].reshape(prefetch_len, 1),
view_kwargs,
adjs,
offset,
size,
int(is_perspective_after),
self._decimal_places_for_asset(asset, dts[-1]),
)
sliding_window = SlidingWindow(window, size, start_ix, offset)
asset_windows[asset] = sliding_window
self._window_blocks[field].set(
(asset, size, is_perspective_after),
sliding_window,
prefetch_end)
return [asset_windows[asset] for asset in assets]
def history(self, assets, dts, field, is_perspective_after):
"""
A window of pricing data with adjustments applied assuming that the
end of the window is the day before the current simulation time.
Parameters
----------
assets : iterable of Assets
The assets in the window.
dts : iterable of datetime64-like
The datetimes for which to fetch data.
Makes an assumption that all dts are present and contiguous,
in the calendar.
field : str
The OHLCV field for which to retrieve data.
is_perspective_after : bool
True, if the window is being viewed immediately after the last dt
in the sliding window.
False, if the window is viewed on the last dt.
This flag is used for handling the case where the last dt in the
requested window immediately precedes a corporate action, e.g.:
- is_perspective_after is True
When the viewpoint is after the last dt in the window, as when a
daily history window is accessed from a simulation that uses a
minute data frequency, the history call to this loader will not
include the current simulation dt. At that point in time, the raw
data for the last day in the window will require adjustment, so the
most recent adjustment with respect to the simulation time is
applied to the last dt in the requested window.
An example equity which has a 0.5 split ratio dated for 05-27,
with the dts for a history call of 5 bars with a '1d' frequency at
05-27 9:31. Simulation frequency is 'minute'.
(In this case this function is called with 4 daily dts, and the
calling function is responsible for stitching back on the
'current' dt)
| | | | | last dt | <-- viewer is here |
| | 05-23 | 05-24 | 05-25 | 05-26 | 05-27 9:31 |
| raw | 10.10 | 10.20 | 10.30 | 10.40 | |
| adj | 5.05 | 5.10 | 5.15 | 5.25 | |
The adjustment is applied to the last dt, 05-26, and all previous
dts.
- is_perspective_after is False, daily
When the viewpoint is the same point in time as the last dt in the
window, as when a daily history window is accessed from a
simulation that uses a daily data frequency, the history call will
include the current dt. At that point in time, the raw data for the
last day in the window will be post-adjustment, so no adjustment
is applied to the last dt.
An example equity which has a 0.5 split ratio dated for 05-27,
with the dts for a history call of 5 bars with a '1d' frequency at
05-27 0:00. Simulation frequency is 'daily'.
| | | | | | <-- viewer is here |
| | | | | | last dt |
| | 05-23 | 05-24 | 05-25 | 05-26 | 05-27 |
| raw | 10.10 | 10.20 | 10.30 | 10.40 | 5.25 |
| adj | 5.05 | 5.10 | 5.15 | 5.20 | 5.25 |
Adjustments are applied 05-23 through 05-26 but not to the last dt,
05-27
Returns
-------
out : np.ndarray with shape(len(days between start, end), len(assets))
"""
block = self._ensure_sliding_windows(assets,
dts,
field,
is_perspective_after)
end_ix = self._calendar.searchsorted(dts[-1])
return concatenate(
[window.get(end_ix) for window in block],
axis=1,
)
class DailyHistoryLoader(HistoryLoader):
@property
def _frequency(self):
return 'daily'
@property
def _calendar(self):
return self._reader.sessions
def _array(self, dts, assets, field):
return self._reader.load_raw_arrays(
[field],
dts[0],
dts[-1],
assets,
)[0]
class MinuteHistoryLoader(HistoryLoader):
@property
def _frequency(self):
return 'minute'
@lazyval
def _calendar(self):
mm = self.trading_calendar.all_minutes
start = mm.searchsorted(self._reader.first_trading_day)
end = mm.searchsorted(self._reader.last_available_dt, side='right')
return mm[start:end]
def _array(self, dts, assets, field):
return self._reader.load_raw_arrays(
[field],
dts[0],
dts[-1],
assets,
)[0] | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/history_loader.py | history_loader.py |
from functools import partial
import warnings
from bcolz import carray, ctable
import logbook
import numpy as np
from numpy import (
array,
full,
iinfo,
nan,
)
from pandas import (
DatetimeIndex,
NaT,
read_csv,
to_datetime,
Timestamp,
)
from six import iteritems, viewkeys
from toolz import compose
from trading_calendars import get_calendar
from zipline.data.session_bars import CurrencyAwareSessionBarReader
from zipline.data.bar_reader import (
NoDataAfterDate,
NoDataBeforeDate,
NoDataOnDate,
)
from zipline.utils.functional import apply
from zipline.utils.input_validation import expect_element
from zipline.utils.numpy_utils import iNaT, float64_dtype, uint32_dtype
from zipline.utils.memoize import lazyval
from zipline.utils.cli import maybe_show_progress
from ._equities import _compute_row_slices, _read_bcolz_data
logger = logbook.Logger('UsEquityPricing')
OHLC = frozenset(['open', 'high', 'low', 'close'])
US_EQUITY_PRICING_BCOLZ_COLUMNS = (
'open', 'high', 'low', 'close', 'volume', 'day', 'id'
)
UINT32_MAX = iinfo(np.uint32).max
def check_uint32_safe(value, colname):
if value >= UINT32_MAX:
raise ValueError(
"Value %s from column '%s' is too large" % (value, colname)
)
@expect_element(invalid_data_behavior={'warn', 'raise', 'ignore'})
def winsorise_uint32(df, invalid_data_behavior, column, *columns):
"""Drops any record where a value would not fit into a uint32.
Parameters
----------
df : pd.DataFrame
The dataframe to winsorise.
invalid_data_behavior : {'warn', 'raise', 'ignore'}
What to do when data is outside the bounds of a uint32.
*columns : iterable[str]
The names of the columns to check.
Returns
-------
truncated : pd.DataFrame
``df`` with values that do not fit into a uint32 zeroed out.
"""
columns = list((column,) + columns)
mask = df[columns] > UINT32_MAX
if invalid_data_behavior != 'ignore':
mask |= df[columns].isnull()
else:
# we are not going to generate a warning or error for this so just use
# nan_to_num
df[columns] = np.nan_to_num(df[columns])
mv = mask.values
if mv.any():
if invalid_data_behavior == 'raise':
raise ValueError(
'%d values out of bounds for uint32: %r' % (
mv.sum(), df[mask.any(axis=1)],
),
)
if invalid_data_behavior == 'warn':
warnings.warn(
'Ignoring %d values because they are out of bounds for'
' uint32: %r' % (
mv.sum(), df[mask.any(axis=1)],
),
stacklevel=3, # one extra frame for `expect_element`
)
df[mask] = 0
return df
class BcolzDailyBarWriter(object):
"""
Class capable of writing daily OHLCV data to disk in a format that can
be read efficiently by BcolzDailyOHLCVReader.
Parameters
----------
filename : str
The location at which we should write our output.
calendar : zipline.utils.calendar.trading_calendar
Calendar to use to compute asset calendar offsets.
start_session: pd.Timestamp
Midnight UTC session label.
end_session: pd.Timestamp
Midnight UTC session label.
See Also
--------
zipline.data.bcolz_daily_bars.BcolzDailyBarReader
"""
_csv_dtypes = {
'open': float64_dtype,
'high': float64_dtype,
'low': float64_dtype,
'close': float64_dtype,
'volume': float64_dtype,
}
def __init__(self, filename, calendar, start_session, end_session):
self._filename = filename
if start_session != end_session:
if not calendar.is_session(start_session):
raise ValueError(
"Start session %s is invalid!" % start_session
)
if not calendar.is_session(end_session):
raise ValueError(
"End session %s is invalid!" % end_session
)
self._start_session = start_session
self._end_session = end_session
self._calendar = calendar
@property
def progress_bar_message(self):
return "Merging daily equity files:"
def progress_bar_item_show_func(self, value):
return value if value is None else str(value[0])
def write(self,
data,
assets=None,
show_progress=False,
invalid_data_behavior='warn'):
"""
Parameters
----------
data : iterable[tuple[int, pandas.DataFrame or bcolz.ctable]]
The data chunks to write. Each chunk should be a tuple of sid
and the data for that asset.
assets : set[int], optional
The assets that should be in ``data``. If this is provided
we will check ``data`` against the assets and provide better
progress information.
show_progress : bool, optional
Whether or not to show a progress bar while writing.
invalid_data_behavior : {'warn', 'raise', 'ignore'}, optional
What to do when data is encountered that is outside the range of
a uint32.
Returns
-------
table : bcolz.ctable
The newly-written table.
"""
ctx = maybe_show_progress(
(
(sid, self.to_ctable(df, invalid_data_behavior))
for sid, df in data
),
show_progress=show_progress,
item_show_func=self.progress_bar_item_show_func,
label=self.progress_bar_message,
length=len(assets) if assets is not None else None,
)
with ctx as it:
return self._write_internal(it, assets)
def write_csvs(self,
asset_map,
show_progress=False,
invalid_data_behavior='warn'):
"""Read CSVs as DataFrames from our asset map.
Parameters
----------
asset_map : dict[int -> str]
A mapping from asset id to file path with the CSV data for that
asset
show_progress : bool
Whether or not to show a progress bar while writing.
invalid_data_behavior : {'warn', 'raise', 'ignore'}
What to do when data is encountered that is outside the range of
a uint32.
"""
read = partial(
read_csv,
parse_dates=['day'],
index_col='day',
dtype=self._csv_dtypes,
)
return self.write(
((asset, read(path)) for asset, path in iteritems(asset_map)),
assets=viewkeys(asset_map),
show_progress=show_progress,
invalid_data_behavior=invalid_data_behavior,
)
def _write_internal(self, iterator, assets):
"""
Internal implementation of write.
`iterator` should be an iterator yielding pairs of (asset, ctable).
"""
total_rows = 0
first_row = {}
last_row = {}
calendar_offset = {}
# Maps column name -> output carray.
columns = {
k: carray(array([], dtype=uint32_dtype))
for k in US_EQUITY_PRICING_BCOLZ_COLUMNS
}
earliest_date = None
sessions = self._calendar.sessions_in_range(
self._start_session, self._end_session
)
if assets is not None:
@apply
def iterator(iterator=iterator, assets=set(assets)):
for asset_id, table in iterator:
if asset_id not in assets:
raise ValueError('unknown asset id %r' % asset_id)
yield asset_id, table
for asset_id, table in iterator:
nrows = len(table)
for column_name in columns:
if column_name == 'id':
# We know what the content of this column is, so don't
# bother reading it.
columns['id'].append(
full((nrows,), asset_id, dtype='uint32'),
)
continue
columns[column_name].append(table[column_name])
if earliest_date is None:
earliest_date = table["day"][0]
else:
earliest_date = min(earliest_date, table["day"][0])
# Bcolz doesn't support ints as keys in `attrs`, so convert
# assets to strings for use as attr keys.
asset_key = str(asset_id)
# Calculate the index into the array of the first and last row
# for this asset. This allows us to efficiently load single
# assets when querying the data back out of the table.
first_row[asset_key] = total_rows
last_row[asset_key] = total_rows + nrows - 1
total_rows += nrows
table_day_to_session = compose(
self._calendar.minute_to_session_label,
partial(Timestamp, unit='s', tz='UTC'),
)
asset_first_day = table_day_to_session(table['day'][0])
asset_last_day = table_day_to_session(table['day'][-1])
asset_sessions = sessions[
sessions.slice_indexer(asset_first_day, asset_last_day)
]
if len(table) != len(asset_sessions):
msg = (
'Asset id: {}, Got {} rows for daily bars table with first day={}, last '
'day={}, expected {} rows.\n'
'Missing sessions: {}\n'
'Extra sessions: {}. Skipping it'.format(
asset_id,
len(table),
asset_first_day.date(),
asset_last_day.date(),
len(asset_sessions),
asset_sessions.difference(
to_datetime(
np.array(table['day']),
unit='s',
utc=True,
)
).tolist(),
to_datetime(
np.array(table['day']),
unit='s',
utc=True,
).difference(asset_sessions).tolist(),
)
)
logger.warning(msg)
continue
# Calculate the number of trading days between the first date
# in the stored data and the first date of **this** asset. This
# offset used for output alignment by the reader.
calendar_offset[asset_key] = sessions.get_loc(asset_first_day)
# This writes the table to disk.
full_table = ctable(
columns=[
columns[colname]
for colname in US_EQUITY_PRICING_BCOLZ_COLUMNS
],
names=US_EQUITY_PRICING_BCOLZ_COLUMNS,
rootdir=self._filename,
mode='w',
)
full_table.attrs['first_trading_day'] = (
earliest_date if earliest_date is not None else iNaT
)
full_table.attrs['first_row'] = first_row
full_table.attrs['last_row'] = last_row
full_table.attrs['calendar_offset'] = calendar_offset
full_table.attrs['calendar_name'] = self._calendar.name
full_table.attrs['start_session_ns'] = self._start_session.value
full_table.attrs['end_session_ns'] = self._end_session.value
full_table.flush()
return full_table
@expect_element(invalid_data_behavior={'warn', 'raise', 'ignore'})
def to_ctable(self, raw_data, invalid_data_behavior):
if isinstance(raw_data, ctable):
# we already have a ctable so do nothing
return raw_data
winsorise_uint32(raw_data, invalid_data_behavior, 'volume', *OHLC)
processed = (raw_data[list(OHLC)] * 1000).round().astype('uint32')
dates = raw_data.index.values.astype('datetime64[s]')
check_uint32_safe(dates.max().view(np.int64), 'day')
processed['day'] = dates.astype('uint32')
processed['volume'] = raw_data.volume.astype('uint32')
return ctable.fromdataframe(processed)
class BcolzDailyBarReader(CurrencyAwareSessionBarReader):
"""
Reader for raw pricing data written by BcolzDailyOHLCVWriter.
Parameters
----------
table : bcolz.ctable
The ctable contaning the pricing data, with attrs corresponding to the
Attributes list below.
read_all_threshold : int
The number of equities at which; below, the data is read by reading a
slice from the carray per asset. above, the data is read by pulling
all of the data for all assets into memory and then indexing into that
array for each day and asset pair. Used to tune performance of reads
when using a small or large number of equities.
Attributes
----------
The table with which this loader interacts contains the following
attributes:
first_row : dict
Map from asset_id -> index of first row in the dataset with that id.
last_row : dict
Map from asset_id -> index of last row in the dataset with that id.
calendar_offset : dict
Map from asset_id -> calendar index of first row.
start_session_ns: int
Epoch ns of the first session used in this dataset.
end_session_ns: int
Epoch ns of the last session used in this dataset.
calendar_name: str
String identifier of trading calendar used (ie, "NYSE").
We use first_row and last_row together to quickly find ranges of rows to
load when reading an asset's data into memory.
We use calendar_offset and calendar to orient loaded blocks within a
range of queried dates.
Notes
------
A Bcolz CTable is comprised of Columns and Attributes.
The table with which this loader interacts contains the following columns:
['open', 'high', 'low', 'close', 'volume', 'day', 'id'].
The data in these columns is interpreted as follows:
- Price columns ('open', 'high', 'low', 'close') are interpreted as 1000 *
as-traded dollar value.
- Volume is interpreted as as-traded volume.
- Day is interpreted as seconds since midnight UTC, Jan 1, 1970.
- Id is the asset id of the row.
The data in each column is grouped by asset and then sorted by day within
each asset block.
The table is built to represent a long time range of data, e.g. ten years
of equity data, so the lengths of each asset block is not equal to each
other. The blocks are clipped to the known start and end date of each asset
to cut down on the number of empty values that would need to be included to
make a regular/cubic dataset.
When read across the open, high, low, close, and volume with the same
index should represent the same asset and day.
See Also
--------
zipline.data.bcolz_daily_bars.BcolzDailyBarWriter
"""
def __init__(self, table, read_all_threshold=3000):
self._maybe_table_rootdir = table
# Cache of fully read np.array for the carrays in the daily bar table.
# raw_array does not use the same cache, but it could.
# Need to test keeping the entire array in memory for the course of a
# process first.
self._spot_cols = {}
self.PRICE_ADJUSTMENT_FACTOR = 0.001
self._read_all_threshold = read_all_threshold
@lazyval
def _table(self):
maybe_table_rootdir = self._maybe_table_rootdir
if isinstance(maybe_table_rootdir, ctable):
return maybe_table_rootdir
return ctable(rootdir=maybe_table_rootdir, mode='r')
@lazyval
def sessions(self):
if 'calendar' in self._table.attrs.attrs:
# backwards compatibility with old formats, will remove
return DatetimeIndex(self._table.attrs['calendar'], tz='UTC')
else:
cal = get_calendar(self._table.attrs['calendar_name'])
start_session_ns = self._table.attrs['start_session_ns']
start_session = Timestamp(start_session_ns, tz='UTC')
end_session_ns = self._table.attrs['end_session_ns']
end_session = Timestamp(end_session_ns, tz='UTC')
sessions = cal.sessions_in_range(start_session, end_session)
return sessions
@lazyval
def _first_rows(self):
return {
int(asset_id): start_index
for asset_id, start_index in iteritems(
self._table.attrs['first_row'],
)
}
@lazyval
def _last_rows(self):
return {
int(asset_id): end_index
for asset_id, end_index in iteritems(
self._table.attrs['last_row'],
)
}
@lazyval
def _calendar_offsets(self):
return {
int(id_): offset
for id_, offset in iteritems(
self._table.attrs['calendar_offset'],
)
}
@lazyval
def first_trading_day(self):
try:
return Timestamp(
self._table.attrs['first_trading_day'],
unit='s',
tz='UTC'
)
except KeyError:
return None
@lazyval
def trading_calendar(self):
if 'calendar_name' in self._table.attrs.attrs:
return get_calendar(self._table.attrs['calendar_name'])
else:
return None
@property
def last_available_dt(self):
return self.sessions[-1]
def _compute_slices(self, start_idx, end_idx, assets):
"""
Compute the raw row indices to load for each asset on a query for the
given dates after applying a shift.
Parameters
----------
start_idx : int
Index of first date for which we want data.
end_idx : int
Index of last date for which we want data.
assets : pandas.Int64Index
Assets for which we want to compute row indices
Returns
-------
A 3-tuple of (first_rows, last_rows, offsets):
first_rows : np.array[intp]
Array with length == len(assets) containing the index of the first
row to load for each asset in `assets`.
last_rows : np.array[intp]
Array with length == len(assets) containing the index of the last
row to load for each asset in `assets`.
offset : np.array[intp]
Array with length == (len(asset) containing the index in a buffer
of length `dates` corresponding to the first row of each asset.
The value of offset[i] will be 0 if asset[i] existed at the start
of a query. Otherwise, offset[i] will be equal to the number of
entries in `dates` for which the asset did not yet exist.
"""
# The core implementation of the logic here is implemented in Cython
# for efficiency.
return _compute_row_slices(
self._first_rows,
self._last_rows,
self._calendar_offsets,
start_idx,
end_idx,
assets,
)
def load_raw_arrays(self, columns, start_date, end_date, assets):
start_idx = self._load_raw_arrays_date_to_index(start_date)
end_idx = self._load_raw_arrays_date_to_index(end_date)
first_rows, last_rows, offsets = self._compute_slices(
start_idx,
end_idx,
assets,
)
read_all = len(assets) > self._read_all_threshold
return _read_bcolz_data(
self._table,
(end_idx - start_idx + 1, len(assets)),
list(columns),
first_rows,
last_rows,
offsets,
read_all,
)
def _load_raw_arrays_date_to_index(self, date):
try:
return self.sessions.get_loc(date)
except KeyError:
raise NoDataOnDate(date)
def _spot_col(self, colname):
"""
Get the colname from daily_bar_table and read all of it into memory,
caching the result.
Parameters
----------
colname : string
A name of a OHLCV carray in the daily_bar_table
Returns
-------
array (uint32)
Full read array of the carray in the daily_bar_table with the
given colname.
"""
try:
col = self._spot_cols[colname]
except KeyError:
col = self._spot_cols[colname] = self._table[colname]
return col
def get_last_traded_dt(self, asset, day):
volumes = self._spot_col('volume')
search_day = day
while True:
try:
ix = self.sid_day_index(asset, search_day)
except NoDataBeforeDate:
return NaT
except NoDataAfterDate:
prev_day_ix = self.sessions.get_loc(search_day) - 1
if prev_day_ix > -1:
search_day = self.sessions[prev_day_ix]
continue
except NoDataOnDate:
return NaT
if volumes[ix] != 0:
return search_day
prev_day_ix = self.sessions.get_loc(search_day) - 1
if prev_day_ix > -1:
search_day = self.sessions[prev_day_ix]
else:
return NaT
def sid_day_index(self, sid, day):
"""
Parameters
----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
Returns
-------
int
Index into the data tape for the given sid and day.
Raises a NoDataOnDate exception if the given day and sid is before
or after the date range of the equity.
"""
try:
day_loc = self.sessions.get_loc(day)
except Exception:
raise NoDataOnDate("day={0} is outside of calendar={1}".format(
day, self.sessions))
offset = day_loc - self._calendar_offsets[sid]
if offset < 0:
raise NoDataBeforeDate(
"No data on or before day={0} for sid={1}".format(
day, sid))
ix = self._first_rows[sid] + offset
if ix > self._last_rows[sid]:
raise NoDataAfterDate(
"No data on or after day={0} for sid={1}".format(
day, sid))
return ix
def get_value(self, sid, dt, field):
"""
Parameters
----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
colname : string
The price field. e.g. ('open', 'high', 'low', 'close', 'volume')
Returns
-------
float
The spot price for colname of the given sid on the given day.
Raises a NoDataOnDate exception if the given day and sid is before
or after the date range of the equity.
Returns -1 if the day is within the date range, but the price is
0.
"""
ix = self.sid_day_index(sid, dt)
price = self._spot_col(field)[ix]
if field != 'volume':
if price == 0:
return nan
else:
return price * 0.001
else:
return price
def currency_codes(self, sids):
# XXX: This is pretty inefficient. This reader doesn't really support
# country codes, so we always either return USD or None if we don't
# know about the sid at all.
first_rows = self._first_rows
out = []
for sid in sids:
if sid in first_rows:
out.append('USD')
else:
out.append(None)
return np.array(out, dtype=object) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bcolz_daily_bars.py | bcolz_daily_bars.py |
from six import iteritems
import numpy as np
import pandas as pd
from pandas import NaT
from trading_calendars import TradingCalendar
from zipline.data.bar_reader import OHLCV, NoDataOnDate, NoDataForSid
from zipline.data.session_bars import CurrencyAwareSessionBarReader
from zipline.utils.input_validation import expect_types, validate_keys
from zipline.utils.pandas_utils import check_indexes_all_same
class InMemoryDailyBarReader(CurrencyAwareSessionBarReader):
"""
A SessionBarReader backed by a dictionary of in-memory DataFrames.
Parameters
----------
frames : dict[str -> pd.DataFrame]
Dictionary from field name ("open", "high", "low", "close", or
"volume") to DataFrame containing data for that field.
calendar : str or trading_calendars.TradingCalendar
Calendar (or name of calendar) to which data is aligned.
currency_codes : pd.Series
Map from sid -> listing currency for that sid.
verify_indices : bool, optional
Whether or not to verify that input data is correctly aligned to the
given calendar. Default is True.
"""
@expect_types(
frames=dict,
calendar=TradingCalendar,
verify_indices=bool,
currency_codes=pd.Series,
)
def __init__(self,
frames,
calendar,
currency_codes,
verify_indices=True):
self._frames = frames
self._values = {key: frame.values for key, frame in iteritems(frames)}
self._calendar = calendar
self._currency_codes = currency_codes
validate_keys(frames, set(OHLCV), type(self).__name__)
if verify_indices:
verify_frames_aligned(list(frames.values()), calendar)
self._sessions = frames['close'].index
self._sids = frames['close'].columns
@classmethod
def from_panel(cls, panel, calendar, currency_codes):
"""Helper for construction from a pandas.Panel.
"""
return cls(dict(panel), calendar, currency_codes)
@property
def last_available_dt(self):
return self._calendar[-1]
@property
def trading_calendar(self):
return self._calendar
@property
def sessions(self):
return self._sessions
def load_raw_arrays(self, columns, start_dt, end_dt, assets):
if start_dt not in self._sessions:
raise NoDataOnDate(start_dt)
if end_dt not in self._sessions:
raise NoDataOnDate(end_dt)
asset_indexer = self._sids.get_indexer(assets)
if -1 in asset_indexer:
bad_assets = assets[asset_indexer == -1]
raise NoDataForSid(bad_assets)
date_indexer = self._sessions.slice_indexer(start_dt, end_dt)
out = []
for c in columns:
out.append(self._values[c][date_indexer, asset_indexer])
return out
def get_value(self, sid, dt, field):
"""
Parameters
----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
field : string
The price field. e.g. ('open', 'high', 'low', 'close', 'volume')
Returns
-------
float
The spot price for colname of the given sid on the given day.
Raises a NoDataOnDate exception if the given day and sid is before
or after the date range of the equity.
Returns -1 if the day is within the date range, but the price is
0.
"""
return self.frames[field].loc[dt, sid]
def get_last_traded_dt(self, asset, dt):
"""
Parameters
----------
asset : zipline.asset.Asset
The asset identifier.
dt : datetime64-like
Midnight of the day for which data is requested.
Returns
-------
pd.Timestamp : The last know dt for the asset and dt;
NaT if no trade is found before the given dt.
"""
try:
return self.frames['close'].loc[:, asset.sid].last_valid_index()
except IndexError:
return NaT
@property
def first_trading_day(self):
return self._sessions[0]
def currency_codes(self, sids):
codes = self._currency_codes
return np.array([codes[sid] for sid in sids])
def verify_frames_aligned(frames, calendar):
"""
Verify that DataFrames in ``frames`` have the same indexing scheme and are
aligned to ``calendar``.
Parameters
----------
frames : list[pd.DataFrame]
calendar : trading_calendars.TradingCalendar
Raises
------
ValueError
If frames have different indexes/columns, or if frame indexes do not
match a contiguous region of ``calendar``.
"""
indexes = [f.index for f in frames]
check_indexes_all_same(indexes, message="DataFrame indexes don't match:")
columns = [f.columns for f in frames]
check_indexes_all_same(columns, message="DataFrame columns don't match:")
start, end = indexes[0][[0, -1]]
cal_sessions = calendar.sessions_in_range(start, end)
check_indexes_all_same(
[indexes[0], cal_sessions],
"DataFrame index doesn't match {} calendar:".format(calendar.name),
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/in_memory_daily_bars.py | in_memory_daily_bars.py |
from abc import ABCMeta, abstractmethod
from numpy import (
full,
nan,
int64,
zeros
)
from six import iteritems, with_metaclass
from zipline.utils.memoize import lazyval
class AssetDispatchBarReader(with_metaclass(ABCMeta)):
"""
Parameters
----------
- trading_calendar : zipline.utils.trading_calendar.TradingCalendar
- asset_finder : zipline.assets.AssetFinder
- readers : dict
A dict mapping Asset type to the corresponding
[Minute|Session]BarReader
- last_available_dt : pd.Timestamp or None, optional
If not provided, infers it by using the min of the
last_available_dt values of the underlying readers.
"""
def __init__(
self,
trading_calendar,
asset_finder,
readers,
last_available_dt=None,
):
self._trading_calendar = trading_calendar
self._asset_finder = asset_finder
self._readers = readers
# if no timezone given, assume utf
if last_available_dt and not last_available_dt.tzinfo:
last_available_dt = last_available_dt.tz_localize('utc')
self._last_available_dt = last_available_dt
for t, r in iteritems(self._readers):
assert trading_calendar == r.trading_calendar, \
"All readers must share target trading_calendar. " \
"Reader={0} for type={1} uses calendar={2} which does not " \
"match the desired shared calendar={3} ".format(
r, t, r.trading_calendar, trading_calendar)
@abstractmethod
def _dt_window_size(self, start_dt, end_dt):
pass
@property
def _asset_types(self):
return self._readers.keys()
def _make_raw_array_shape(self, start_dt, end_dt, num_sids):
return self._dt_window_size(start_dt, end_dt), num_sids
def _make_raw_array_out(self, field, shape):
if field != 'volume' and field != 'sid':
out = full(shape, nan)
else:
out = zeros(shape, dtype=int64)
return out
@property
def trading_calendar(self):
return self._trading_calendar
@lazyval
def last_available_dt(self):
if self._last_available_dt is not None:
return self._last_available_dt
else:
return max(r.last_available_dt for r in self._readers.values())
@lazyval
def first_trading_day(self):
return min(r.first_trading_day for r in self._readers.values())
def get_value(self, sid, dt, field):
asset = self._asset_finder.retrieve_asset(sid)
r = self._readers[type(asset)]
return r.get_value(asset, dt, field)
def get_last_traded_dt(self, asset, dt):
r = self._readers[type(asset)]
return r.get_last_traded_dt(asset, dt)
def load_raw_arrays(self, fields, start_dt, end_dt, sids):
asset_types = self._asset_types
sid_groups = {t: [] for t in asset_types}
out_pos = {t: [] for t in asset_types}
assets = self._asset_finder.retrieve_all(sids)
for i, asset in enumerate(assets):
t = type(asset)
sid_groups[t].append(asset)
out_pos[t].append(i)
batched_arrays = {
t: self._readers[t].load_raw_arrays(fields,
start_dt,
end_dt,
sid_groups[t])
for t in asset_types if sid_groups[t]}
results = []
shape = self._make_raw_array_shape(start_dt, end_dt, len(sids))
for i, field in enumerate(fields):
out = self._make_raw_array_out(field, shape)
for t, arrays in iteritems(batched_arrays):
out[:, out_pos[t]] = arrays[i]
results.append(out)
return results
class AssetDispatchMinuteBarReader(AssetDispatchBarReader):
def _dt_window_size(self, start_dt, end_dt):
return len(self.trading_calendar.minutes_in_range(start_dt, end_dt))
class AssetDispatchSessionBarReader(AssetDispatchBarReader):
def _dt_window_size(self, start_dt, end_dt):
return len(self.trading_calendar.sessions_in_range(start_dt, end_dt))
@lazyval
def sessions(self):
return self.trading_calendar.sessions_in_range(
self.first_trading_day,
self.last_available_dt) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/dispatch_bar_reader.py | dispatch_bar_reader.py |
from abc import ABCMeta, abstractmethod
import json
import os
from glob import glob
from os.path import join
from textwrap import dedent
from lru import LRU
import bcolz
from bcolz import ctable
import h5py
from intervaltree import IntervalTree
import logbook
import numpy as np
import pandas as pd
from pandas import HDFStore
import tables
from six import with_metaclass
from toolz import keymap, valmap
from trading_calendars import get_calendar
from zipline.data._minute_bar_internal import (
minute_value,
find_position_of_minute,
find_last_traded_position_internal
)
from zipline.gens.sim_engine import NANOS_IN_MINUTE
from zipline.data.bar_reader import BarReader, NoDataForSid, NoDataOnDate
from zipline.data.bcolz_daily_bars import check_uint32_safe
from zipline.utils.cli import maybe_show_progress
from zipline.utils.compat import mappingproxy
from zipline.utils.memoize import lazyval
logger = logbook.Logger('MinuteBars')
US_EQUITIES_MINUTES_PER_DAY = 390
FUTURES_MINUTES_PER_DAY = 1440
DEFAULT_EXPECTEDLEN = US_EQUITIES_MINUTES_PER_DAY * 252 * 15
OHLC_RATIO = 1000
class BcolzMinuteOverlappingData(Exception):
pass
class BcolzMinuteWriterColumnMismatch(Exception):
pass
class MinuteBarReader(BarReader):
@property
def data_frequency(self):
return "minute"
def _calc_minute_index(market_opens, minutes_per_day):
minutes = np.zeros(len(market_opens) * minutes_per_day,
dtype='datetime64[ns]')
deltas = np.arange(0, minutes_per_day, dtype='timedelta64[m]')
for i, market_open in enumerate(market_opens):
start = market_open.asm8
minute_values = start + deltas
start_ix = minutes_per_day * i
end_ix = start_ix + minutes_per_day
minutes[start_ix:end_ix] = minute_values
return pd.to_datetime(minutes, utc=True)
def _sid_subdir_path(sid):
"""
Format subdir path to limit the number directories in any given
subdirectory to 100.
The number in each directory is designed to support at least 100000
equities.
Parameters
----------
sid : int
Asset identifier.
Returns
-------
out : string
A path for the bcolz rootdir, including subdirectory prefixes based on
the padded string representation of the given sid.
e.g. 1 is formatted as 00/00/000001.bcolz
"""
padded_sid = format(sid, '06')
return os.path.join(
# subdir 1 00/XX
padded_sid[0:2],
# subdir 2 XX/00
padded_sid[2:4],
"{0}.bcolz".format(str(padded_sid))
)
def convert_cols(cols, scale_factor, sid, invalid_data_behavior):
"""Adapt OHLCV columns into uint32 columns.
Parameters
----------
cols : dict
A dict mapping each column name (open, high, low, close, volume)
to a float column to convert to uint32.
scale_factor : int
Factor to use to scale float values before converting to uint32.
sid : int
Sid of the relevant asset, for logging.
invalid_data_behavior : str
Specifies behavior when data cannot be converted to uint32.
If 'raise', raises an exception.
If 'warn', logs a warning and filters out incompatible values.
If 'ignore', silently filters out incompatible values.
"""
scaled_opens = (np.nan_to_num(cols['open']) * scale_factor).round()
scaled_highs = (np.nan_to_num(cols['high']) * scale_factor).round()
scaled_lows = (np.nan_to_num(cols['low']) * scale_factor).round()
scaled_closes = (np.nan_to_num(cols['close']) * scale_factor).round()
exclude_mask = np.zeros_like(scaled_opens, dtype=bool)
for col_name, scaled_col in [
('open', scaled_opens),
('high', scaled_highs),
('low', scaled_lows),
('close', scaled_closes),
]:
max_val = scaled_col.max()
try:
check_uint32_safe(max_val, col_name)
except ValueError:
if invalid_data_behavior == 'raise':
raise
if invalid_data_behavior == 'warn':
logger.warn(
'Values for sid={}, col={} contain some too large for '
'uint32 (max={}), filtering them out',
sid, col_name, max_val,
)
# We want to exclude all rows that have an unsafe value in
# this column.
exclude_mask &= (scaled_col >= np.iinfo(np.uint32).max)
# Convert all cols to uint32.
opens = scaled_opens.astype(np.uint32)
highs = scaled_highs.astype(np.uint32)
lows = scaled_lows.astype(np.uint32)
closes = scaled_closes.astype(np.uint32)
volumes = cols['volume'].astype(np.uint32)
# Exclude rows with unsafe values by setting to zero.
opens[exclude_mask] = 0
highs[exclude_mask] = 0
lows[exclude_mask] = 0
closes[exclude_mask] = 0
volumes[exclude_mask] = 0
return opens, highs, lows, closes, volumes
class BcolzMinuteBarMetadata(object):
"""
Parameters
----------
ohlc_ratio : int
The factor by which the pricing data is multiplied so that the
float data can be stored as an integer.
calendar : trading_calendars.trading_calendar.TradingCalendar
The TradingCalendar on which the minute bars are based.
start_session : datetime
The first trading session in the data set.
end_session : datetime
The last trading session in the data set.
minutes_per_day : int
The number of minutes per each period.
"""
FORMAT_VERSION = 3
METADATA_FILENAME = 'metadata.json'
@classmethod
def metadata_path(cls, rootdir):
return os.path.join(rootdir, cls.METADATA_FILENAME)
@classmethod
def read(cls, rootdir):
path = cls.metadata_path(rootdir)
with open(path) as fp:
raw_data = json.load(fp)
try:
version = raw_data['version']
except KeyError:
# Version was first written with version 1, assume 0,
# if version does not match.
version = 0
default_ohlc_ratio = raw_data['ohlc_ratio']
if version >= 1:
minutes_per_day = raw_data['minutes_per_day']
else:
# version 0 always assumed US equities.
minutes_per_day = US_EQUITIES_MINUTES_PER_DAY
if version >= 2:
calendar = get_calendar(raw_data['calendar_name'])
start_session = pd.Timestamp(
raw_data['start_session'], tz='UTC')
end_session = pd.Timestamp(raw_data['end_session'], tz='UTC')
else:
# No calendar info included in older versions, so
# default to NYSE.
calendar = get_calendar('XNYS')
start_session = pd.Timestamp(
raw_data['first_trading_day'], tz='UTC')
end_session = calendar.minute_to_session_label(
pd.Timestamp(
raw_data['market_closes'][-1], unit='m', tz='UTC')
)
if version >= 3:
ohlc_ratios_per_sid = raw_data['ohlc_ratios_per_sid']
if ohlc_ratios_per_sid is not None:
ohlc_ratios_per_sid = keymap(int, ohlc_ratios_per_sid)
else:
ohlc_ratios_per_sid = None
return cls(
default_ohlc_ratio,
ohlc_ratios_per_sid,
calendar,
start_session,
end_session,
minutes_per_day,
version=version,
)
def __init__(
self,
default_ohlc_ratio,
ohlc_ratios_per_sid,
calendar,
start_session,
end_session,
minutes_per_day,
version=FORMAT_VERSION,
):
self.calendar = calendar
self.start_session = start_session
self.end_session = end_session
self.default_ohlc_ratio = default_ohlc_ratio
self.ohlc_ratios_per_sid = ohlc_ratios_per_sid
self.minutes_per_day = minutes_per_day
self.version = version
def write(self, rootdir):
"""
Write the metadata to a JSON file in the rootdir.
Values contained in the metadata are:
version : int
The value of FORMAT_VERSION of this class.
ohlc_ratio : int
The default ratio by which to multiply the pricing data to
convert the floats from floats to an integer to fit within
the np.uint32. If ohlc_ratios_per_sid is None or does not
contain a mapping for a given sid, this ratio is used.
ohlc_ratios_per_sid : dict
A dict mapping each sid in the output to the factor by
which the pricing data is multiplied so that the float data
can be stored as an integer.
minutes_per_day : int
The number of minutes per each period.
calendar_name : str
The name of the TradingCalendar on which the minute bars are
based.
start_session : datetime
'YYYY-MM-DD' formatted representation of the first trading
session in the data set.
end_session : datetime
'YYYY-MM-DD' formatted representation of the last trading
session in the data set.
Deprecated, but included for backwards compatibility:
first_trading_day : string
'YYYY-MM-DD' formatted representation of the first trading day
available in the dataset.
market_opens : list
List of int64 values representing UTC market opens as
minutes since epoch.
market_closes : list
List of int64 values representing UTC market closes as
minutes since epoch.
"""
calendar = self.calendar
slicer = calendar.schedule.index.slice_indexer(
self.start_session,
self.end_session,
)
schedule = calendar.schedule[slicer]
market_opens = schedule.market_open
market_closes = schedule.market_close
metadata = {
'version': self.version,
'ohlc_ratio': self.default_ohlc_ratio,
'ohlc_ratios_per_sid': self.ohlc_ratios_per_sid,
'minutes_per_day': self.minutes_per_day,
'calendar_name': self.calendar.name,
'start_session': str(self.start_session.date()),
'end_session': str(self.end_session.date()),
# Write these values for backwards compatibility
'first_trading_day': str(self.start_session.date()),
'market_opens': (
market_opens.values.astype('datetime64[m]').
astype(np.int64).tolist()),
'market_closes': (
market_closes.values.astype('datetime64[m]').
astype(np.int64).tolist()),
}
with open(self.metadata_path(rootdir), 'w+') as fp:
json.dump(metadata, fp)
class BcolzMinuteBarWriter(object):
"""
Class capable of writing minute OHLCV data to disk into bcolz format.
Parameters
----------
rootdir : string
Path to the root directory into which to write the metadata and
bcolz subdirectories.
calendar : trading_calendars.trading_calendar.TradingCalendar
The trading calendar on which to base the minute bars. Used to
get the market opens used as a starting point for each periodic
span of minutes in the index, and the market closes that
correspond with the market opens.
minutes_per_day : int
The number of minutes per each period. Defaults to 390, the mode
of minutes in NYSE trading days.
start_session : datetime
The first trading session in the data set.
end_session : datetime
The last trading session in the data set.
default_ohlc_ratio : int, optional
The default ratio by which to multiply the pricing data to
convert from floats to integers that fit within np.uint32. If
ohlc_ratios_per_sid is None or does not contain a mapping for a
given sid, this ratio is used. Default is OHLC_RATIO (1000).
ohlc_ratios_per_sid : dict, optional
A dict mapping each sid in the output to the ratio by which to
multiply the pricing data to convert the floats from floats to
an integer to fit within the np.uint32.
expectedlen : int, optional
The expected length of the dataset, used when creating the initial
bcolz ctable.
If the expectedlen is not used, the chunksize and corresponding
compression ratios are not ideal.
Defaults to supporting 15 years of NYSE equity market data.
see: http://bcolz.blosc.org/opt-tips.html#informing-about-the-length-of-your-carrays # noqa
write_metadata : bool, optional
If True, writes the minute bar metadata (on init of the writer).
If False, no metadata is written (existing metadata is
retained). Default is True.
Notes
-----
Writes a bcolz directory for each individual sid, all contained within
a root directory which also contains metadata about the entire dataset.
Each individual asset's data is stored as a bcolz table with a column for
each pricing field: (open, high, low, close, volume)
The open, high, low, and close columns are integers which are 1000 times
the quoted price, so that the data can represented and stored as an
np.uint32, supporting market prices quoted up to the thousands place.
volume is a np.uint32 with no mutation of the tens place.
The 'index' for each individual asset are a repeating period of minutes of
length `minutes_per_day` starting from each market open.
The file format does not account for half-days.
e.g.:
2016-01-19 14:31
2016-01-19 14:32
...
2016-01-19 20:59
2016-01-19 21:00
2016-01-20 14:31
2016-01-20 14:32
...
2016-01-20 20:59
2016-01-20 21:00
All assets are written with a common 'index', sharing a common first
trading day. Assets that do not begin trading until after the first trading
day will have zeros for all pricing data up and until data is traded.
'index' is in quotations, because bcolz does not provide an index. The
format allows index-like behavior by writing each minute's data into the
corresponding position of the enumeration of the aforementioned datetime
index.
The datetimes which correspond to each position are written in the metadata
as integer nanoseconds since the epoch into the `minute_index` key.
See Also
--------
zipline.data.minute_bars.BcolzMinuteBarReader
"""
COL_NAMES = ('open', 'high', 'low', 'close', 'volume')
def __init__(self,
rootdir,
calendar,
start_session,
end_session,
minutes_per_day,
default_ohlc_ratio=OHLC_RATIO,
ohlc_ratios_per_sid=None,
expectedlen=DEFAULT_EXPECTEDLEN,
write_metadata=True):
self._rootdir = rootdir
self._start_session = start_session
self._end_session = end_session
self._calendar = calendar
slicer = (
calendar.schedule.index.slice_indexer(start_session, end_session))
self._schedule = calendar.schedule[slicer]
self._session_labels = self._schedule.index
self._minutes_per_day = minutes_per_day
self._expectedlen = expectedlen
self._default_ohlc_ratio = default_ohlc_ratio
self._ohlc_ratios_per_sid = ohlc_ratios_per_sid
self._minute_index = _calc_minute_index(
self._schedule.market_open, self._minutes_per_day)
if write_metadata:
metadata = BcolzMinuteBarMetadata(
self._default_ohlc_ratio,
self._ohlc_ratios_per_sid,
self._calendar,
self._start_session,
self._end_session,
self._minutes_per_day,
)
metadata.write(self._rootdir)
@classmethod
def open(cls, rootdir, end_session=None):
"""
Open an existing ``rootdir`` for writing.
Parameters
----------
end_session : Timestamp (optional)
When appending, the intended new ``end_session``.
"""
metadata = BcolzMinuteBarMetadata.read(rootdir)
return BcolzMinuteBarWriter(
rootdir,
metadata.calendar,
metadata.start_session,
end_session if end_session is not None else metadata.end_session,
metadata.minutes_per_day,
metadata.default_ohlc_ratio,
metadata.ohlc_ratios_per_sid,
write_metadata=end_session is not None
)
@property
def first_trading_day(self):
return self._start_session
def ohlc_ratio_for_sid(self, sid):
if self._ohlc_ratios_per_sid is not None:
try:
return self._ohlc_ratios_per_sid[sid]
except KeyError:
pass
# If no ohlc_ratios_per_sid dict is passed, or if the specified
# sid is not in the dict, fallback to the general ohlc_ratio.
return self._default_ohlc_ratio
def sidpath(self, sid):
"""
Parameters
----------
sid : int
Asset identifier.
Returns
-------
out : string
Full path to the bcolz rootdir for the given sid.
"""
sid_subdir = _sid_subdir_path(sid)
return join(self._rootdir, sid_subdir)
def last_date_in_output_for_sid(self, sid):
"""
Parameters
----------
sid : int
Asset identifier.
Returns
-------
out : pd.Timestamp
The midnight of the last date written in to the output for the
given sid.
"""
sizes_path = "{0}/close/meta/sizes".format(self.sidpath(sid))
if not os.path.exists(sizes_path):
return pd.NaT
with open(sizes_path, mode='r') as f:
sizes = f.read()
data = json.loads(sizes)
# use integer division so that the result is an int
# for pandas index later https://github.com/pandas-dev/pandas/blob/master/pandas/tseries/base.py#L247 # noqa
num_days = data['shape'][0] // self._minutes_per_day
if num_days == 0:
# empty container
return pd.NaT
return self._session_labels[num_days - 1]
def _init_ctable(self, path):
"""
Create empty ctable for given path.
Parameters
----------
path : string
The path to rootdir of the new ctable.
"""
# Only create the containing subdir on creation.
# This is not to be confused with the `.bcolz` directory, but is the
# directory up one level from the `.bcolz` directories.
sid_containing_dirname = os.path.dirname(path)
if not os.path.exists(sid_containing_dirname):
# Other sids may have already created the containing directory.
os.makedirs(sid_containing_dirname)
initial_array = np.empty(0, np.uint32)
table = ctable(
rootdir=path,
columns=[
initial_array,
initial_array,
initial_array,
initial_array,
initial_array,
],
names=[
'open',
'high',
'low',
'close',
'volume'
],
expectedlen=self._expectedlen,
mode='w',
)
table.flush()
return table
def _ensure_ctable(self, sid):
"""Ensure that a ctable exists for ``sid``, then return it."""
sidpath = self.sidpath(sid)
if not os.path.exists(sidpath):
return self._init_ctable(sidpath)
return bcolz.ctable(rootdir=sidpath, mode='a')
def _zerofill(self, table, numdays):
# Compute the number of minutes to be filled, accounting for the
# possibility of a partial day's worth of minutes existing for
# the previous day.
minute_offset = len(table) % self._minutes_per_day
num_to_prepend = numdays * self._minutes_per_day - minute_offset
prepend_array = np.zeros(num_to_prepend, np.uint32)
# Fill all OHLCV with zeros.
table.append([prepend_array] * 5)
table.flush()
def pad(self, sid, date):
"""
Fill sid container with empty data through the specified date.
If the last recorded trade is not at the close, then that day will be
padded with zeros until its close. Any day after that (up to and
including the specified date) will be padded with `minute_per_day`
worth of zeros
Parameters
----------
sid : int
The asset identifier for the data being written.
date : datetime-like
The date used to calculate how many slots to be pad.
The padding is done through the date, i.e. after the padding is
done the `last_date_in_output_for_sid` will be equal to `date`
"""
table = self._ensure_ctable(sid)
last_date = self.last_date_in_output_for_sid(sid)
tds = self._session_labels
if date <= last_date or date < tds[0]:
# No need to pad.
return
if pd.isnull(last_date):
# If there is no data, determine how many days to add so that
# desired days are written to the correct slots.
days_to_zerofill = tds[tds.slice_indexer(end=date)]
else:
days_to_zerofill = tds[tds.slice_indexer(
start=last_date + tds.freq,
end=date)]
self._zerofill(table, len(days_to_zerofill))
new_last_date = self.last_date_in_output_for_sid(sid)
assert new_last_date == date, "new_last_date={0} != date={1}".format(
new_last_date, date)
def set_sid_attrs(self, sid, **kwargs):
"""Write all the supplied kwargs as attributes of the sid's file.
"""
table = self._ensure_ctable(sid)
for k, v in kwargs.items():
table.attrs[k] = v
def write(self, data, show_progress=False, invalid_data_behavior='warn'):
"""Write a stream of minute data.
Parameters
----------
data : iterable[(int, pd.DataFrame)]
The data to write. Each element should be a tuple of sid, data
where data has the following format:
columns : ('open', 'high', 'low', 'close', 'volume')
open : float64
high : float64
low : float64
close : float64
volume : float64|int64
index : DatetimeIndex of market minutes.
A given sid may appear more than once in ``data``; however,
the dates must be strictly increasing.
show_progress : bool, optional
Whether or not to show a progress bar while writing.
"""
ctx = maybe_show_progress(
data,
show_progress=show_progress,
item_show_func=lambda e: e if e is None else str(e[0]),
label="Merging minute equity files:",
)
write_sid = self.write_sid
with ctx as it:
for e in it:
write_sid(*e, invalid_data_behavior=invalid_data_behavior)
def write_sid(self, sid, df, invalid_data_behavior='warn'):
"""
Write the OHLCV data for the given sid.
If there is no bcolz ctable yet created for the sid, create it.
If the length of the bcolz ctable is not exactly to the date before
the first day provided, fill the ctable with 0s up to that date.
Parameters
----------
sid : int
The asset identifer for the data being written.
df : pd.DataFrame
DataFrame of market data with the following characteristics.
columns : ('open', 'high', 'low', 'close', 'volume')
open : float64
high : float64
low : float64
close : float64
volume : float64|int64
index : DatetimeIndex of market minutes.
"""
cols = {
'open': df.open.values,
'high': df.high.values,
'low': df.low.values,
'close': df.close.values,
'volume': df.volume.values,
}
dts = df.index.values
# Call internal method, since DataFrame has already ensured matching
# index and value lengths.
self._write_cols(sid, dts, cols, invalid_data_behavior)
def write_cols(self, sid, dts, cols, invalid_data_behavior='warn'):
"""
Write the OHLCV data for the given sid.
If there is no bcolz ctable yet created for the sid, create it.
If the length of the bcolz ctable is not exactly to the date before
the first day provided, fill the ctable with 0s up to that date.
Parameters
----------
sid : int
The asset identifier for the data being written.
dts : datetime64 array
The dts corresponding to values in cols.
cols : dict of str -> np.array
dict of market data with the following characteristics.
keys are ('open', 'high', 'low', 'close', 'volume')
open : float64
high : float64
low : float64
close : float64
volume : float64|int64
"""
if not all(len(dts) == len(cols[name]) for name in self.COL_NAMES):
raise BcolzMinuteWriterColumnMismatch(
"Length of dts={0} should match cols: {1}".format(
len(dts),
" ".join("{0}={1}".format(name, len(cols[name]))
for name in self.COL_NAMES)))
self._write_cols(sid, dts, cols, invalid_data_behavior)
def _write_cols(self, sid, dts, cols, invalid_data_behavior):
"""
Internal method for `write_cols` and `write`.
Parameters
----------
sid : int
The asset identifier for the data being written.
dts : datetime64 array
The dts corresponding to values in cols.
cols : dict of str -> np.array
dict of market data with the following characteristics.
keys are ('open', 'high', 'low', 'close', 'volume')
open : float64
high : float64
low : float64
close : float64
volume : float64|int64
"""
table = self._ensure_ctable(sid)
tds = self._session_labels
input_first_day = self._calendar.minute_to_session_label(
pd.Timestamp(dts[0]), direction='previous')
last_date = self.last_date_in_output_for_sid(sid)
day_before_input = input_first_day - tds.freq
self.pad(sid, day_before_input)
table = self._ensure_ctable(sid)
# Get the number of minutes already recorded in this sid's ctable
num_rec_mins = table.size
all_minutes = self._minute_index
# Get the latest minute we wish to write to the ctable
last_minute_to_write = pd.Timestamp(dts[-1])
if not last_minute_to_write.tzname():
last_minute_to_write = last_minute_to_write.tz_localize('utc')
# In the event that we've already written some minutely data to the
# ctable, guard against overwriting that data.
if num_rec_mins > 0:
last_recorded_minute = all_minutes[num_rec_mins - 1]
if last_minute_to_write <= last_recorded_minute:
raise BcolzMinuteOverlappingData(dedent("""
Data with last_date={0} already includes input start={1} for
sid={2}""".strip()).format(last_date, input_first_day, sid))
latest_min_count = all_minutes.get_loc(last_minute_to_write)
# Get all the minutes we wish to write (all market minutes after the
# latest currently written, up to and including last_minute_to_write)
all_minutes_in_window = all_minutes[num_rec_mins:latest_min_count + 1]
minutes_count = all_minutes_in_window.size
open_col = np.zeros(minutes_count, dtype=np.uint32)
high_col = np.zeros(minutes_count, dtype=np.uint32)
low_col = np.zeros(minutes_count, dtype=np.uint32)
close_col = np.zeros(minutes_count, dtype=np.uint32)
vol_col = np.zeros(minutes_count, dtype=np.uint32)
dt_ixs = np.searchsorted(all_minutes_in_window.values,
dts.astype('datetime64[ns]'))
ohlc_ratio = self.ohlc_ratio_for_sid(sid)
(
open_col[dt_ixs],
high_col[dt_ixs],
low_col[dt_ixs],
close_col[dt_ixs],
vol_col[dt_ixs],
) = convert_cols(cols, ohlc_ratio, sid, invalid_data_behavior)
table.append([
open_col,
high_col,
low_col,
close_col,
vol_col
])
table.flush()
def data_len_for_day(self, day):
"""
Return the number of data points up to and including the
provided day.
"""
day_ix = self._session_labels.get_loc(day)
# Add one to the 0-indexed day_ix to get the number of days.
num_days = day_ix + 1
return num_days * self._minutes_per_day
def truncate(self, date):
"""Truncate data beyond this date in all ctables."""
truncate_slice_end = self.data_len_for_day(date)
glob_path = os.path.join(self._rootdir, "*", "*", "*.bcolz")
sid_paths = sorted(glob(glob_path))
for sid_path in sid_paths:
file_name = os.path.basename(sid_path)
try:
table = bcolz.open(rootdir=sid_path)
except IOError:
continue
if table.len <= truncate_slice_end:
logger.info("{0} not past truncate date={1}.", file_name, date)
continue
logger.info(
"Truncating {0} at end_date={1}", file_name, date.date()
)
table.resize(truncate_slice_end)
# Update end session in metadata.
metadata = BcolzMinuteBarMetadata.read(self._rootdir)
metadata.end_session = date
metadata.write(self._rootdir)
class BcolzMinuteBarReader(MinuteBarReader):
"""
Reader for data written by BcolzMinuteBarWriter
Parameters
----------
rootdir : string
The root directory containing the metadata and asset bcolz
directories.
See Also
--------
zipline.data.minute_bars.BcolzMinuteBarWriter
"""
FIELDS = ('open', 'high', 'low', 'close', 'volume')
DEFAULT_MINUTELY_SID_CACHE_SIZES = {
'close': 3000,
'open': 1550,
'high': 1550,
'low': 1550,
'volume': 1550,
}
assert set(FIELDS) == set(DEFAULT_MINUTELY_SID_CACHE_SIZES), \
"FIELDS should match DEFAULT_MINUTELY_SID_CACHE_SIZES keys"
# Wrap the defaults in proxy so that we don't accidentally mutate them in
# place in the constructor. If a user wants to change the defaults, they
# can do so by mutating DEFAULT_MINUTELY_SID_CACHE_SIZES.
_default_proxy = mappingproxy(DEFAULT_MINUTELY_SID_CACHE_SIZES)
def __init__(self, rootdir, sid_cache_sizes=_default_proxy):
self._rootdir = rootdir
metadata = self._get_metadata()
self._start_session = metadata.start_session
self._end_session = metadata.end_session
self.calendar = metadata.calendar
slicer = self.calendar.schedule.index.slice_indexer(
self._start_session,
self._end_session,
)
self._schedule = self.calendar.schedule[slicer]
self._market_opens = self._schedule.market_open
self._market_open_values = self._market_opens.values.\
astype('datetime64[m]').astype(np.int64)
self._market_closes = self._schedule.market_close
self._market_close_values = self._market_closes.values.\
astype('datetime64[m]').astype(np.int64)
self._default_ohlc_inverse = 1.0 / metadata.default_ohlc_ratio
ohlc_ratios = metadata.ohlc_ratios_per_sid
if ohlc_ratios:
self._ohlc_inverses_per_sid = (
valmap(lambda x: 1.0 / x, ohlc_ratios))
else:
self._ohlc_inverses_per_sid = None
self._minutes_per_day = metadata.minutes_per_day
self._carrays = {
field: LRU(sid_cache_sizes[field])
for field in self.FIELDS
}
self._last_get_value_dt_position = None
self._last_get_value_dt_value = None
# This is to avoid any bad data or other performance-killing situation
# where there a consecutive streak of 0 (no volume) starting at an
# asset's start date.
# if asset 1 started on 2015-01-03 but its first trade is 2015-01-06
# 10:31 AM US/Eastern, this dict would store {1: 23675971},
# which is the minute epoch of that date.
self._known_zero_volume_dict = {}
def _get_metadata(self):
return BcolzMinuteBarMetadata.read(self._rootdir)
@property
def trading_calendar(self):
return self.calendar
@lazyval
def last_available_dt(self):
_, close = self.calendar.open_and_close_for_session(self._end_session)
return close
@property
def first_trading_day(self):
return self._start_session
def _ohlc_ratio_inverse_for_sid(self, sid):
if self._ohlc_inverses_per_sid is not None:
try:
return self._ohlc_inverses_per_sid[sid]
except KeyError:
pass
# If we can not get a sid-specific OHLC inverse for this sid,
# fallback to the default.
return self._default_ohlc_inverse
def _minutes_to_exclude(self):
"""
Calculate the minutes which should be excluded when a window
occurs on days which had an early close, i.e. days where the close
based on the regular period of minutes per day and the market close
do not match.
Returns
-------
List of DatetimeIndex representing the minutes to exclude because
of early closes.
"""
market_opens = self._market_opens.values.astype('datetime64[m]')
market_closes = self._market_closes.values.astype('datetime64[m]')
minutes_per_day = (market_closes - market_opens).astype(np.int64)
early_indices = np.where(
minutes_per_day != self._minutes_per_day - 1)[0]
early_opens = self._market_opens[early_indices]
early_closes = self._market_closes[early_indices]
minutes = [(market_open, early_close)
for market_open, early_close
in zip(early_opens, early_closes)]
return minutes
@lazyval
def _minute_exclusion_tree(self):
"""
Build an interval tree keyed by the start and end of each range
of positions should be dropped from windows. (These are the minutes
between an early close and the minute which would be the close based
on the regular period if there were no early close.)
The value of each node is the same start and end position stored as
a tuple.
The data is stored as such in support of a fast answer to the question,
does a given start and end position overlap any of the exclusion spans?
Returns
-------
IntervalTree containing nodes which represent the minutes to exclude
because of early closes.
"""
itree = IntervalTree()
for market_open, early_close in self._minutes_to_exclude():
start_pos = self._find_position_of_minute(early_close) + 1
end_pos = (
self._find_position_of_minute(market_open)
+
self._minutes_per_day
-
1
)
data = (start_pos, end_pos)
itree[start_pos:end_pos + 1] = data
return itree
def _exclusion_indices_for_range(self, start_idx, end_idx):
"""
Returns
-------
List of tuples of (start, stop) which represent the ranges of minutes
which should be excluded when a market minute window is requested.
"""
itree = self._minute_exclusion_tree
if itree.overlaps(start_idx, end_idx):
ranges = []
intervals = itree[start_idx:end_idx]
for interval in intervals:
ranges.append(interval.data)
return sorted(ranges)
else:
return None
def _get_carray_path(self, sid, field):
sid_subdir = _sid_subdir_path(sid)
# carrays are subdirectories of the sid's rootdir
return os.path.join(self._rootdir, sid_subdir, field)
def _open_minute_file(self, field, sid):
sid = int(sid)
try:
carray = self._carrays[field][sid]
except KeyError:
try:
carray = self._carrays[field][sid] = bcolz.carray(
rootdir=self._get_carray_path(sid, field),
mode='r',
)
except IOError:
raise NoDataForSid('No minute data for sid {}.'.format(sid))
return carray
def table_len(self, sid):
"""Returns the length of the underlying table for this sid."""
return len(self._open_minute_file('close', sid))
def get_sid_attr(self, sid, name):
sid_subdir = _sid_subdir_path(sid)
sid_path = os.path.join(self._rootdir, sid_subdir)
attrs = bcolz.attrs.attrs(sid_path, 'r')
try:
return attrs[name]
except KeyError:
return None
def get_value(self, sid, dt, field):
"""
Retrieve the pricing info for the given sid, dt, and field.
Parameters
----------
sid : int
Asset identifier.
dt : datetime-like
The datetime at which the trade occurred.
field : string
The type of pricing data to retrieve.
('open', 'high', 'low', 'close', 'volume')
Returns
-------
out : float|int
The market data for the given sid, dt, and field coordinates.
For OHLC:
Returns a float if a trade occurred at the given dt.
If no trade occurred, a np.nan is returned.
For volume:
Returns the integer value of the volume.
(A volume of 0 signifies no trades for the given dt.)
"""
if self._last_get_value_dt_value == dt.value:
minute_pos = self._last_get_value_dt_position
else:
try:
minute_pos = self._find_position_of_minute(dt)
except ValueError:
raise NoDataOnDate()
self._last_get_value_dt_value = dt.value
self._last_get_value_dt_position = minute_pos
try:
value = self._open_minute_file(field, sid)[minute_pos]
except IndexError:
value = 0
if value == 0:
if field == 'volume':
return 0
else:
return np.nan
if field != 'volume':
value *= self._ohlc_ratio_inverse_for_sid(sid)
return value
def get_last_traded_dt(self, asset, dt):
minute_pos = self._find_last_traded_position(asset, dt)
if minute_pos == -1:
return pd.NaT
return self._pos_to_minute(minute_pos)
def _find_last_traded_position(self, asset, dt):
volumes = self._open_minute_file('volume', asset)
start_date_minute = asset.start_date.value / NANOS_IN_MINUTE
dt_minute = dt.value / NANOS_IN_MINUTE
try:
# if we know of a dt before which this asset has no volume,
# don't look before that dt
earliest_dt_to_search = self._known_zero_volume_dict[asset.sid]
except KeyError:
earliest_dt_to_search = start_date_minute
if dt_minute < earliest_dt_to_search:
return -1
pos = find_last_traded_position_internal(
self._market_open_values,
self._market_close_values,
dt_minute,
earliest_dt_to_search,
volumes,
self._minutes_per_day,
)
if pos == -1:
# if we didn't find any volume before this dt, save it to avoid
# work in the future.
try:
self._known_zero_volume_dict[asset.sid] = max(
dt_minute,
self._known_zero_volume_dict[asset.sid]
)
except KeyError:
self._known_zero_volume_dict[asset.sid] = dt_minute
return pos
def _pos_to_minute(self, pos):
minute_epoch = minute_value(
self._market_open_values,
pos,
self._minutes_per_day
)
return pd.Timestamp(minute_epoch, tz='UTC', unit="m")
def _find_position_of_minute(self, minute_dt):
"""
Internal method that returns the position of the given minute in the
list of every trading minute since market open of the first trading
day. Adjusts non market minutes to the last close.
ex. this method would return 1 for 2002-01-02 9:32 AM Eastern, if
2002-01-02 is the first trading day of the dataset.
Parameters
----------
minute_dt: pd.Timestamp
The minute whose position should be calculated.
Returns
-------
int: The position of the given minute in the list of all trading
minutes since market open on the first trading day.
"""
return find_position_of_minute(
self._market_open_values,
self._market_close_values,
minute_dt.value / NANOS_IN_MINUTE,
self._minutes_per_day,
False,
)
def load_raw_arrays(self, fields, start_dt, end_dt, sids):
"""
Parameters
----------
fields : list of str
'open', 'high', 'low', 'close', or 'volume'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
sids : list of int
The asset identifiers in the window.
Returns
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
start_idx = self._find_position_of_minute(start_dt)
end_idx = self._find_position_of_minute(end_dt)
num_minutes = (end_idx - start_idx + 1)
results = []
indices_to_exclude = self._exclusion_indices_for_range(
start_idx, end_idx)
if indices_to_exclude is not None:
for excl_start, excl_stop in indices_to_exclude:
length = excl_stop - excl_start + 1
num_minutes -= length
shape = num_minutes, len(sids)
for field in fields:
if field != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
for i, sid in enumerate(sids):
carray = self._open_minute_file(field, sid)
values = carray[start_idx:end_idx + 1]
if indices_to_exclude is not None:
for excl_start, excl_stop in indices_to_exclude[::-1]:
excl_slice = np.s_[
excl_start - start_idx:excl_stop - start_idx + 1]
values = np.delete(values, excl_slice)
where = values != 0
# first slice down to len(where) because we might not have
# written data for all the minutes requested
if field != 'volume':
out[:len(where), i][where] = (
values[where] * self._ohlc_ratio_inverse_for_sid(sid))
else:
out[:len(where), i][where] = values[where]
results.append(out)
return results
class MinuteBarUpdateReader(with_metaclass(ABCMeta, object)):
"""
Abstract base class for minute update readers.
"""
@abstractmethod
def read(self, dts, sids):
"""
Read and return pricing update data.
Parameters
----------
dts : DatetimeIndex
The minutes for which to read the pricing updates.
sids : iter[int]
The sids for which to read the pricing updates.
Returns
-------
data : iter[(int, DataFrame)]
Returns an iterable of ``sid`` to the corresponding OHLCV data.
"""
raise NotImplementedError()
class H5MinuteBarUpdateWriter(object):
"""
Writer for files containing minute bar updates for consumption by a writer
for a ``MinuteBarReader`` format.
Parameters
----------
path : str
The destination path.
complevel : int, optional
The HDF5 complevel, defaults to ``5``.
complib : str, optional
The HDF5 complib, defaults to ``zlib``.
"""
FORMAT_VERSION = 0
_COMPLEVEL = 5
_COMPLIB = 'zlib'
def __init__(self, path, complevel=None, complib=None):
self._complevel = complevel if complevel \
is not None else self._COMPLEVEL
self._complib = complib if complib \
is not None else self._COMPLIB
self._path = path
def write(self, frames):
"""
Write the frames to the target HDF5 file, using the format used by
``pd.Panel.to_hdf``
Parameters
----------
frames : iter[(int, DataFrame)] or dict[int -> DataFrame]
An iterable or other mapping of sid to the corresponding OHLCV
pricing data.
"""
with HDFStore(self._path, 'w',
complevel=self._complevel, complib=self._complib) \
as store:
panel = pd.concat(dict(frames), axis=1)
panel.to_hdf(store, 'updates')
with tables.open_file(self._path, mode='r+') as h5file:
h5file.set_node_attr('/', 'version', 0)
class H5MinuteBarUpdateReader(MinuteBarUpdateReader):
"""
Reader for minute bar updates stored in HDF5 files.
Parameters
----------
path : str
The path of the HDF5 file from which to source data.
"""
def __init__(self, path):
try:
self._panel = pd.read_hdf(path)
return
except TypeError:
pass
# There is a bug in `pandas.read_hdf` whereby in Python 3 it fails to
# read the timezone attr of an h5 file if that file was written in
# Python 2. Until zipline has dropped Python 2 entirely we are at risk
# of hitting this issue. For now, use h5py to read the file instead.
# The downside of using h5py directly is that we need to interpret the
# attrs manually when creating our panel (specifically the tz attr),
# but since we know exactly how the file was written this should be
# pretty straightforward.
with h5py.File(path, 'r') as f:
updates = f['updates']
values = updates['block0_values']
items = updates['axis0']
major = updates['axis1']
minor = updates['axis2']
# Our current version of h5py is unable to read the tz attr in the
# tests as it was written by HDFStore. This is fixed in version
# 2.10.0 of h5py, but that requires >=Python3.7 on conda, so until
# then we should be safe to assume UTC.
try:
tz = major.attrs['tz'].decode()
except OSError:
tz = 'UTC'
self._panel = pd.Panel(
data=np.array(values).T,
items=np.array(items),
major_axis=pd.DatetimeIndex(major, tz=tz, freq='T'),
minor_axis=np.array(minor).astype('U'),
)
def read(self, dts, sids):
result = []
for sid in sids:
result.append((sid, self._panel[sid].loc[dts]))
return iter(result) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/minute_bars.py | minute_bars.py |
from operator import mul
from logbook import Logger
import numpy as np
from numpy import float64, int64, nan
import pandas as pd
from pandas import isnull
from six import iteritems
from six.moves import reduce
from zipline.assets import (
Asset,
AssetConvertible,
Equity,
Future,
PricingDataAssociable,
)
from zipline.assets.continuous_futures import ContinuousFuture
from zipline.data.continuous_future_reader import (
ContinuousFutureSessionBarReader,
ContinuousFutureMinuteBarReader
)
from zipline.assets.roll_finder import (
CalendarRollFinder,
VolumeRollFinder
)
from zipline.data.dispatch_bar_reader import (
AssetDispatchMinuteBarReader,
AssetDispatchSessionBarReader
)
from zipline.data.resample import (
DailyHistoryAggregator,
ReindexMinuteBarReader,
ReindexSessionBarReader,
)
from zipline.data.history_loader import (
DailyHistoryLoader,
MinuteHistoryLoader,
)
from zipline.data.bar_reader import NoDataOnDate
from zipline.utils.math_utils import (
nansum,
nanmean,
nanstd
)
from zipline.utils.memoize import remember_last, weak_lru_cache
from zipline.utils.pandas_utils import (
normalize_date,
timedelta_to_integral_minutes,
)
from zipline.errors import HistoryWindowStartsBeforeData
log = Logger('DataPortal')
BASE_FIELDS = frozenset([
"open",
"high",
"low",
"close",
"volume",
"price",
"contract",
"sid",
"last_traded",
])
OHLCV_FIELDS = frozenset([
"open", "high", "low", "close", "volume"
])
OHLCVP_FIELDS = frozenset([
"open", "high", "low", "close", "volume", "price"
])
HISTORY_FREQUENCIES = set(["1m", "1d"])
DEFAULT_MINUTE_HISTORY_PREFETCH = 1560
DEFAULT_DAILY_HISTORY_PREFETCH = 40
_DEF_M_HIST_PREFETCH = DEFAULT_MINUTE_HISTORY_PREFETCH
_DEF_D_HIST_PREFETCH = DEFAULT_DAILY_HISTORY_PREFETCH
class DataPortal(object):
"""Interface to all of the data that a zipline simulation needs.
This is used by the simulation runner to answer questions about the data,
like getting the prices of assets on a given day or to service history
calls.
Parameters
----------
asset_finder : zipline.assets.assets.AssetFinder
The AssetFinder instance used to resolve assets.
trading_calendar: zipline.utils.calendar.exchange_calendar.TradingCalendar
The calendar instance used to provide minute->session information.
first_trading_day : pd.Timestamp
The first trading day for the simulation.
equity_daily_reader : BcolzDailyBarReader, optional
The daily bar reader for equities. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
equity_minute_reader : BcolzMinuteBarReader, optional
The minute bar reader for equities. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
future_daily_reader : BcolzDailyBarReader, optional
The daily bar ready for futures. This will be used to service
daily data backtests or daily history calls in a minute backetest.
If a daily bar reader is not provided but a minute bar reader is,
the minutes will be rolled up to serve the daily requests.
future_minute_reader : BcolzFutureMinuteBarReader, optional
The minute bar reader for futures. This will be used to service
minute data backtests or minute history calls. This can be used
to serve daily calls if no daily bar reader is provided.
adjustment_reader : SQLiteAdjustmentWriter, optional
The adjustment reader. This is used to apply splits, dividends, and
other adjustment data to the raw data from the readers.
last_available_session : pd.Timestamp, optional
The last session to make available in session-level data.
last_available_minute : pd.Timestamp, optional
The last minute to make available in minute-level data.
"""
def __init__(self,
asset_finder,
trading_calendar,
first_trading_day,
equity_daily_reader=None,
equity_minute_reader=None,
future_daily_reader=None,
future_minute_reader=None,
adjustment_reader=None,
last_available_session=None,
last_available_minute=None,
minute_history_prefetch_length=_DEF_M_HIST_PREFETCH,
daily_history_prefetch_length=_DEF_D_HIST_PREFETCH):
self.trading_calendar = trading_calendar
self.asset_finder = asset_finder
self._adjustment_reader = adjustment_reader
# caches of sid -> adjustment list
self._splits_dict = {}
self._mergers_dict = {}
self._dividends_dict = {}
# Handle extra sources, like Fetcher.
self._augmented_sources_map = {}
self._extra_source_df = None
self._first_available_session = first_trading_day
if last_available_session:
self._last_available_session = last_available_session
else:
# Infer the last session from the provided readers.
last_sessions = [
reader.last_available_dt
for reader in [equity_daily_reader, future_daily_reader]
if reader is not None
]
if last_sessions:
self._last_available_session = min(last_sessions)
else:
self._last_available_session = None
if last_available_minute:
self._last_available_minute = last_available_minute
else:
# Infer the last minute from the provided readers.
last_minutes = [
reader.last_available_dt
for reader in [equity_minute_reader, future_minute_reader]
if reader is not None
]
if last_minutes:
self._last_available_minute = max(last_minutes)
else:
self._last_available_minute = None
aligned_equity_minute_reader = self._ensure_reader_aligned(
equity_minute_reader)
aligned_equity_session_reader = self._ensure_reader_aligned(
equity_daily_reader)
aligned_future_minute_reader = self._ensure_reader_aligned(
future_minute_reader)
aligned_future_session_reader = self._ensure_reader_aligned(
future_daily_reader)
self._roll_finders = {
'calendar': CalendarRollFinder(self.trading_calendar,
self.asset_finder),
}
aligned_minute_readers = {}
aligned_session_readers = {}
if aligned_equity_minute_reader is not None:
aligned_minute_readers[Equity] = aligned_equity_minute_reader
if aligned_equity_session_reader is not None:
aligned_session_readers[Equity] = aligned_equity_session_reader
if aligned_future_minute_reader is not None:
aligned_minute_readers[Future] = aligned_future_minute_reader
aligned_minute_readers[ContinuousFuture] = \
ContinuousFutureMinuteBarReader(
aligned_future_minute_reader,
self._roll_finders,
)
if aligned_future_session_reader is not None:
aligned_session_readers[Future] = aligned_future_session_reader
self._roll_finders['volume'] = VolumeRollFinder(
self.trading_calendar,
self.asset_finder,
aligned_future_session_reader,
)
aligned_session_readers[ContinuousFuture] = \
ContinuousFutureSessionBarReader(
aligned_future_session_reader,
self._roll_finders,
)
_dispatch_minute_reader = AssetDispatchMinuteBarReader(
self.trading_calendar,
self.asset_finder,
aligned_minute_readers,
self._last_available_minute,
)
_dispatch_session_reader = AssetDispatchSessionBarReader(
self.trading_calendar,
self.asset_finder,
aligned_session_readers,
self._last_available_session,
)
self._pricing_readers = {
'minute': _dispatch_minute_reader,
'daily': _dispatch_session_reader,
}
self._daily_aggregator = DailyHistoryAggregator(
self.trading_calendar.schedule.market_open,
_dispatch_minute_reader,
self.trading_calendar
)
self._history_loader = DailyHistoryLoader(
self.trading_calendar,
_dispatch_session_reader,
self._adjustment_reader,
self.asset_finder,
self._roll_finders,
prefetch_length=daily_history_prefetch_length,
)
self._minute_history_loader = MinuteHistoryLoader(
self.trading_calendar,
_dispatch_minute_reader,
self._adjustment_reader,
self.asset_finder,
self._roll_finders,
prefetch_length=minute_history_prefetch_length,
)
self._first_trading_day = str(first_trading_day.date())
# Get the first trading minute
self._first_trading_minute, _ = (
self.trading_calendar.open_and_close_for_session(
self._first_trading_day
)
if self._first_trading_day is not None else (None, None)
)
# Store the locs of the first day and first minute
self._first_trading_day_loc = (
self.trading_calendar.all_sessions.get_loc(self._first_trading_day)
if self._first_trading_day is not None else None
)
def _ensure_reader_aligned(self, reader):
if reader is None:
return
if reader.trading_calendar.name == self.trading_calendar.name:
return reader
elif reader.data_frequency == 'minute':
return ReindexMinuteBarReader(
self.trading_calendar,
reader,
self._first_available_session,
self._last_available_session
)
elif reader.data_frequency == 'session':
return ReindexSessionBarReader(
self.trading_calendar,
reader,
self._first_available_session,
self._last_available_session
)
def _reindex_extra_source(self, df, source_date_index):
return df.reindex(index=source_date_index, method='ffill')
def handle_extra_source(self, source_df, sim_params):
"""
Extra sources always have a sid column.
We expand the given data (by forward filling) to the full range of
the simulation dates, so that lookup is fast during simulation.
"""
if source_df is None:
return
# Normalize all the dates in the df
source_df.index = source_df.index.normalize()
# source_df's sid column can either consist of assets we know about
# (such as sid(24)) or of assets we don't know about (such as
# palladium).
#
# In both cases, we break up the dataframe into individual dfs
# that only contain a single asset's information. ie, if source_df
# has data for PALLADIUM and GOLD, we split source_df into two
# dataframes, one for each. (same applies if source_df has data for
# AAPL and IBM).
#
# We then take each child df and reindex it to the simulation's date
# range by forward-filling missing values. this makes reads simpler.
#
# Finally, we store the data. For each column, we store a mapping in
# self.augmented_sources_map from the column to a dictionary of
# asset -> df. In other words,
# self.augmented_sources_map['days_to_cover']['AAPL'] gives us the df
# holding that data.
source_date_index = self.trading_calendar.sessions_in_range(
sim_params.start_session,
sim_params.end_session
)
# Break the source_df up into one dataframe per sid. This lets
# us (more easily) calculate accurate start/end dates for each sid,
# de-dup data, and expand the data to fit the backtest start/end date.
grouped_by_sid = source_df.groupby(["sid"])
group_names = grouped_by_sid.groups.keys()
group_dict = {}
for group_name in group_names:
group_dict[group_name] = grouped_by_sid.get_group(group_name)
# This will be the dataframe which we query to get fetcher assets at
# any given time. Get's overwritten every time there's a new fetcher
# call
extra_source_df = pd.DataFrame()
for identifier, df in iteritems(group_dict):
# Since we know this df only contains a single sid, we can safely
# de-dupe by the index (dt). If minute granularity, will take the
# last data point on any given day
df = df.groupby(level=0).last()
# Reindex the dataframe based on the backtest start/end date.
# This makes reads easier during the backtest.
df = self._reindex_extra_source(df, source_date_index)
for col_name in df.columns.difference(['sid']):
if col_name not in self._augmented_sources_map:
self._augmented_sources_map[col_name] = {}
self._augmented_sources_map[col_name][identifier] = df
# Append to extra_source_df the reindexed dataframe for the single
# sid
extra_source_df = extra_source_df.append(df)
self._extra_source_df = extra_source_df
def _get_pricing_reader(self, data_frequency):
return self._pricing_readers[data_frequency]
def get_last_traded_dt(self, asset, dt, data_frequency):
"""
Given an asset and dt, returns the last traded dt from the viewpoint
of the given dt.
If there is a trade on the dt, the answer is dt provided.
"""
return self._get_pricing_reader(data_frequency).get_last_traded_dt(
asset, dt)
@staticmethod
def _is_extra_source(asset, field, map):
"""
Internal method that determines if this asset/field combination
represents a fetcher value or a regular OHLCVP lookup.
"""
# If we have an extra source with a column called "price", only look
# at it if it's on something like palladium and not AAPL (since our
# own price data always wins when dealing with assets).
return not (field in BASE_FIELDS and
(isinstance(asset, (Asset, ContinuousFuture))))
def _get_fetcher_value(self, asset, field, dt):
day = normalize_date(dt)
try:
return \
self._augmented_sources_map[field][asset].loc[day, field]
except KeyError:
return np.NaN
def _get_single_asset_value(self,
session_label,
asset,
field,
dt,
data_frequency):
if self._is_extra_source(
asset, field, self._augmented_sources_map):
return self._get_fetcher_value(asset, field, dt)
if field not in BASE_FIELDS:
raise KeyError("Invalid column: " + str(field))
if dt < asset.start_date or \
(data_frequency == "daily" and
session_label > asset.end_date) or \
(data_frequency == "minute" and
session_label > asset.end_date):
if field == "volume":
return 0
elif field == "contract":
return None
elif field != "last_traded":
return np.NaN
if data_frequency == "daily":
if field == "contract":
return self._get_current_contract(asset, session_label)
else:
return self._get_daily_spot_value(
asset, field, session_label,
)
else:
if field == "last_traded":
return self.get_last_traded_dt(asset, dt, 'minute')
elif field == "price":
return self._get_minute_spot_value(
asset, "close", dt, ffill=True,
)
elif field == "contract":
return self._get_current_contract(asset, dt)
else:
return self._get_minute_spot_value(asset, field, dt)
def get_spot_value(self, assets, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
assets : Asset, ContinuousFuture, or iterable of same.
The asset or assets whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume',
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The spot value of ``field`` for ``asset`` The return type is based
on the ``field`` requested. If the field is one of 'open', 'high',
'low', 'close', or 'price', the value will be a float. If the
``field`` is 'volume' the value will be a int. If the ``field`` is
'last_traded' the value will be a Timestamp.
"""
assets_is_scalar = False
if isinstance(assets, (AssetConvertible, PricingDataAssociable)):
assets_is_scalar = True
else:
# If 'assets' was not one of the expected types then it should be
# an iterable.
try:
iter(assets)
except TypeError:
raise TypeError(
"Unexpected 'assets' value of type {}."
.format(type(assets))
)
session_label = self.trading_calendar.minute_to_session_label(dt)
if assets_is_scalar:
return self._get_single_asset_value(
session_label,
assets,
field,
dt,
data_frequency,
)
else:
get_single_asset_value = self._get_single_asset_value
return [
get_single_asset_value(
session_label,
asset,
field,
dt,
data_frequency,
)
for asset in assets
]
def get_scalar_asset_spot_value(self, asset, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
assets : Asset
The asset or assets whose data is desired. This cannot be
an arbitrary AssetConvertible.
field : {'open', 'high', 'low', 'close', 'volume',
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The spot value of ``field`` for ``asset`` The return type is based
on the ``field`` requested. If the field is one of 'open', 'high',
'low', 'close', or 'price', the value will be a float. If the
``field`` is 'volume' the value will be a int. If the ``field`` is
'last_traded' the value will be a Timestamp.
"""
return self._get_single_asset_value(
self.trading_calendar.minute_to_session_label(dt),
asset,
field,
dt,
data_frequency,
)
def get_adjustments(self, assets, field, dt, perspective_dt):
"""
Returns a list of adjustments between the dt and perspective_dt for the
given field and list of assets
Parameters
----------
assets : list of type Asset, or Asset
The asset, or assets whose adjustments are desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
Returns
-------
adjustments : list[Adjustment]
The adjustments to that field.
"""
if isinstance(assets, Asset):
assets = [assets]
adjustment_ratios_per_asset = []
def split_adj_factor(x):
return x if field != 'volume' else 1.0 / x
for asset in assets:
adjustments_for_asset = []
split_adjustments = self._get_adjustment_list(
asset, self._splits_dict, "SPLITS"
)
for adj_dt, adj in split_adjustments:
if dt < adj_dt <= perspective_dt:
adjustments_for_asset.append(split_adj_factor(adj))
elif adj_dt > perspective_dt:
break
if field != 'volume':
merger_adjustments = self._get_adjustment_list(
asset, self._mergers_dict, "MERGERS"
)
for adj_dt, adj in merger_adjustments:
if dt < adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
dividend_adjustments = self._get_adjustment_list(
asset, self._dividends_dict, "DIVIDENDS",
)
for adj_dt, adj in dividend_adjustments:
if dt < adj_dt <= perspective_dt:
adjustments_for_asset.append(adj)
elif adj_dt > perspective_dt:
break
ratio = reduce(mul, adjustments_for_asset, 1.0)
adjustment_ratios_per_asset.append(ratio)
return adjustment_ratios_per_asset
def get_adjusted_value(self, asset, field, dt,
perspective_dt,
data_frequency,
spot_value=None):
"""
Returns a scalar value representing the value
of the desired asset's field at the given dt with adjustments applied.
Parameters
----------
asset : Asset
The asset whose data is desired.
field : {'open', 'high', 'low', 'close', 'volume', \
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
perspective_dt : pd.Timestamp
The timestamp from which the data is being viewed back from.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The value of the given ``field`` for ``asset`` at ``dt`` with any
adjustments known by ``perspective_dt`` applied. The return type is
based on the ``field`` requested. If the field is one of 'open',
'high', 'low', 'close', or 'price', the value will be a float. If
the ``field`` is 'volume' the value will be a int. If the ``field``
is 'last_traded' the value will be a Timestamp.
"""
if spot_value is None:
# if this a fetcher field, we want to use perspective_dt (not dt)
# because we want the new value as of midnight (fetcher only works
# on a daily basis, all timestamps are on midnight)
if self._is_extra_source(asset, field,
self._augmented_sources_map):
spot_value = self.get_spot_value(asset, field, perspective_dt,
data_frequency)
else:
spot_value = self.get_spot_value(asset, field, dt,
data_frequency)
if isinstance(asset, Equity):
ratio = self.get_adjustments(asset, field, dt, perspective_dt)[0]
spot_value *= ratio
return spot_value
def _get_minute_spot_value(self, asset, column, dt, ffill=False):
reader = self._get_pricing_reader('minute')
if not ffill:
try:
return reader.get_value(asset.sid, dt, column)
except NoDataOnDate:
if column != 'volume':
return np.nan
else:
return 0
# At this point the pairing of column='close' and ffill=True is
# assumed.
try:
# Optimize the best case scenario of a liquid asset
# returning a valid price.
result = reader.get_value(asset.sid, dt, column)
if not pd.isnull(result):
return result
except NoDataOnDate:
# Handling of no data for the desired date is done by the
# forward filling logic.
# The last trade may occur on a previous day.
pass
# If forward filling, we want the last minute with values (up to
# and including dt).
query_dt = reader.get_last_traded_dt(asset, dt)
if pd.isnull(query_dt):
# no last traded dt, bail
return np.nan
result = reader.get_value(asset.sid, query_dt, column)
if (dt == query_dt) or (dt.date() == query_dt.date()):
return result
# the value we found came from a different day, so we have to
# adjust the data if there are any adjustments on that day barrier
return self.get_adjusted_value(
asset, column, query_dt,
dt, "minute", spot_value=result
)
def _get_daily_spot_value(self, asset, column, dt):
reader = self._get_pricing_reader('daily')
if column == "last_traded":
last_traded_dt = reader.get_last_traded_dt(asset, dt)
if isnull(last_traded_dt):
return pd.NaT
else:
return last_traded_dt
elif column in OHLCV_FIELDS:
# don't forward fill
try:
return reader.get_value(asset, dt, column)
except NoDataOnDate:
return np.nan
elif column == "price":
found_dt = dt
while True:
try:
value = reader.get_value(
asset, found_dt, "close"
)
if not isnull(value):
if dt == found_dt:
return value
else:
# adjust if needed
return self.get_adjusted_value(
asset, column, found_dt, dt, "minute",
spot_value=value
)
else:
found_dt -= self.trading_calendar.day
except NoDataOnDate:
return np.nan
@remember_last
def _get_days_for_window(self, end_date, bar_count):
tds = self.trading_calendar.all_sessions
end_loc = tds.get_loc(end_date)
start_loc = end_loc - bar_count + 1
if start_loc < self._first_trading_day_loc:
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day,
bar_count=bar_count,
suggested_start_day=tds[
self._first_trading_day_loc + bar_count
].date(),
)
return tds[start_loc:end_loc + 1]
def _get_history_daily_window(self,
assets,
end_dt,
bar_count,
field_to_use,
data_frequency):
"""
Internal method that returns a dataframe containing history bars
of daily frequency for the given sids.
"""
session = self.trading_calendar.minute_to_session_label(end_dt)
days_for_window = self._get_days_for_window(session, bar_count)
if len(assets) == 0:
return pd.DataFrame(None,
index=days_for_window,
columns=None)
data = self._get_history_daily_window_data(
assets, days_for_window, end_dt, field_to_use, data_frequency
)
return pd.DataFrame(
data,
index=days_for_window,
columns=assets
)
def _get_history_daily_window_data(self,
assets,
days_for_window,
end_dt,
field_to_use,
data_frequency):
if data_frequency == 'daily':
# two cases where we use daily data for the whole range:
# 1) the history window ends at midnight utc.
# 2) the last desired day of the window is after the
# last trading day, use daily data for the whole range.
return self._get_daily_window_data(
assets,
field_to_use,
days_for_window,
extra_slot=False
)
else:
# minute mode, requesting '1d'
daily_data = self._get_daily_window_data(
assets,
field_to_use,
days_for_window[0:-1]
)
if field_to_use == 'open':
minute_value = self._daily_aggregator.opens(
assets, end_dt)
elif field_to_use == 'high':
minute_value = self._daily_aggregator.highs(
assets, end_dt)
elif field_to_use == 'low':
minute_value = self._daily_aggregator.lows(
assets, end_dt)
elif field_to_use == 'close':
minute_value = self._daily_aggregator.closes(
assets, end_dt)
elif field_to_use == 'volume':
minute_value = self._daily_aggregator.volumes(
assets, end_dt)
elif field_to_use == 'sid':
minute_value = [
int(self._get_current_contract(asset, end_dt))
for asset in assets]
# append the partial day.
daily_data[-1] = minute_value
return daily_data
def _handle_minute_history_out_of_bounds(self, bar_count):
cal = self.trading_calendar
first_trading_minute_loc = (
cal.all_minutes.get_loc(
self._first_trading_minute
)
if self._first_trading_minute is not None else None
)
suggested_start_day = cal.minute_to_session_label(
cal.all_minutes[
first_trading_minute_loc + bar_count
] + cal.day
)
raise HistoryWindowStartsBeforeData(
first_trading_day=self._first_trading_day,
bar_count=bar_count,
suggested_start_day=suggested_start_day.date(),
)
def _get_history_minute_window(self, assets, end_dt, bar_count,
field_to_use):
"""
Internal method that returns a dataframe containing history bars
of minute frequency for the given sids.
"""
# get all the minutes for this window
try:
minutes_for_window = self.trading_calendar.minutes_window(
end_dt, -bar_count
)
except KeyError:
self._handle_minute_history_out_of_bounds(bar_count)
if minutes_for_window[0] < self._first_trading_minute:
self._handle_minute_history_out_of_bounds(bar_count)
asset_minute_data = self._get_minute_window_data(
assets,
field_to_use,
minutes_for_window,
)
return pd.DataFrame(
asset_minute_data,
index=minutes_for_window,
columns=assets
)
def get_history_window(self,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency,
ffill=True):
"""
Public API method that returns a dataframe containing the requested
history window. Data is fully adjusted.
Parameters
----------
assets : list of zipline.data.Asset objects
The assets whose data is desired.
bar_count: int
The number of bars desired.
frequency: string
"1d" or "1m"
field: string
The desired field of the asset.
data_frequency: string
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars.
ffill: boolean
Forward-fill missing values. Only has effect if field
is 'price'.
Returns
-------
A dataframe containing the requested data.
"""
if field not in OHLCVP_FIELDS and field != 'sid':
raise ValueError("Invalid field: {0}".format(field))
if bar_count < 1:
raise ValueError(
"bar_count must be >= 1, but got {}".format(bar_count)
)
if frequency == "1d":
if field == "price":
df = self._get_history_daily_window(assets, end_dt, bar_count,
"close", data_frequency)
else:
df = self._get_history_daily_window(assets, end_dt, bar_count,
field, data_frequency)
elif frequency == "1m":
if field == "price":
df = self._get_history_minute_window(assets, end_dt, bar_count,
"close")
else:
df = self._get_history_minute_window(assets, end_dt, bar_count,
field)
else:
raise ValueError("Invalid frequency: {0}".format(frequency))
# forward-fill price
if field == "price":
if frequency == "1m":
ffill_data_frequency = 'minute'
elif frequency == "1d":
ffill_data_frequency = 'daily'
else:
raise Exception(
"Only 1d and 1m are supported for forward-filling.")
assets_with_leading_nan = np.where(isnull(df.iloc[0]))[0]
history_start, history_end = df.index[[0, -1]]
if ffill_data_frequency == 'daily' and data_frequency == 'minute':
# When we're looking for a daily value, but we haven't seen any
# volume in today's minute bars yet, we need to use the
# previous day's ffilled daily price. Using today's daily price
# could yield a value from later today.
history_start -= self.trading_calendar.day
initial_values = []
for asset in df.columns[assets_with_leading_nan]:
last_traded = self.get_last_traded_dt(
asset,
history_start,
ffill_data_frequency,
)
if isnull(last_traded):
initial_values.append(nan)
else:
initial_values.append(
self.get_adjusted_value(
asset,
field,
dt=last_traded,
perspective_dt=history_end,
data_frequency=ffill_data_frequency,
)
)
# Set leading values for assets that were missing data, then ffill.
df.iloc[0, assets_with_leading_nan] = np.array(
initial_values,
dtype=np.float64
)
df.fillna(method='ffill', inplace=True)
# forward-filling will incorrectly produce values after the end of
# an asset's lifetime, so write NaNs back over the asset's
# end_date.
normed_index = df.index.normalize()
for asset in df.columns:
if history_end >= asset.end_date:
# if the window extends past the asset's end date, set
# all post-end-date values to NaN in that asset's series
df.loc[normed_index > asset.end_date, asset] = nan
return df
def _get_minute_window_data(self, assets, field, minutes_for_window):
"""
Internal method that gets a window of adjusted minute data for an asset
and specified date range. Used to support the history API method for
minute bars.
Missing bars are filled with NaN.
Parameters
----------
assets : iterable[Asset]
The assets whose data is desired.
field: string
The specific field to return. "open", "high", "close_price", etc.
minutes_for_window: pd.DateTimeIndex
The list of minutes representing the desired window. Each minute
is a pd.Timestamp.
Returns
-------
A numpy array with requested values.
"""
return self._minute_history_loader.history(assets,
minutes_for_window,
field,
False)
def _get_daily_window_data(self,
assets,
field,
days_in_window,
extra_slot=True):
"""
Internal method that gets a window of adjusted daily data for a sid
and specified date range. Used to support the history API method for
daily bars.
Parameters
----------
asset : Asset
The asset whose data is desired.
start_dt: pandas.Timestamp
The start of the desired window of data.
bar_count: int
The number of days of data to return.
field: string
The specific field to return. "open", "high", "close_price", etc.
extra_slot: boolean
Whether to allocate an extra slot in the returned numpy array.
This extra slot will hold the data for the last partial day. It's
much better to create it here than to create a copy of the array
later just to add a slot.
Returns
-------
A numpy array with requested values. Any missing slots filled with
nan.
"""
bar_count = len(days_in_window)
# create an np.array of size bar_count
dtype = float64 if field != 'sid' else int64
if extra_slot:
return_array = np.zeros((bar_count + 1, len(assets)), dtype=dtype)
else:
return_array = np.zeros((bar_count, len(assets)), dtype=dtype)
if field != "volume":
# volumes default to 0, so we don't need to put NaNs in the array
return_array[:] = np.NAN
if bar_count != 0:
data = self._history_loader.history(assets,
days_in_window,
field,
extra_slot)
if extra_slot:
return_array[:len(return_array) - 1, :] = data
else:
return_array[:len(data)] = data
return return_array
def _get_adjustment_list(self, asset, adjustments_dict, table_name):
"""
Internal method that returns a list of adjustments for the given sid.
Parameters
----------
asset : Asset
The asset for which to return adjustments.
adjustments_dict: dict
A dictionary of sid -> list that is used as a cache.
table_name: string
The table that contains this data in the adjustments db.
Returns
-------
adjustments: list
A list of [multiplier, pd.Timestamp], earliest first
"""
if self._adjustment_reader is None:
return []
sid = int(asset)
try:
adjustments = adjustments_dict[sid]
except KeyError:
adjustments = adjustments_dict[sid] = self._adjustment_reader.\
get_adjustments_for_sid(table_name, sid)
return adjustments
def get_splits(self, assets, dt):
"""
Returns any splits for the given sids and the given dt.
Parameters
----------
assets : container
Assets for which we want splits.
dt : pd.Timestamp
The date for which we are checking for splits. Note: this is
expected to be midnight UTC.
Returns
-------
splits : list[(asset, float)]
List of splits, where each split is a (asset, ratio) tuple.
"""
if self._adjustment_reader is None or not assets:
return []
# convert dt to # of seconds since epoch, because that's what we use
# in the adjustments db
seconds = int(dt.value / 1e9)
c = self._adjustment_reader.conn.cursor()
c.execute(f"SELECT sid, ratio FROM SPLITS WHERE effective_date = {seconds}")
splits = c.fetchall()
splits = [split for split in splits if split[0] in assets]
splits = [(self.asset_finder.retrieve_asset(split[0]), split[1])
for split in splits]
return splits
def get_stock_dividends(self, sid, trading_days):
"""
Returns all the stock dividends for a specific sid that occur
in the given trading range.
Parameters
----------
sid: int
The asset whose stock dividends should be returned.
trading_days: pd.DatetimeIndex
The trading range.
Returns
-------
list: A list of objects with all relevant attributes populated.
All timestamp fields are converted to pd.Timestamps.
"""
if self._adjustment_reader is None:
return []
if len(trading_days) == 0:
return []
start_dt = trading_days[0].value / 1e9
end_dt = trading_days[-1].value / 1e9
dividends = self._adjustment_reader.conn.execute(
"SELECT * "
"FROM stock_dividend_payouts WHERE sid = ? AND "
"ex_date > ? AND pay_date < ?", (int(sid), start_dt, end_dt,)).\
fetchall()
dividend_info = []
for dividend_tuple in dividends:
dividend_info.append({
"sid": dividend_tuple[1],
"payment_sid": dividend_tuple[2],
"ratio": dividend_tuple[3],
"declared_date": dividend_tuple[4],
"ex_date": pd.Timestamp(dividend_tuple[5], unit="s"),
"record_date": pd.Timestamp(dividend_tuple[6], unit="s"),
"pay_date": pd.Timestamp(dividend_tuple[7], unit="s"),
})
return dividend_info
def contains(self, asset, field):
return field in BASE_FIELDS or \
(field in self._augmented_sources_map and
asset in self._augmented_sources_map[field])
def get_fetcher_assets(self, dt):
"""
Returns a list of assets for the current date, as defined by the
fetcher data.
Returns
-------
list: a list of Asset objects.
"""
# return a list of assets for the current date, as defined by the
# fetcher source
if self._extra_source_df is None:
return []
day = normalize_date(dt)
if day in self._extra_source_df.index:
assets = self._extra_source_df.loc[day]['sid']
else:
return []
if isinstance(assets, pd.Series):
return [x for x in assets if isinstance(x, Asset)]
else:
return [assets] if isinstance(assets, Asset) else []
# cache size picked somewhat loosely. this code exists purely to
# handle deprecated API.
@weak_lru_cache(20)
def _get_minute_count_for_transform(self, ending_minute, days_count):
# This function works in three steps.
# Step 1. Count the minutes from ``ending_minute`` to the start of its
# session.
# Step 2. Count the minutes from the prior ``days_count - 1`` sessions.
# Step 3. Return the sum of the results from steps (1) and (2).
# Example (NYSE Calendar)
# ending_minute = 2016-12-28 9:40 AM US/Eastern
# days_count = 3
# Step 1. Calculate that there are 10 minutes in the ending session.
# Step 2. Calculate that there are 390 + 210 = 600 minutes in the prior
# two sessions. (Prior sessions are 2015-12-23 and 2015-12-24.)
# 2015-12-24 is a half day.
# Step 3. Return 600 + 10 = 610.
cal = self.trading_calendar
ending_session = cal.minute_to_session_label(
ending_minute,
direction="none", # It's an error to pass a non-trading minute.
)
# Assume that calendar days are always full of contiguous minutes,
# which means we can just take 1 + (number of minutes between the last
# minute and the start of the session). We add one so that we include
# the ending minute in the total.
ending_session_minute_count = timedelta_to_integral_minutes(
ending_minute - cal.open_and_close_for_session(ending_session)[0]
) + 1
if days_count == 1:
# We just need sessions for the active day.
return ending_session_minute_count
# XXX: We're subtracting 2 here to account for two offsets:
# 1. We only want ``days_count - 1`` sessions, since we've already
# accounted for the ending session above.
# 2. The API of ``sessions_window`` is to return one more session than
# the requested number. I don't think any consumers actually want
# that behavior, but it's the tested and documented behavior right
# now, so we have to request one less session than we actually want.
completed_sessions = cal.sessions_window(
cal.previous_session_label(ending_session),
2 - days_count,
)
completed_sessions_minute_count = (
self.trading_calendar.minutes_count_for_sessions_in_range(
completed_sessions[0],
completed_sessions[-1]
)
)
return ending_session_minute_count + completed_sessions_minute_count
def get_simple_transform(self, asset, transform_name, dt, data_frequency,
bars=None):
if transform_name == "returns":
# returns is always calculated over the last 2 days, regardless
# of the simulation's data frequency.
hst = self.get_history_window(
[asset],
dt,
2,
"1d",
"price",
data_frequency,
ffill=True,
)[asset]
return (hst.iloc[-1] - hst.iloc[0]) / hst.iloc[0]
if bars is None:
raise ValueError("bars cannot be None!")
if data_frequency == "minute":
freq_str = "1m"
calculated_bar_count = int(self._get_minute_count_for_transform(
dt, bars
))
else:
freq_str = "1d"
calculated_bar_count = bars
price_arr = self.get_history_window(
[asset],
dt,
calculated_bar_count,
freq_str,
"price",
data_frequency,
ffill=True,
)[asset]
if transform_name == "mavg":
return nanmean(price_arr)
elif transform_name == "stddev":
return nanstd(price_arr, ddof=1)
elif transform_name == "vwap":
volume_arr = self.get_history_window(
[asset],
dt,
calculated_bar_count,
freq_str,
"volume",
data_frequency,
ffill=True,
)[asset]
vol_sum = nansum(volume_arr)
try:
ret = nansum(price_arr * volume_arr) / vol_sum
except ZeroDivisionError:
ret = np.nan
return ret
def get_current_future_chain(self, continuous_future, dt):
"""
Retrieves the future chain for the contract at the given `dt` according
the `continuous_future` specification.
Returns
-------
future_chain : list[Future]
A list of active futures, where the first index is the current
contract specified by the continuous future definition, the second
is the next upcoming contract and so on.
"""
rf = self._roll_finders[continuous_future.roll_style]
session = self.trading_calendar.minute_to_session_label(dt)
contract_center = rf.get_contract_center(
continuous_future.root_symbol, session,
continuous_future.offset)
oc = self.asset_finder.get_ordered_contracts(
continuous_future.root_symbol)
chain = oc.active_chain(contract_center, session.value)
return self.asset_finder.retrieve_all(chain)
def _get_current_contract(self, continuous_future, dt):
rf = self._roll_finders[continuous_future.roll_style]
contract_sid = rf.get_contract_center(continuous_future.root_symbol,
dt,
continuous_future.offset)
if contract_sid is None:
return None
return self.asset_finder.retrieve_asset(contract_sid)
@property
def adjustment_reader(self):
return self._adjustment_reader | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/data_portal.py | data_portal.py |
from collections import namedtuple
from errno import ENOENT
from os import remove
import sqlalchemy as sa
from sqlalchemy.engine.reflection import Inspector
from sqlalchemy.exc import IntegrityError
from toolz import first
from logbook import Logger
import numpy as np
from numpy import integer as any_integer
import pandas as pd
from pandas import Timestamp
import six
import sqlite3
from zipline.utils.functional import keysorted
from zipline.utils.input_validation import preprocess
from zipline.utils.numpy_utils import (
datetime64ns_dtype,
float64_dtype,
int64_dtype,
uint32_dtype,
uint64_dtype,
)
from zipline.utils.pandas_utils import empty_dataframe
from zipline.utils.db_utils import group_into_chunks, coerce_string_to_conn
from ._adjustments import load_adjustments_from_sqlite
log = Logger(__name__)
SQLITE_ADJUSTMENT_TABLENAMES = frozenset(['splits', 'dividends', 'mergers'])
UNPAID_ALL_QUERY_TEMPLATE = """
SELECT sid, amount, pay_date, ex_date from dividend_payouts
WHERE sid IN ({0})
"""
UNPAID_QUERY_TEMPLATE = """
SELECT sid, amount, pay_date from dividend_payouts
WHERE ex_date={0} AND sid IN ({1})
"""
Dividend = namedtuple('Dividend', ['asset', 'amount', 'pay_date'])
UNPAID_ALL_STOCK_DIVIDEND_QUERY_TEMPLATE = """
SELECT sid, payment_sid, ratio, pay_date, ex_date from stock_dividend_payouts
WHERE sid IN ({0})
"""
UNPAID_STOCK_DIVIDEND_QUERY_TEMPLATE = """
SELECT sid, payment_sid, ratio, pay_date from stock_dividend_payouts
WHERE ex_date={0} AND sid IN ({1})
"""
StockDividend = namedtuple(
'StockDividend',
['asset', 'payment_asset', 'ratio', 'pay_date'],
)
SQLITE_ADJUSTMENT_COLUMN_DTYPES = {
'effective_date': any_integer,
'ratio': float64_dtype,
'sid': any_integer,
}
SQLITE_DIVIDEND_PAYOUT_COLUMN_DTYPES = {
'sid': any_integer,
'ex_date': any_integer,
'declared_date': any_integer,
'record_date': any_integer,
'pay_date': any_integer,
'amount': float,
}
SQLITE_STOCK_DIVIDEND_PAYOUT_COLUMN_DTYPES = {
'sid': any_integer,
'ex_date': any_integer,
'declared_date': any_integer,
'record_date': any_integer,
'pay_date': any_integer,
'payment_sid': any_integer,
'ratio': float,
}
def specialize_any_integer(d):
out = {}
for k, v in six.iteritems(d):
if v is any_integer:
out[k] = int64_dtype
else:
out[k] = v
return out
class SQLiteAdjustmentReader(object):
"""
Loads adjustments based on corporate actions from a SQLite database.
Expects data written in the format output by `SQLiteAdjustmentWriter`.
Parameters
----------
conn : str or sqlite3.Connection
Connection from which to load data.
See Also
--------
:class:`zipline.data.adjustments.SQLiteAdjustmentWriter`
"""
_datetime_int_cols = {
'splits': ('effective_date',),
'mergers': ('effective_date',),
'dividends': ('effective_date',),
'dividend_payouts': (
'declared_date', 'ex_date', 'pay_date', 'record_date',
),
'stock_dividend_payouts': (
'declared_date', 'ex_date', 'pay_date', 'record_date',
)
}
_raw_table_dtypes = {
# We use any_integer above to be lenient in accepting different dtypes
# from users. For our outputs, however, we always want to return the
# same types, and any_integer turns into int32 on some numpy windows
# builds, so specify int64 explicitly here.
'splits': specialize_any_integer(SQLITE_ADJUSTMENT_COLUMN_DTYPES),
'mergers': specialize_any_integer(SQLITE_ADJUSTMENT_COLUMN_DTYPES),
'dividends': specialize_any_integer(SQLITE_ADJUSTMENT_COLUMN_DTYPES),
'dividend_payouts': specialize_any_integer(
SQLITE_DIVIDEND_PAYOUT_COLUMN_DTYPES,
),
'stock_dividend_payouts': specialize_any_integer(
SQLITE_STOCK_DIVIDEND_PAYOUT_COLUMN_DTYPES,
),
}
@preprocess(conn=coerce_string_to_conn(require_exists=True))
def __init__(self, conn):
self.conn = conn
self._dividend_cache = {}
self._stock_dividend_cache = {}
def __enter__(self):
return self
def __exit__(self, *exc_info):
self.close()
def close(self):
return self.conn.close()
def load_adjustments(self,
dates,
assets,
should_include_splits,
should_include_mergers,
should_include_dividends,
adjustment_type):
"""
Load collection of Adjustment objects from underlying adjustments db.
Parameters
----------
dates : pd.DatetimeIndex
Dates for which adjustments are needed.
assets : pd.Int64Index
Assets for which adjustments are needed.
should_include_splits : bool
Whether split adjustments should be included.
should_include_mergers : bool
Whether merger adjustments should be included.
should_include_dividends : bool
Whether dividend adjustments should be included.
adjustment_type : str
Whether price adjustments, volume adjustments, or both, should be
included in the output.
Returns
-------
adjustments : dict[str -> dict[int -> Adjustment]]
A dictionary containing price and/or volume adjustment mappings
from index to adjustment objects to apply at that index.
"""
return load_adjustments_from_sqlite(
self.conn,
dates,
assets,
should_include_splits,
should_include_mergers,
should_include_dividends,
adjustment_type,
)
def load_pricing_adjustments(self, columns, dates, assets):
if 'volume' not in set(columns):
adjustment_type = 'price'
elif len(set(columns)) == 1:
adjustment_type = 'volume'
else:
adjustment_type = 'all'
adjustments = self.load_adjustments(
dates,
assets,
should_include_splits=True,
should_include_mergers=True,
should_include_dividends=True,
adjustment_type=adjustment_type,
)
price_adjustments = adjustments.get('price')
volume_adjustments = adjustments.get('volume')
return [
volume_adjustments if column == 'volume'
else price_adjustments
for column in columns
]
def get_adjustments_for_sid(self, table_name, sid):
t = (sid,)
c = self.conn.cursor()
c.execute(f'SELECT effective_date, ratio FROM {table_name} WHERE sid = {sid}')
adjustments_for_sid = c.fetchall()
c.close()
return [[Timestamp(adjustment[0], unit='s', tz='UTC'), adjustment[1]]
for adjustment in
adjustments_for_sid]
def get_dividends_with_ex_date(self, assets, date, asset_finder):
divs = []
seconds = date.value / int(1e9)
for asset in assets:
sid = int(asset)
if not sid in self._dividend_cache:
c = self.conn.cursor()
self._dividend_cache[sid] = pd.read_sql(
UNPAID_ALL_QUERY_TEMPLATE.format(sid),
self.conn,
index_col='ex_date')
try:
cached_div = self._dividend_cache[sid].loc[seconds]
div = Dividend(
asset,
cached_div['amount'],
Timestamp(cached_div['pay_date'], unit='s', tz='UTC'))
divs.append(div)
except KeyError:
pass
return divs
def get_stock_dividends_with_ex_date(self, assets, date, asset_finder):
stock_divs = []
seconds = date.value / int(1e9)
for asset in assets:
sid = int(asset)
if not sid in self._stock_dividend_cache:
c = self.conn.cursor()
self._stock_dividend_cache[sid] = pd.read_sql(
UNPAID_ALL_STOCK_DIVIDEND_QUERY_TEMPLATE.format(sid),
self.conn,
index_col='ex_date')
try:
cached_stock_div = self._stock_dividend_cache[sid].loc[seconds]
div = StockDividend(
asset,
asset_finder.retrieve_asset(cached_stock_div['payment_sid']),
cached_stock_div['ratio'],
Timestamp(cached_stock_div['pay_date'], unit='s', tz='UTC'))
stock_divs.append(div)
except KeyError:
pass
return stock_divs
def unpack_db_to_component_dfs(self, convert_dates=False):
"""Returns the set of known tables in the adjustments file in DataFrame
form.
Parameters
----------
convert_dates : bool, optional
By default, dates are returned in seconds since EPOCH. If
convert_dates is True, all ints in date columns will be converted
to datetimes.
Returns
-------
dfs : dict{str->DataFrame}
Dictionary which maps table name to the corresponding DataFrame
version of the table, where all date columns have been coerced back
from int to datetime.
"""
return {
t_name: self.get_df_from_table(t_name, convert_dates)
for t_name in self._datetime_int_cols
}
def get_df_from_table(self, table_name, convert_dates=False):
try:
date_cols = self._datetime_int_cols[table_name]
except KeyError:
raise ValueError(
"Requested table %s not found.\n"
"Available tables: %s\n" % (
table_name,
self._datetime_int_cols.keys(),
)
)
# Dates are stored in second resolution as ints in adj.db tables.
# Need to specifically convert them as UTC, not local time.
kwargs = (
{'parse_dates': {col: {'unit': 's', 'utc': True}
for col in date_cols}
}
if convert_dates
else {}
)
result = pd.read_sql(
'select * from "{}"'.format(table_name),
self.conn,
index_col='index',
**kwargs
).rename_axis(None)
if not len(result):
dtypes = self._df_dtypes(table_name, convert_dates)
return empty_dataframe(*keysorted(dtypes))
return result
def _df_dtypes(self, table_name, convert_dates):
"""Get dtypes to use when unpacking sqlite tables as dataframes.
"""
out = self._raw_table_dtypes[table_name]
if convert_dates:
out = out.copy()
for date_column in self._datetime_int_cols[table_name]:
out[date_column] = datetime64ns_dtype
return out
class SQLiteAdjustmentWriter(object):
"""
Writer for data to be read by SQLiteAdjustmentReader
Parameters
----------
conn_or_path : str or sqlite3.Connection
A handle to the target sqlite database.
equity_daily_bar_reader : SessionBarReader
Daily bar reader to use for dividend writes.
overwrite : bool, optional, default=False
If True and conn_or_path is a string, remove any existing files at the
given path before connecting.
See Also
--------
zipline.data.adjustments.SQLiteAdjustmentReader
"""
def __init__(self, conn_or_path, equity_daily_bar_reader, overwrite=False):
if isinstance(conn_or_path, sqlite3.Connection):
self.conn = conn_or_path
self.engine = False
elif isinstance(conn_or_path, six.string_types):
if not conn_or_path.startswith('postgresql://'):
if overwrite:
try:
remove(conn_or_path)
except OSError as e:
if e.errno != ENOENT:
raise
# switch to regex if we want to support other engines
if conn_or_path.startswith('postgresql://'):
self.engine = sa.create_engine(conn_or_path)
self.conn = self.engine.connect()
# not needed for sqlite
self._tables = self.ensure_tables()
else:
self.engine = False
self.conn = sqlite3.connect(conn_or_path)
self.uri = conn_or_path
else:
raise TypeError("Unknown connection type %s" % type(conn_or_path))
self._equity_daily_bar_reader = equity_daily_bar_reader
def __enter__(self):
return self
def __exit__(self, *exc_info):
self.close()
def close(self):
self.conn.close()
def _write(self, tablename, expected_dtypes, frame):
if frame is None or frame.empty:
# keeping the dtypes correct for empty frames is not easy
frame = pd.DataFrame(
np.array([], dtype=list(expected_dtypes.items())),
)
else:
if frozenset(frame.columns) != frozenset(expected_dtypes):
raise ValueError(
"Unexpected frame columns:\n"
"Expected Columns: %s\n"
"Received Columns: %s" % (
set(expected_dtypes),
frame.columns.tolist(),
)
)
actual_dtypes = frame.dtypes
for colname, expected in six.iteritems(expected_dtypes):
actual = actual_dtypes[colname]
if not np.issubdtype(actual, expected):
raise TypeError(
"Expected data of type {expected} for column"
" '{colname}', but got '{actual}'.".format(
expected=expected,
colname=colname,
actual=actual,
),
)
# in case of sqlite, use naive way of writing
if not self.engine:
frame.to_sql(
tablename,
self.conn,
if_exists='append',
chunksize=50000,
)
else:
frame.reset_index(inplace=True)
frame.drop(columns='index', inplace=True)
table = self._tables[tablename]
constr_table = table
# sqlite needs a table-string, postgres needs a table-object
if 'sqlite:///' in str(self.engine):
constr_table = str(table)
insp = Inspector.from_engine(self.engine)
constrs = insp.get_unique_constraints(constr_table)
uq_cols = set()
for constr in constrs:
for col in constr['column_names']:
uq_cols.add(col)
for i, row in frame.iterrows():
values = {}
for column in list(frame.columns):
values[column] = row[column]
try:
ins = table.insert().values(values)
self.engine.execute(ins)
except IntegrityError:
uq_col_objs = [col for col in table.columns if col.name in uq_cols]
where_cond = False
for col_obj in uq_col_objs:
if where_cond == False:
where_cond = col_obj == values[col_obj.name]
else:
where_cond = sa.and_(where_cond, col_obj == values[col_obj.name])
upd = table.update().where(where_cond).values(values)
self.engine.execute(upd)
def write_frame(self, tablename, frame):
if tablename not in SQLITE_ADJUSTMENT_TABLENAMES:
raise ValueError(
"Adjustment table %s not in %s" % (
tablename,
SQLITE_ADJUSTMENT_TABLENAMES,
)
)
if not (frame is None or frame.empty):
frame = frame.copy()
frame['effective_date'] = frame['effective_date'].values.astype(
'datetime64[s]',
).astype('int64')
return self._write(
tablename,
SQLITE_ADJUSTMENT_COLUMN_DTYPES,
frame,
)
def write_dividend_payouts(self, frame):
"""
Write dividend payout data to SQLite table `dividend_payouts`.
"""
return self._write(
'dividend_payouts',
SQLITE_DIVIDEND_PAYOUT_COLUMN_DTYPES,
frame,
)
def write_stock_dividend_payouts(self, frame):
return self._write(
'stock_dividend_payouts',
SQLITE_STOCK_DIVIDEND_PAYOUT_COLUMN_DTYPES,
frame,
)
def calc_dividend_ratios(self, dividends):
"""
Calculate the ratios to apply to equities when looking back at pricing
history so that the price is smoothed over the ex_date, when the market
adjusts to the change in equity value due to upcoming dividend.
Returns
-------
DataFrame
A frame in the same format as splits and mergers, with keys
- sid, the id of the equity
- effective_date, the date in seconds on which to apply the ratio.
- ratio, the ratio to apply to backwards looking pricing data.
"""
if dividends is None or dividends.empty:
return pd.DataFrame(np.array(
[],
dtype=[
('sid', uint64_dtype),
('effective_date', uint32_dtype),
('ratio', float64_dtype),
],
))
pricing_reader = self._equity_daily_bar_reader
input_sids = dividends.sid.values
unique_sids, sids_ix = np.unique(input_sids, return_inverse=True)
dates = pricing_reader.sessions.values
close, = pricing_reader.load_raw_arrays(
['close'],
pd.Timestamp(dates[0], tz='UTC'),
pd.Timestamp(dates[-1], tz='UTC'),
unique_sids,
)
date_ix = np.searchsorted(dates, dividends.ex_date.values)
mask = date_ix > 0
date_ix = date_ix[mask]
sids_ix = sids_ix[mask]
input_dates = dividends.ex_date.values[mask]
# subtract one day to get the close on the day prior to the merger
previous_close = close[date_ix - 1, sids_ix]
input_sids = input_sids[mask]
amount = dividends.amount.values[mask]
ratio = 1.0 - amount / previous_close
non_nan_ratio_mask = ~np.isnan(ratio)
for ix in np.flatnonzero(~non_nan_ratio_mask):
log.warn(
"Couldn't compute ratio for dividend"
" sid={sid}, ex_date={ex_date:%Y-%m-%d}, amount={amount:.3f}",
sid=input_sids[ix],
ex_date=pd.Timestamp(input_dates[ix]),
amount=amount[ix],
)
positive_ratio_mask = ratio > 0
for ix in np.flatnonzero(~positive_ratio_mask & non_nan_ratio_mask):
log.warn(
"Dividend ratio <= 0 for dividend"
" sid={sid}, ex_date={ex_date:%Y-%m-%d}, amount={amount:.3f}",
sid=input_sids[ix],
ex_date=pd.Timestamp(input_dates[ix]),
amount=amount[ix],
)
valid_ratio_mask = non_nan_ratio_mask & positive_ratio_mask
return pd.DataFrame({
'sid': input_sids[valid_ratio_mask],
'effective_date': input_dates[valid_ratio_mask],
'ratio': ratio[valid_ratio_mask],
})
def _write_dividends(self, dividends):
if dividends is None:
dividend_payouts = None
else:
dividend_payouts = dividends.copy()
dividend_payouts['ex_date'] = dividend_payouts['ex_date'].values.\
astype('datetime64[s]').astype(int64_dtype)
dividend_payouts['record_date'] = \
dividend_payouts['record_date'].values.\
astype('datetime64[s]').astype(int64_dtype)
dividend_payouts['declared_date'] = \
dividend_payouts['declared_date'].values.\
astype('datetime64[s]').astype(int64_dtype)
dividend_payouts['pay_date'] = \
dividend_payouts['pay_date'].values.astype('datetime64[s]').\
astype(int64_dtype)
self.write_dividend_payouts(dividend_payouts)
def _write_stock_dividends(self, stock_dividends):
if stock_dividends is None:
stock_dividend_payouts = None
else:
stock_dividend_payouts = stock_dividends.copy()
stock_dividend_payouts['ex_date'] = \
stock_dividend_payouts['ex_date'].values.\
astype('datetime64[s]').astype(int64_dtype)
stock_dividend_payouts['record_date'] = \
stock_dividend_payouts['record_date'].values.\
astype('datetime64[s]').astype(int64_dtype)
stock_dividend_payouts['declared_date'] = \
stock_dividend_payouts['declared_date'].\
values.astype('datetime64[s]').astype(int64_dtype)
stock_dividend_payouts['pay_date'] = \
stock_dividend_payouts['pay_date'].\
values.astype('datetime64[s]').astype(int64_dtype)
self.write_stock_dividend_payouts(stock_dividend_payouts)
def write_dividend_data(self, dividends, stock_dividends=None):
"""
Write both dividend payouts and the derived price adjustment ratios.
"""
# First write the dividend payouts.
self._write_dividends(dividends)
self._write_stock_dividends(stock_dividends)
# Second from the dividend payouts, calculate ratios.
dividend_ratios = self.calc_dividend_ratios(dividends)
self.write_frame('dividends', dividend_ratios)
def ensure_tables(self):
metadata = sa.MetaData()
tables = {}
tables['dividend_payouts'] = sa.Table(
'dividend_payouts',
metadata,
sa.Column('id', sa.BigInteger(), unique=True, nullable=False, primary_key=True, autoincrement=True),
sa.Column('sid', sa.BigInteger() ),
sa.Column('ex_date', sa.BigInteger() ),
sa.Column('declared_date', sa.BigInteger() ),
sa.Column('record_date', sa.BigInteger() ),
sa.Column('pay_date', sa.BigInteger() ),
sa.Column('amount', sa.Float() ),
sa.UniqueConstraint('sid', 'ex_date', name='div_payouts_uq')
)
tables['stock_dividend_payouts'] = sa.Table(
'stock_dividend_payouts',
metadata,
sa.Column('index', sa.BigInteger(), unique=True, nullable=False, primary_key=True, autoincrement=True),
sa.Column('sid', sa.BigInteger() ),
sa.Column('ex_date', sa.BigInteger() ),
sa.Column('declared_date', sa.BigInteger() ),
sa.Column('record_date', sa.BigInteger() ),
sa.Column('pay_date', sa.BigInteger() ),
sa.Column('payment_sid', sa.BigInteger() ),
sa.Column('ratio', sa.Float() ),
sa.UniqueConstraint('sid', 'ex_date', name='stk_div_payouts_uq')
)
tables['dividends'] = sa.Table(
'dividends',
metadata,
sa.Column('index', sa.BigInteger(), unique=True, nullable=False, primary_key=True, autoincrement=True),
sa.Column('sid', sa.BigInteger() ),
sa.Column('effective_date', sa.BigInteger() ),
sa.Column('ratio', sa.Float() ),
sa.UniqueConstraint('sid', 'effective_date', name='div_uq')
)
tables['mergers'] = sa.Table(
'mergers',
metadata,
sa.Column('index', sa.BigInteger(), unique=True, nullable=False, primary_key=True, autoincrement=True),
sa.Column('sid', sa.BigInteger() ),
sa.Column('effective_date', sa.BigInteger() ),
sa.Column('ratio', sa.Float() ),
sa.UniqueConstraint('sid', 'effective_date', name='mergers_uq')
)
tables['splits'] = sa.Table(
'splits',
metadata,
sa.Column('index', sa.BigInteger(), unique=True, primary_key=True, autoincrement=True),
sa.Column('sid', sa.BigInteger() ),
sa.Column('effective_date', sa.BigInteger() ),
sa.Column('ratio', sa.Float() ),
sa.UniqueConstraint('sid', 'effective_date', name='splits_uq')
)
metadata.create_all(self.engine)
return tables
def write(self,
splits=None,
mergers=None,
dividends=None,
stock_dividends=None):
"""
Writes data to a SQLite file to be read by SQLiteAdjustmentReader.
Parameters
----------
splits : pandas.DataFrame, optional
Dataframe containing split data. The format of this dataframe is:
effective_date : int
The date, represented as seconds since Unix epoch, on which
the adjustment should be applied.
ratio : float
A value to apply to all data earlier than the effective date.
For open, high, low, and close those values are multiplied by
the ratio. Volume is divided by this value.
sid : int
The asset id associated with this adjustment.
mergers : pandas.DataFrame, optional
DataFrame containing merger data. The format of this dataframe is:
effective_date : int
The date, represented as seconds since Unix epoch, on which
the adjustment should be applied.
ratio : float
A value to apply to all data earlier than the effective date.
For open, high, low, and close those values are multiplied by
the ratio. Volume is unaffected.
sid : int
The asset id associated with this adjustment.
dividends : pandas.DataFrame, optional
DataFrame containing dividend data. The format of the dataframe is:
sid : int
The asset id associated with this adjustment.
ex_date : datetime64
The date on which an equity must be held to be eligible to
receive payment.
declared_date : datetime64
The date on which the dividend is announced to the public.
pay_date : datetime64
The date on which the dividend is distributed.
record_date : datetime64
The date on which the stock ownership is checked to determine
distribution of dividends.
amount : float
The cash amount paid for each share.
Dividend ratios are calculated as:
``1.0 - (dividend_value / "close on day prior to ex_date")``
stock_dividends : pandas.DataFrame, optional
DataFrame containing stock dividend data. The format of the
dataframe is:
sid : int
The asset id associated with this adjustment.
ex_date : datetime64
The date on which an equity must be held to be eligible to
receive payment.
declared_date : datetime64
The date on which the dividend is announced to the public.
pay_date : datetime64
The date on which the dividend is distributed.
record_date : datetime64
The date on which the stock ownership is checked to determine
distribution of dividends.
payment_sid : int
The asset id of the shares that should be paid instead of
cash.
ratio : float
The ratio of currently held shares in the held sid that
should be paid with new shares of the payment_sid.
See Also
--------
zipline.data.adjustments.SQLiteAdjustmentReader
"""
self.write_frame('splits', splits)
self.write_frame('mergers', mergers)
self.write_dividend_data(dividends, stock_dividends)
# Use IF NOT EXISTS here to allow multiple writes if desired.
self.conn.execute(
"CREATE INDEX IF NOT EXISTS splits_sids "
"ON splits(sid)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS splits_effective_date "
"ON splits(effective_date)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS mergers_sids "
"ON mergers(sid)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS mergers_effective_date "
"ON mergers(effective_date)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS dividends_sid "
"ON dividends(sid)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS dividends_effective_date "
"ON dividends(effective_date)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS dividend_payouts_sid "
"ON dividend_payouts(sid)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS dividends_payouts_ex_date "
"ON dividend_payouts(ex_date)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS stock_dividend_payouts_sid "
"ON stock_dividend_payouts(sid)"
)
self.conn.execute(
"CREATE INDEX IF NOT EXISTS stock_dividends_payouts_ex_date "
"ON stock_dividend_payouts(ex_date)"
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/adjustments.py | adjustments.py |
from collections import OrderedDict
from abc import ABCMeta, abstractmethod
import numpy as np
import pandas as pd
from six import with_metaclass
from zipline.data._resample import (
_minute_to_session_open,
_minute_to_session_high,
_minute_to_session_low,
_minute_to_session_close,
_minute_to_session_volume,
)
from zipline.data.bar_reader import NoDataOnDate
from zipline.data.minute_bars import MinuteBarReader
from zipline.data.session_bars import SessionBarReader
from zipline.utils.memoize import lazyval
_MINUTE_TO_SESSION_OHCLV_HOW = OrderedDict((
('open', 'first'),
('high', 'max'),
('low', 'min'),
('close', 'last'),
('volume', 'sum'),
))
def minute_frame_to_session_frame(minute_frame, calendar):
"""
Resample a DataFrame with minute data into the frame expected by a
BcolzDailyBarWriter.
Parameters
----------
minute_frame : pd.DataFrame
A DataFrame with the columns `open`, `high`, `low`, `close`, `volume`,
and `dt` (minute dts)
calendar : trading_calendars.trading_calendar.TradingCalendar
A TradingCalendar on which session labels to resample from minute
to session.
Return
------
session_frame : pd.DataFrame
A DataFrame with the columns `open`, `high`, `low`, `close`, `volume`,
and `day` (datetime-like).
"""
how = OrderedDict((c, _MINUTE_TO_SESSION_OHCLV_HOW[c])
for c in minute_frame.columns)
labels = calendar.minute_index_to_session_labels(minute_frame.index)
return minute_frame.groupby(labels).agg(how)
def minute_to_session(column, close_locs, data, out):
"""
Resample an array with minute data into an array with session data.
This function assumes that the minute data is the exact length of all
minutes in the sessions in the output.
Parameters
----------
column : str
The `open`, `high`, `low`, `close`, or `volume` column.
close_locs : array[intp]
The locations in `data` which are the market close minutes.
data : array[float64|uint32]
The minute data to be sampled into session data.
The first value should align with the market open of the first session,
containing values for all minutes for all sessions. With the last value
being the market close of the last session.
out : array[float64|uint32]
The output array into which to write the sampled sessions.
"""
if column == 'open':
_minute_to_session_open(close_locs, data, out)
elif column == 'high':
_minute_to_session_high(close_locs, data, out)
elif column == 'low':
_minute_to_session_low(close_locs, data, out)
elif column == 'close':
_minute_to_session_close(close_locs, data, out)
elif column == 'volume':
_minute_to_session_volume(close_locs, data, out)
return out
class DailyHistoryAggregator(object):
"""
Converts minute pricing data into a daily summary, to be used for the
last slot in a call to history with a frequency of `1d`.
This summary is the same as a daily bar rollup of minute data, with the
distinction that the summary is truncated to the `dt` requested.
i.e. the aggregation slides forward during a the course of simulation day.
Provides aggregation for `open`, `high`, `low`, `close`, and `volume`.
The aggregation rules for each price type is documented in their respective
"""
def __init__(self, market_opens, minute_reader, trading_calendar):
self._market_opens = market_opens
self._minute_reader = minute_reader
self._trading_calendar = trading_calendar
# The caches are structured as (date, market_open, entries), where
# entries is a dict of asset -> (last_visited_dt, value)
#
# Whenever an aggregation method determines the current value,
# the entry for the respective asset should be overwritten with a new
# entry for the current dt.value (int) and aggregation value.
#
# When the requested dt's date is different from date the cache is
# flushed, so that the cache entries do not grow unbounded.
#
# Example cache:
# cache = (date(2016, 3, 17),
# pd.Timestamp('2016-03-17 13:31', tz='UTC'),
# {
# 1: (1458221460000000000, np.nan),
# 2: (1458221460000000000, 42.0),
# })
self._caches = {
'open': None,
'high': None,
'low': None,
'close': None,
'volume': None
}
# The int value is used for deltas to avoid extra computation from
# creating new Timestamps.
self._one_min = pd.Timedelta('1 min').value
def _prelude(self, dt, field):
session = self._trading_calendar.minute_to_session_label(dt)
dt_value = dt.value
cache = self._caches[field]
if cache is None or cache[0] != session:
market_open = self._market_opens.loc[session]
cache = self._caches[field] = (session, market_open, {})
_, market_open, entries = cache
market_open = market_open.tz_localize('UTC')
if dt != market_open:
prev_dt = dt_value - self._one_min
else:
prev_dt = None
return market_open, prev_dt, dt_value, entries
def opens(self, assets, dt):
"""
The open field's aggregation returns the first value that occurs
for the day, if there has been no data on or before the `dt` the open
is `nan`.
Once the first non-nan open is seen, that value remains constant per
asset for the remainder of the day.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'open')
opens = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
opens.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'open')
entries[asset] = (dt_value, val)
opens.append(val)
continue
else:
try:
last_visited_dt, first_open = entries[asset]
if last_visited_dt == dt_value:
opens.append(first_open)
continue
elif not pd.isnull(first_open):
opens.append(first_open)
entries[asset] = (dt_value, first_open)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['open'],
after_last,
dt,
[asset],
)[0]
nonnan = window[~pd.isnull(window)]
if len(nonnan):
val = nonnan[0]
else:
val = np.nan
entries[asset] = (dt_value, val)
opens.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['open'],
market_open,
dt,
[asset],
)[0]
nonnan = window[~pd.isnull(window)]
if len(nonnan):
val = nonnan[0]
else:
val = np.nan
entries[asset] = (dt_value, val)
opens.append(val)
continue
return np.array(opens)
def highs(self, assets, dt):
"""
The high field's aggregation returns the largest high seen between
the market open and the current dt.
If there has been no data on or before the `dt` the high is `nan`.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'high')
highs = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
highs.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'high')
entries[asset] = (dt_value, val)
highs.append(val)
continue
else:
try:
last_visited_dt, last_max = entries[asset]
if last_visited_dt == dt_value:
highs.append(last_max)
continue
elif last_visited_dt == prev_dt:
curr_val = self._minute_reader.get_value(
asset, dt, 'high')
if pd.isnull(curr_val):
val = last_max
elif pd.isnull(last_max):
val = curr_val
else:
val = max(last_max, curr_val)
entries[asset] = (dt_value, val)
highs.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['high'],
after_last,
dt,
[asset],
)[0].T
val = np.nanmax(np.append(window, last_max))
entries[asset] = (dt_value, val)
highs.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['high'],
market_open,
dt,
[asset],
)[0].T
val = np.nanmax(window)
entries[asset] = (dt_value, val)
highs.append(val)
continue
return np.array(highs)
def lows(self, assets, dt):
"""
The low field's aggregation returns the smallest low seen between
the market open and the current dt.
If there has been no data on or before the `dt` the low is `nan`.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'low')
lows = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
lows.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'low')
entries[asset] = (dt_value, val)
lows.append(val)
continue
else:
try:
last_visited_dt, last_min = entries[asset]
if last_visited_dt == dt_value:
lows.append(last_min)
continue
elif last_visited_dt == prev_dt:
curr_val = self._minute_reader.get_value(
asset, dt, 'low')
val = np.nanmin([last_min, curr_val])
entries[asset] = (dt_value, val)
lows.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['low'],
after_last,
dt,
[asset],
)[0].T
val = np.nanmin(np.append(window, last_min))
entries[asset] = (dt_value, val)
lows.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['low'],
market_open,
dt,
[asset],
)[0].T
val = np.nanmin(window)
entries[asset] = (dt_value, val)
lows.append(val)
continue
return np.array(lows)
def closes(self, assets, dt):
"""
The close field's aggregation returns the latest close at the given
dt.
If the close for the given dt is `nan`, the most recent non-nan
`close` is used.
If there has been no data on or before the `dt` the close is `nan`.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'close')
closes = []
session_label = self._trading_calendar.minute_to_session_label(dt)
def _get_filled_close(asset):
"""
Returns the most recent non-nan close for the asset in this
session. If there has been no data in this session on or before the
`dt`, returns `nan`
"""
window = self._minute_reader.load_raw_arrays(
['close'],
market_open,
dt,
[asset],
)[0]
try:
return window[~np.isnan(window)][-1]
except IndexError:
return np.NaN
for asset in assets:
if not asset.is_alive_for_session(session_label):
closes.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'close')
entries[asset] = (dt_value, val)
closes.append(val)
continue
else:
try:
last_visited_dt, last_close = entries[asset]
if last_visited_dt == dt_value:
closes.append(last_close)
continue
elif last_visited_dt == prev_dt:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = last_close
entries[asset] = (dt_value, val)
closes.append(val)
continue
else:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = _get_filled_close(asset)
entries[asset] = (dt_value, val)
closes.append(val)
continue
except KeyError:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = _get_filled_close(asset)
entries[asset] = (dt_value, val)
closes.append(val)
continue
return np.array(closes)
def volumes(self, assets, dt):
"""
The volume field's aggregation returns the sum of all volumes
between the market open and the `dt`
If there has been no data on or before the `dt` the volume is 0.
Returns
-------
np.array with dtype=int64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'volume')
volumes = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
volumes.append(0)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'volume')
entries[asset] = (dt_value, val)
volumes.append(val)
continue
else:
try:
last_visited_dt, last_total = entries[asset]
if last_visited_dt == dt_value:
volumes.append(last_total)
continue
elif last_visited_dt == prev_dt:
val = self._minute_reader.get_value(
asset, dt, 'volume')
val += last_total
entries[asset] = (dt_value, val)
volumes.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['volume'],
after_last,
dt,
[asset],
)[0]
val = np.nansum(window) + last_total
entries[asset] = (dt_value, val)
volumes.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['volume'],
market_open,
dt,
[asset],
)[0]
val = np.nansum(window)
entries[asset] = (dt_value, val)
volumes.append(val)
continue
return np.array(volumes)
class MinuteResampleSessionBarReader(SessionBarReader):
def __init__(self, calendar, minute_bar_reader):
self._calendar = calendar
self._minute_bar_reader = minute_bar_reader
def _get_resampled(self, columns, start_session, end_session, assets):
range_open = self._calendar.session_open(start_session)
range_close = self._calendar.session_close(end_session)
minute_data = self._minute_bar_reader.load_raw_arrays(
columns,
range_open,
range_close,
assets,
)
# Get the index of the close minute for each session in the range.
# If the range contains only one session, the only close in the range
# is the last minute in the data. Otherwise, we need to get all the
# session closes and find their indices in the range of minutes.
if start_session == end_session:
close_ilocs = np.array([len(minute_data[0]) - 1], dtype=np.int64)
else:
minutes = self._calendar.minutes_in_range(
range_open,
range_close,
)
session_closes = self._calendar.session_closes_in_range(
start_session,
end_session,
)
close_ilocs = minutes.searchsorted(pd.to_datetime(session_closes.values, utc=True))
results = []
shape = (len(close_ilocs), len(assets))
for col in columns:
if col != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
results.append(out)
for i in range(len(assets)):
for j, column in enumerate(columns):
data = minute_data[j][:, i]
minute_to_session(column, close_ilocs, data, results[j][:, i])
return results
@property
def trading_calendar(self):
return self._calendar
def load_raw_arrays(self, columns, start_dt, end_dt, sids):
return self._get_resampled(columns, start_dt, end_dt, sids)
def get_value(self, sid, session, colname):
# WARNING: This will need caching or other optimization if used in a
# tight loop.
# This was developed to complete interface, but has not been tuned
# for real world use.
return self._get_resampled([colname], session, session, [sid])[0][0][0]
@lazyval
def sessions(self):
cal = self._calendar
first = self._minute_bar_reader.first_trading_day
last = cal.minute_to_session_label(
self._minute_bar_reader.last_available_dt)
return cal.sessions_in_range(first, last)
@lazyval
def last_available_dt(self):
return self.trading_calendar.minute_to_session_label(
self._minute_bar_reader.last_available_dt
)
@property
def first_trading_day(self):
return self._minute_bar_reader.first_trading_day
def get_last_traded_dt(self, asset, dt):
return self.trading_calendar.minute_to_session_label(
self._minute_bar_reader.get_last_traded_dt(asset, dt))
class ReindexBarReader(with_metaclass(ABCMeta)):
"""
A base class for readers which reindexes results, filling in the additional
indices with empty data.
Used to align the reading assets which trade on different calendars.
Currently only supports a ``trading_calendar`` which is a superset of the
``reader``'s calendar.
Parameters
----------
- trading_calendar : zipline.utils.trading_calendar.TradingCalendar
The calendar to use when indexing results from the reader.
- reader : MinuteBarReader|SessionBarReader
The reader which has a calendar that is a subset of the desired
``trading_calendar``.
- first_trading_session : pd.Timestamp
The first trading session the reader should provide. Must be specified,
since the ``reader``'s first session may not exactly align with the
desired calendar. Specifically, in the case where the first session
on the target calendar is a holiday on the ``reader``'s calendar.
- last_trading_session : pd.Timestamp
The last trading session the reader should provide. Must be specified,
since the ``reader``'s last session may not exactly align with the
desired calendar. Specifically, in the case where the last session
on the target calendar is a holiday on the ``reader``'s calendar.
"""
def __init__(self,
trading_calendar,
reader,
first_trading_session,
last_trading_session):
self._trading_calendar = trading_calendar
self._reader = reader
self._first_trading_session = first_trading_session
self._last_trading_session = last_trading_session
@property
def last_available_dt(self):
return self._reader.last_available_dt
def get_last_traded_dt(self, sid, dt):
return self._reader.get_last_traded_dt(sid, dt)
@property
def first_trading_day(self):
return self._reader.first_trading_day
def get_value(self, sid, dt, field):
# Give an empty result if no data is present.
try:
return self._reader.get_value(sid, dt, field)
except NoDataOnDate:
if field == 'volume':
return 0
else:
return np.nan
@abstractmethod
def _outer_dts(self, start_dt, end_dt):
raise NotImplementedError
@abstractmethod
def _inner_dts(self, start_dt, end_dt):
raise NotImplementedError
@property
def trading_calendar(self):
return self._trading_calendar
@lazyval
def sessions(self):
return self.trading_calendar.sessions_in_range(
self._first_trading_session,
self._last_trading_session
)
def load_raw_arrays(self, fields, start_dt, end_dt, sids):
outer_dts = self._outer_dts(start_dt, end_dt)
inner_dts = self._inner_dts(start_dt, end_dt)
indices = outer_dts.searchsorted(inner_dts)
shape = len(outer_dts), len(sids)
outer_results = []
if len(inner_dts) > 0:
inner_results = self._reader.load_raw_arrays(
fields, inner_dts[0], inner_dts[-1], sids)
else:
inner_results = None
for i, field in enumerate(fields):
if field != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
if inner_results is not None:
out[indices] = inner_results[i]
outer_results.append(out)
return outer_results
class ReindexMinuteBarReader(ReindexBarReader, MinuteBarReader):
"""
See: ``ReindexBarReader``
"""
def _outer_dts(self, start_dt, end_dt):
return self._trading_calendar.minutes_in_range(start_dt, end_dt)
def _inner_dts(self, start_dt, end_dt):
return self._reader.calendar.minutes_in_range(start_dt, end_dt)
class ReindexSessionBarReader(ReindexBarReader, SessionBarReader):
"""
See: ``ReindexBarReader``
"""
def _outer_dts(self, start_dt, end_dt):
return self.trading_calendar.sessions_in_range(start_dt, end_dt)
def _inner_dts(self, start_dt, end_dt):
return self._reader.trading_calendar.sessions_in_range(
start_dt, end_dt) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/resample.py | resample.py |
import numpy as np
import pandas as pd
from zipline.data.session_bars import SessionBarReader
class ContinuousFutureSessionBarReader(SessionBarReader):
def __init__(self, bar_reader, roll_finders):
self._bar_reader = bar_reader
self._roll_finders = roll_finders
def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
fields : list of str
'sid'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
sids : list of int
The asset identifiers in the window.
Returns
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
rolls_by_asset = {}
for asset in assets:
rf = self._roll_finders[asset.roll_style]
rolls_by_asset[asset] = rf.get_rolls(
asset.root_symbol,
start_date,
end_date,
asset.offset
)
num_sessions = len(
self.trading_calendar.sessions_in_range(start_date, end_date)
)
shape = num_sessions, len(assets)
results = []
tc = self._bar_reader.trading_calendar
sessions = tc.sessions_in_range(start_date, end_date)
# Get partitions
partitions_by_asset = {}
for asset in assets:
partitions = []
partitions_by_asset[asset] = partitions
rolls = rolls_by_asset[asset]
start = start_date
for roll in rolls:
sid, roll_date = roll
start_loc = sessions.get_loc(start)
if roll_date is not None:
end = roll_date - sessions.freq
end_loc = sessions.get_loc(end)
else:
end = end_date
end_loc = len(sessions) - 1
partitions.append((sid, start, end, start_loc, end_loc))
if roll_date is not None:
start = sessions[end_loc + 1]
for column in columns:
if column != 'volume' and column != 'sid':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.int64)
for i, asset in enumerate(assets):
partitions = partitions_by_asset[asset]
for sid, start, end, start_loc, end_loc in partitions:
if column != 'sid':
result = self._bar_reader.load_raw_arrays(
[column], start, end, [sid])[0][:, 0]
else:
result = int(sid)
out[start_loc:end_loc + 1, i] = result
results.append(out)
return results
@property
def last_available_dt(self):
"""
Returns
-------
dt : pd.Timestamp
The last session for which the reader can provide data.
"""
return self._bar_reader.last_available_dt
@property
def trading_calendar(self):
"""
Returns the zipline.utils.calendar.trading_calendar used to read
the data. Can be None (if the writer didn't specify it).
"""
return self._bar_reader.trading_calendar
@property
def first_trading_day(self):
"""
Returns
-------
dt : pd.Timestamp
The first trading day (session) for which the reader can provide
data.
"""
return self._bar_reader.first_trading_day
def get_value(self, continuous_future, dt, field):
"""
Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
The OHLVC name for the desired data point.
Returns
-------
value : float|int
The value at the given coordinates, ``float`` for OHLC, ``int``
for 'volume'.
Raises
------
NoDataOnDate
If the given dt is not a valid market minute (in minute mode) or
session (in daily mode) according to this reader's tradingcalendar.
"""
rf = self._roll_finders[continuous_future.roll_style]
sid = (rf.get_contract_center(continuous_future.root_symbol,
dt,
continuous_future.offset))
return self._bar_reader.get_value(sid, dt, field)
def get_last_traded_dt(self, asset, dt):
"""
Get the latest minute on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
The asset for which to get the last traded minute.
dt : pd.Timestamp
The minute at which to start searching for the last traded minute.
Returns
-------
last_traded : pd.Timestamp
The dt of the last trade for the given asset, using the input
dt as a vantage point.
"""
rf = self._roll_finders[asset.roll_style]
sid = (rf.get_contract_center(asset.root_symbol,
dt,
asset.offset))
if sid is None:
return pd.NaT
contract = rf.asset_finder.retrieve_asset(sid)
return self._bar_reader.get_last_traded_dt(contract, dt)
@property
def sessions(self):
"""
Returns
-------
sessions : DatetimeIndex
All session labels (unioning the range for all assets) which the
reader can provide.
"""
return self._bar_reader.sessions
class ContinuousFutureMinuteBarReader(SessionBarReader):
def __init__(self, bar_reader, roll_finders):
self._bar_reader = bar_reader
self._roll_finders = roll_finders
def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
fields : list of str
'open', 'high', 'low', 'close', or 'volume'
start_dt: Timestamp
Beginning of the window range.
end_dt: Timestamp
End of the window range.
sids : list of int
The asset identifiers in the window.
Returns
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
rolls_by_asset = {}
tc = self.trading_calendar
start_session = tc.minute_to_session_label(start_date)
end_session = tc.minute_to_session_label(end_date)
for asset in assets:
rf = self._roll_finders[asset.roll_style]
rolls_by_asset[asset] = rf.get_rolls(
asset.root_symbol,
start_session,
end_session, asset.offset)
sessions = tc.sessions_in_range(start_date, end_date)
minutes = tc.minutes_in_range(start_date, end_date)
num_minutes = len(minutes)
shape = num_minutes, len(assets)
results = []
# Get partitions
partitions_by_asset = {}
for asset in assets:
partitions = []
partitions_by_asset[asset] = partitions
rolls = rolls_by_asset[asset]
start = start_date
for roll in rolls:
sid, roll_date = roll
start_loc = minutes.searchsorted(start)
if roll_date is not None:
_, end = tc.open_and_close_for_session(
roll_date - sessions.freq)
end_loc = minutes.searchsorted(end)
else:
end = end_date
end_loc = len(minutes) - 1
partitions.append((sid, start, end, start_loc, end_loc))
if roll[-1] is not None:
start, _ = tc.open_and_close_for_session(
tc.minute_to_session_label(minutes[end_loc + 1]))
for column in columns:
if column != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
for i, asset in enumerate(assets):
partitions = partitions_by_asset[asset]
for sid, start, end, start_loc, end_loc in partitions:
if column != 'sid':
result = self._bar_reader.load_raw_arrays(
[column], start, end, [sid])[0][:, 0]
else:
result = int(sid)
out[start_loc:end_loc + 1, i] = result
results.append(out)
return results
@property
def last_available_dt(self):
"""
Returns
-------
dt : pd.Timestamp
The last session for which the reader can provide data.
"""
return self._bar_reader.last_available_dt
@property
def trading_calendar(self):
"""
Returns the zipline.utils.calendar.trading_calendar used to read
the data. Can be None (if the writer didn't specify it).
"""
return self._bar_reader.trading_calendar
@property
def first_trading_day(self):
"""
Returns
-------
dt : pd.Timestamp
The first trading day (session) for which the reader can provide
data.
"""
return self._bar_reader.first_trading_day
def get_value(self, continuous_future, dt, field):
"""
Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
The OHLVC name for the desired data point.
Returns
-------
value : float|int
The value at the given coordinates, ``float`` for OHLC, ``int``
for 'volume'.
Raises
------
NoDataOnDate
If the given dt is not a valid market minute (in minute mode) or
session (in daily mode) according to this reader's tradingcalendar.
"""
rf = self._roll_finders[continuous_future.roll_style]
sid = (rf.get_contract_center(continuous_future.root_symbol,
dt,
continuous_future.offset))
return self._bar_reader.get_value(sid, dt, field)
def get_last_traded_dt(self, asset, dt):
"""
Get the latest minute on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
The asset for which to get the last traded minute.
dt : pd.Timestamp
The minute at which to start searching for the last traded minute.
Returns
-------
last_traded : pd.Timestamp
The dt of the last trade for the given asset, using the input
dt as a vantage point.
"""
rf = self._roll_finders[asset.roll_style]
sid = (rf.get_contract_center(asset.root_symbol,
dt,
asset.offset))
if sid is None:
return pd.NaT
contract = rf.asset_finder.retrieve_asset(sid)
return self._bar_reader.get_last_traded_dt(contract, dt)
@property
def sessions(self):
return self._bar_reader.sessions | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/continuous_future_reader.py | continuous_future_reader.py |
from functools import partial
import h5py
import logbook
import numpy as np
import pandas as pd
from six import iteritems, raise_from, viewkeys
from six.moves import reduce
from zipline.data.bar_reader import (
NoDataAfterDate,
NoDataBeforeDate,
NoDataForSid,
NoDataOnDate,
)
from zipline.data.session_bars import CurrencyAwareSessionBarReader
from zipline.utils.memoize import lazyval
from zipline.utils.numpy_utils import bytes_array_to_native_str_object_array
from zipline.utils.pandas_utils import check_indexes_all_same
log = logbook.Logger('HDF5DailyBars')
VERSION = 0
DATA = 'data'
INDEX = 'index'
LIFETIMES = 'lifetimes'
CURRENCY = 'currency'
CODE = 'code'
SCALING_FACTOR = 'scaling_factor'
OPEN = 'open'
HIGH = 'high'
LOW = 'low'
CLOSE = 'close'
VOLUME = 'volume'
FIELDS = (OPEN, HIGH, LOW, CLOSE, VOLUME)
DAY = 'day'
SID = 'sid'
START_DATE = 'start_date'
END_DATE = 'end_date'
# XXX is reserved for "transactions involving no currency".
MISSING_CURRENCY = 'XXX'
DEFAULT_SCALING_FACTORS = {
# Retain 3 decimal places for prices.
OPEN: 1000,
HIGH: 1000,
LOW: 1000,
CLOSE: 1000,
# Volume is expected to be a whole integer.
VOLUME: 1,
}
def coerce_to_uint32(a, scaling_factor):
"""
Returns a copy of the array as uint32, applying a scaling factor to
maintain precision if supplied.
"""
return (a * scaling_factor).round().astype('uint32')
def days_and_sids_for_frames(frames):
"""
Returns the date index and sid columns shared by a list of dataframes,
ensuring they all match.
Parameters
----------
frames : list[pd.DataFrame]
A list of dataframes indexed by day, with a column per sid.
Returns
-------
days : np.array[datetime64[ns]]
The days in these dataframes.
sids : np.array[int64]
The sids in these dataframes.
Raises
------
ValueError
If the dataframes passed are not all indexed by the same days
and sids.
"""
if not frames:
days = np.array([], dtype='datetime64[ns]')
sids = np.array([], dtype='int64')
return days, sids
# Ensure the indices and columns all match.
check_indexes_all_same(
[frame.index for frame in frames],
message='Frames have mismatched days.',
)
check_indexes_all_same(
[frame.columns for frame in frames],
message='Frames have mismatched sids.',
)
return frames[0].index.values, frames[0].columns.values
class HDF5DailyBarWriter(object):
"""
Class capable of writing daily OHLCV data to disk in a format that
can be read efficiently by HDF5DailyBarReader.
Parameters
----------
filename : str
The location at which we should write our output.
date_chunk_size : int
The number of days per chunk in the HDF5 file. If this is
greater than the number of days in the data, the chunksize will
match the actual number of days.
See Also
--------
zipline.data.hdf5_daily_bars.HDF5DailyBarReader
"""
def __init__(self, filename, date_chunk_size):
self._filename = filename
self._date_chunk_size = date_chunk_size
def h5_file(self, mode):
return h5py.File(self._filename, mode)
def write(self,
country_code,
frames,
currency_codes=None,
scaling_factors=None):
"""
Write the OHLCV data for one country to the HDF5 file.
Parameters
----------
country_code : str
The ISO 3166 alpha-2 country code for this country.
frames : dict[str, pd.DataFrame]
A dict mapping each OHLCV field to a dataframe with a row
for each date and a column for each sid. The dataframes need
to have the same index and columns.
currency_codes : pd.Series, optional
Series mapping sids to 3-digit currency code values for those sids'
listing currencies. If not passed, missing currencies will be
written.
scaling_factors : dict[str, float], optional
A dict mapping each OHLCV field to a scaling factor, which
is applied (as a multiplier) to the values of field to
efficiently store them as uint32, while maintaining desired
precision. These factors are written to the file as metadata,
which is consumed by the reader to adjust back to the original
float values. Default is None, in which case
DEFAULT_SCALING_FACTORS is used.
"""
if scaling_factors is None:
scaling_factors = DEFAULT_SCALING_FACTORS
# Note that this functions validates that all of the frames
# share the same days and sids.
days, sids = days_and_sids_for_frames(list(frames.values()))
# XXX: We should make this required once we're using it everywhere.
if currency_codes is None:
currency_codes = pd.Series(index=sids, data=MISSING_CURRENCY)
# Currency codes should match dataframe columns.
check_sids_arrays_match(
sids,
currency_codes.index.values,
message="currency_codes sids do not match data sids:",
)
# Write start and end dates for each sid.
start_date_ixs, end_date_ixs = compute_asset_lifetimes(frames)
if len(sids):
chunks = (len(sids), min(self._date_chunk_size, len(days)))
else:
# h5py crashes if we provide chunks for empty data.
chunks = None
with self.h5_file(mode='a') as h5_file:
# ensure that the file version has been written
h5_file.attrs['version'] = VERSION
country_group = h5_file.create_group(country_code)
self._write_index_group(country_group, days, sids)
self._write_lifetimes_group(
country_group,
start_date_ixs,
end_date_ixs,
)
self._write_currency_group(country_group, currency_codes)
self._write_data_group(
country_group,
frames,
scaling_factors,
chunks,
)
def write_from_sid_df_pairs(self,
country_code,
data,
currency_codes=None,
scaling_factors=None):
"""
Parameters
----------
country_code : str
The ISO 3166 alpha-2 country code for this country.
data : iterable[tuple[int, pandas.DataFrame]]
The data chunks to write. Each chunk should be a tuple of
sid and the data for that asset.
currency_codes : pd.Series, optional
Series mapping sids to 3-digit currency code values for those sids'
listing currencies. If not passed, missing currencies will be
written.
scaling_factors : dict[str, float], optional
A dict mapping each OHLCV field to a scaling factor, which
is applied (as a multiplier) to the values of field to
efficiently store them as uint32, while maintaining desired
precision. These factors are written to the file as metadata,
which is consumed by the reader to adjust back to the original
float values. Default is None, in which case
DEFAULT_SCALING_FACTORS is used.
"""
data = list(data)
if not data:
empty_frame = pd.DataFrame(
data=None,
index=np.array([], dtype='datetime64[ns]'),
columns=np.array([], dtype='int64'),
)
return self.write(
country_code,
{f: empty_frame.copy() for f in FIELDS},
scaling_factors,
)
sids, frames = zip(*data)
ohlcv_frame = pd.concat(frames)
# Repeat each sid for each row in its corresponding frame.
sid_ix = np.repeat(sids, [len(f) for f in frames])
# Add id to the index, so the frame is indexed by (date, id).
ohlcv_frame.set_index(sid_ix, append=True, inplace=True)
frames = {
field: ohlcv_frame[field].unstack()
for field in FIELDS
}
return self.write(
country_code=country_code,
frames=frames,
scaling_factors=scaling_factors,
currency_codes=currency_codes
)
def _write_index_group(self, country_group, days, sids):
"""Write /country/index.
"""
index_group = country_group.create_group(INDEX)
self._log_writing_dataset(index_group)
index_group.create_dataset(SID, data=sids)
# h5py does not support datetimes, so they need to be stored
# as integers.
index_group.create_dataset(DAY, data=days.astype(np.int64))
def _write_lifetimes_group(self,
country_group,
start_date_ixs,
end_date_ixs):
"""Write /country/lifetimes
"""
lifetimes_group = country_group.create_group(LIFETIMES)
self._log_writing_dataset(lifetimes_group)
lifetimes_group.create_dataset(START_DATE, data=start_date_ixs)
lifetimes_group.create_dataset(END_DATE, data=end_date_ixs)
def _write_currency_group(self, country_group, currencies):
"""Write /country/currency
"""
currency_group = country_group.create_group(CURRENCY)
self._log_writing_dataset(currency_group)
currency_group.create_dataset(
CODE,
data=currencies.values.astype(dtype='S3'),
)
def _write_data_group(self,
country_group,
frames,
scaling_factors,
chunks):
"""Write /country/data
"""
data_group = country_group.create_group(DATA)
self._log_writing_dataset(data_group)
for field in FIELDS:
frame = frames[field]
# Sort rows by increasing sid, and columns by increasing date.
frame.sort_index(inplace=True)
frame.sort_index(axis='columns', inplace=True)
data = coerce_to_uint32(
frame.T.fillna(0).values,
scaling_factors[field],
)
dataset = data_group.create_dataset(
field,
compression='lzf',
shuffle=True,
data=data,
chunks=chunks,
)
self._log_writing_dataset(dataset)
dataset.attrs[SCALING_FACTOR] = scaling_factors[field]
log.debug(
'Writing dataset {} to file {}',
dataset.name, self._filename
)
def _log_writing_dataset(self, dataset):
log.debug("Writing {} to file {}", dataset.name, self._filename)
def compute_asset_lifetimes(frames):
"""
Parameters
----------
frames : dict[str, pd.DataFrame]
A dict mapping each OHLCV field to a dataframe with a row for
each date and a column for each sid, as passed to write().
Returns
-------
start_date_ixs : np.array[int64]
The index of the first date with non-nan values, for each sid.
end_date_ixs : np.array[int64]
The index of the last date with non-nan values, for each sid.
"""
# Build a 2D array (dates x sids), where an entry is True if all
# fields are nan for the given day and sid.
is_null_matrix = np.logical_and.reduce(
[frames[field].isnull().values for field in FIELDS],
)
if not is_null_matrix.size:
empty = np.array([], dtype='int64')
return empty, empty.copy()
# Offset of the first null from the start of the input.
start_date_ixs = is_null_matrix.argmin(axis=0)
# Offset of the last null from the **end** of the input.
end_offsets = is_null_matrix[::-1].argmin(axis=0)
# Offset of the last null from the start of the input
end_date_ixs = is_null_matrix.shape[0] - end_offsets - 1
return start_date_ixs, end_date_ixs
def convert_price_with_scaling_factor(a, scaling_factor):
conversion_factor = (1.0 / scaling_factor)
zeroes = (a == 0)
return np.where(zeroes, np.nan, a.astype('float64')) * conversion_factor
class HDF5DailyBarReader(CurrencyAwareSessionBarReader):
"""
Parameters
---------
country_group : h5py.Group
The group for a single country in an HDF5 daily pricing file.
"""
def __init__(self, country_group):
self._country_group = country_group
self._postprocessors = {
OPEN: partial(convert_price_with_scaling_factor,
scaling_factor=self._read_scaling_factor(OPEN)),
HIGH: partial(convert_price_with_scaling_factor,
scaling_factor=self._read_scaling_factor(HIGH)),
LOW: partial(convert_price_with_scaling_factor,
scaling_factor=self._read_scaling_factor(LOW)),
CLOSE: partial(convert_price_with_scaling_factor,
scaling_factor=self._read_scaling_factor(CLOSE)),
VOLUME: lambda a: a,
}
@classmethod
def from_file(cls, h5_file, country_code):
"""
Construct from an h5py.File and a country code.
Parameters
----------
h5_file : h5py.File
An HDF5 daily pricing file.
country_code : str
The ISO 3166 alpha-2 country code for the country to read.
"""
if h5_file.attrs['version'] != VERSION:
raise ValueError(
'mismatched version: file is of version %s, expected %s' % (
h5_file.attrs['version'],
VERSION,
),
)
return cls(h5_file[country_code])
@classmethod
def from_path(cls, path, country_code):
"""
Construct from a file path and a country code.
Parameters
----------
path : str
The path to an HDF5 daily pricing file.
country_code : str
The ISO 3166 alpha-2 country code for the country to read.
"""
return cls.from_file(h5py.File(path, 'r'), country_code)
def _read_scaling_factor(self, field):
return self._country_group[DATA][field].attrs[SCALING_FACTOR]
def load_raw_arrays(self,
columns,
start_date,
end_date,
assets):
"""
Parameters
----------
columns : list of str
'open', 'high', 'low', 'close', or 'volume'
start_date: Timestamp
Beginning of the window range.
end_date: Timestamp
End of the window range.
assets : list of int
The asset identifiers in the window.
Returns
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
self._validate_timestamp(start_date)
self._validate_timestamp(end_date)
start = start_date.asm8
end = end_date.asm8
date_slice = self._compute_date_range_slice(start, end)
n_dates = date_slice.stop - date_slice.start
# Create a buffer into which we'll read data from the h5 file.
# Allocate an extra row of space that will always contain null values.
# We'll use that space to provide "data" for entries in ``assets`` that
# are unknown to us.
full_buf = np.zeros((len(self.sids) + 1, n_dates), dtype=np.uint32)
# We'll only read values into this portion of the read buf.
mutable_buf = full_buf[:-1]
# Indexer that converts an array aligned to self.sids (which is what we
# pull from the h5 file) into an array aligned to ``assets``.
#
# Unknown assets will have an index of -1, which means they'll always
# pull from the last row of the read buffer. We allocated an extra
# empty row above so that these lookups will cause us to fill our
# output buffer with "null" values.
sid_selector = self._make_sid_selector(assets)
out = []
for column in columns:
# Zero the buffer to prepare to receive new data.
mutable_buf.fill(0)
dataset = self._country_group[DATA][column]
# Fill the mutable portion of our buffer with data from the file.
dataset.read_direct(
mutable_buf,
np.s_[:, date_slice],
)
# Select data from the **full buffer**. Unknown assets will pull
# from the last row, which is always empty.
out.append(self._postprocessors[column](full_buf[sid_selector].T))
return out
def _make_sid_selector(self, assets):
"""
Build an indexer mapping ``self.sids`` to ``assets``.
Parameters
----------
assets : list[int]
List of assets requested by a caller of ``load_raw_arrays``.
Returns
-------
index : np.array[int64]
Index array containing the index in ``self.sids`` for each location
in ``assets``. Entries in ``assets`` for which we don't have a sid
will contain -1. It is caller's responsibility to handle these
values correctly.
"""
assets = np.array(assets)
sid_selector = self.sids.searchsorted(assets)
unknown = np.in1d(assets, self.sids, invert=True)
sid_selector[unknown] = -1
return sid_selector
def _compute_date_range_slice(self, start_date, end_date):
# Get the index of the start of dates for ``start_date``.
start_ix = self.dates.searchsorted(start_date)
# Get the index of the start of the first date **after** end_date.
end_ix = self.dates.searchsorted(end_date, side='right')
return slice(start_ix, end_ix)
def _validate_assets(self, assets):
"""Validate that asset identifiers are contained in the daily bars.
Parameters
----------
assets : array-like[int]
The asset identifiers to validate.
Raises
------
NoDataForSid
If one or more of the provided asset identifiers are not
contained in the daily bars.
"""
missing_sids = np.setdiff1d(assets, self.sids)
if len(missing_sids):
raise NoDataForSid(
'Assets not contained in daily pricing file: {}'.format(
missing_sids
)
)
def _validate_timestamp(self, ts):
if ts.asm8 not in self.dates:
raise NoDataOnDate(ts)
@lazyval
def dates(self):
return self._country_group[INDEX][DAY][:].astype('datetime64[ns]')
@lazyval
def sids(self):
return self._country_group[INDEX][SID][:].astype('int64', copy=False)
@lazyval
def asset_start_dates(self):
return self.dates[self._country_group[LIFETIMES][START_DATE][:]]
@lazyval
def asset_end_dates(self):
return self.dates[self._country_group[LIFETIMES][END_DATE][:]]
@lazyval
def _currency_codes(self):
bytes_array = self._country_group[CURRENCY][CODE][:]
return bytes_array_to_native_str_object_array(bytes_array)
def currency_codes(self, sids):
"""Get currencies in which prices are quoted for the requested sids.
Parameters
----------
sids : np.array[int64]
Array of sids for which currencies are needed.
Returns
-------
currency_codes : np.array[object]
Array of currency codes for listing currencies of ``sids``.
"""
# Find the index of requested sids in our stored sids.
ixs = self.sids.searchsorted(sids, side='left')
result = self._currency_codes[ixs]
# searchsorted returns the index of the next lowest sid if the lookup
# fails. Fill these sids with the special "missing" sentinel.
not_found = (self.sids[ixs] != sids)
result[not_found] = None
return result
@property
def last_available_dt(self):
"""
Returns
-------
dt : pd.Timestamp
The last session for which the reader can provide data.
"""
return pd.Timestamp(self.dates[-1], tz='UTC')
@property
def trading_calendar(self):
"""
Returns the zipline.utils.calendar.trading_calendar used to read
the data. Can be None (if the writer didn't specify it).
"""
raise NotImplementedError(
'HDF5 pricing does not yet support trading calendars.'
)
@property
def first_trading_day(self):
"""
Returns
-------
dt : pd.Timestamp
The first trading day (session) for which the reader can provide
data.
"""
return pd.Timestamp(self.dates[0], tz='UTC')
@lazyval
def sessions(self):
"""
Returns
-------
sessions : DatetimeIndex
All session labels (unioning the range for all assets) which the
reader can provide.
"""
return pd.to_datetime(self.dates, utc=True)
def get_value(self, sid, dt, field):
"""
Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
The OHLVC name for the desired data point.
Returns
-------
value : float|int
The value at the given coordinates, ``float`` for OHLC, ``int``
for 'volume'.
Raises
------
NoDataOnDate
If the given dt is not a valid market minute (in minute mode) or
session (in daily mode) according to this reader's tradingcalendar.
"""
self._validate_assets([sid])
self._validate_timestamp(dt)
sid_ix = self.sids.searchsorted(sid)
dt_ix = self.dates.searchsorted(dt.asm8)
value = self._postprocessors[field](
self._country_group[DATA][field][sid_ix, dt_ix]
)
# When the value is nan, this dt may be outside the asset's lifetime.
# If that's the case, the proper NoDataOnDate exception is raised.
# Otherwise (when there's just a hole in the middle of the data), the
# nan is returned.
if np.isnan(value):
if dt.asm8 < self.asset_start_dates[sid_ix]:
raise NoDataBeforeDate()
if dt.asm8 > self.asset_end_dates[sid_ix]:
raise NoDataAfterDate()
return value
def get_last_traded_dt(self, asset, dt):
"""
Get the latest day on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
The asset for which to get the last traded day.
dt : pd.Timestamp
The dt at which to start searching for the last traded day.
Returns
-------
last_traded : pd.Timestamp
The day of the last trade for the given asset, using the
input dt as a vantage point.
"""
sid_ix = self.sids.searchsorted(asset.sid)
# Used to get a slice of all dates up to and including ``dt``.
dt_limit_ix = self.dates.searchsorted(dt.asm8, side='right')
# Get the indices of all dates with nonzero volume.
nonzero_volume_ixs = np.ravel(
np.nonzero(self._country_group[DATA][VOLUME][sid_ix, :dt_limit_ix])
)
if len(nonzero_volume_ixs) == 0:
return pd.NaT
return pd.Timestamp(self.dates[nonzero_volume_ixs][-1], tz='UTC')
class MultiCountryDailyBarReader(CurrencyAwareSessionBarReader):
"""
Parameters
---------
readers : dict[str -> SessionBarReader]
A dict mapping country codes to SessionBarReader instances to
service each country.
"""
def __init__(self, readers):
self._readers = readers
self._country_map = pd.concat([
pd.Series(index=reader.sids, data=country_code)
for country_code, reader in iteritems(readers)
])
@classmethod
def from_file(cls, h5_file):
"""
Construct from an h5py.File.
Parameters
----------
h5_file : h5py.File
An HDF5 daily pricing file.
"""
return cls({
country: HDF5DailyBarReader.from_file(h5_file, country)
for country in h5_file.keys()
})
@classmethod
def from_path(cls, path):
"""
Construct from a file path.
Parameters
----------
path : str
Path to an HDF5 daily pricing file.
"""
return cls.from_file(h5py.File(path, 'r'))
@property
def countries(self):
"""A set-like object of the country codes supplied by this reader.
"""
return viewkeys(self._readers)
def _country_code_for_assets(self, assets):
country_codes = self._country_map.get(assets)
# In some versions of pandas (observed in 0.22), Series.get()
# returns None if none of the labels are in the index.
if country_codes is not None:
unique_country_codes = country_codes.dropna().unique()
num_countries = len(unique_country_codes)
else:
num_countries = 0
if num_countries == 0:
raise ValueError('At least one valid asset id is required.')
elif num_countries > 1:
raise NotImplementedError(
(
'Assets were requested from multiple countries ({}),'
' but multi-country reads are not yet supported.'
).format(list(unique_country_codes))
)
return np.asscalar(unique_country_codes)
def load_raw_arrays(self,
columns,
start_date,
end_date,
assets):
"""
Parameters
----------
columns : list of str
'open', 'high', 'low', 'close', or 'volume'
start_date: Timestamp
Beginning of the window range.
end_date: Timestamp
End of the window range.
assets : list of int
The asset identifiers in the window.
Returns
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
country_code = self._country_code_for_assets(assets)
return self._readers[country_code].load_raw_arrays(
columns,
start_date,
end_date,
assets,
)
@property
def last_available_dt(self):
"""
Returns
-------
dt : pd.Timestamp
The last session for which the reader can provide data.
"""
return max(
reader.last_available_dt for reader in self._readers.values()
)
@property
def trading_calendar(self):
"""
Returns the zipline.utils.calendar.trading_calendar used to read
the data. Can be None (if the writer didn't specify it).
"""
raise NotImplementedError(
'HDF5 pricing does not yet support trading calendars.'
)
@property
def first_trading_day(self):
"""
Returns
-------
dt : pd.Timestamp
The first trading day (session) for which the reader can provide
data.
"""
return min(
reader.first_trading_day for reader in self._readers.values()
)
@property
def sessions(self):
"""
Returns
-------
sessions : DatetimeIndex
All session labels (unioning the range for all assets) which the
reader can provide.
"""
return pd.to_datetime(
reduce(
np.union1d,
(reader.dates for reader in self._readers.values()),
),
utc=True,
)
def get_value(self, sid, dt, field):
"""
Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
The OHLVC name for the desired data point.
Returns
-------
value : float|int
The value at the given coordinates, ``float`` for OHLC, ``int``
for 'volume'.
Raises
------
NoDataOnDate
If the given dt is not a valid market minute (in minute mode) or
session (in daily mode) according to this reader's tradingcalendar.
NoDataForSid
If the given sid is not valid.
"""
try:
country_code = self._country_code_for_assets([sid])
except ValueError as exc:
raise_from(
NoDataForSid(
'Asset not contained in daily pricing file: {}'.format(sid)
),
exc
)
return self._readers[country_code].get_value(sid, dt, field)
def get_last_traded_dt(self, asset, dt):
"""
Get the latest day on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
The asset for which to get the last traded day.
dt : pd.Timestamp
The dt at which to start searching for the last traded day.
Returns
-------
last_traded : pd.Timestamp
The day of the last trade for the given asset, using the
input dt as a vantage point.
"""
country_code = self._country_code_for_assets([asset.sid])
return self._readers[country_code].get_last_traded_dt(asset, dt)
def currency_codes(self, sids):
"""Get currencies in which prices are quoted for the requested sids.
Assumes that a sid's prices are always quoted in a single currency.
Parameters
----------
sids : np.array[int64]
Array of sids for which currencies are needed.
Returns
-------
currency_codes : np.array[S3]
Array of currency codes for listing currencies of ``sids``.
"""
country_code = self._country_code_for_assets(sids)
return self._readers[country_code].currency_codes(sids)
def check_sids_arrays_match(left, right, message):
"""Check that two 1d arrays of sids are equal
"""
if len(left) != len(right):
raise ValueError(
"{}:\nlen(left) ({}) != len(right) ({})".format(
message, len(left), len(right)
)
)
diff = (left != right)
if diff.any():
(bad_locs,) = np.where(diff)
raise ValueError(
"{}:\n Indices with differences: {}".format(message, bad_locs)
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/hdf5_daily_bars.py | hdf5_daily_bars.py |
from datetime import timedelta
import pandas as pd
from zipline.data.data_portal import DataPortal
from logbook import Logger
log = Logger('DataPortalLive')
class DataPortalLive(DataPortal):
def __init__(self, broker, *args, **kwargs):
self.broker = broker
super(DataPortalLive, self).__init__(*args, **kwargs)
def get_last_traded_dt(self, asset, dt, data_frequency):
return self.broker.get_last_traded_dt(asset)
def get_spot_value(self, assets, field, dt, data_frequency):
return self.broker.get_spot_value(assets, field, dt, data_frequency)
def get_history_window(self,
assets,
end_dt,
bar_count,
frequency,
field,
data_frequency,
ffill=True):
# This method is responsible for merging the ingested historical data
# with the real-time collected data through the Broker.
# DataPortal.get_history_window() is called with ffill=False to mark
# the missing fields with NaNs. After merge on the historical and
# real-time data the missing values (NaNs) are filled based on their
# next available values in the requested time window.
#
# Warning: setting ffill=True in DataPortal.get_history_window() call
# results a wrong behavior: The last available value reported by
# get_spot_value() will be used to fill the missing data - which is
# always representing the current spot price presented by Broker.
if frequency == '1d':
# if you want today's open price - get minute data and filter the open time
historical_bars = super(DataPortalLive,
self).get_history_window(
assets,
end_dt - timedelta(days=1),
bar_count,
frequency,
field,
data_frequency,
ffill=True)
return historical_bars
realtime_bars = self.broker.get_realtime_bars(assets, frequency)
# Broker.get_realtime_history() returns the asset as level 0 column,
# open, high, low, close, volume returned as level 1 columns.
# To filter for field the levels needs to be swapped
realtime_bars = realtime_bars.swaplevel(0, 1, axis=1)
ohlcv_field = 'close' if field == 'price' else field
realtime_bars = realtime_bars[ohlcv_field]
if ffill and field == 'price':
# Simple forward fill is not enough here as the last ingested
# value might be outside of the requested time window. That case
# the time series starts with NaN and forward filling won't help.
# To provide values for such cases we backward fill.
# Backward fill as a second operation will have no effect if the
# forward-fill was successful.
realtime_bars.fillna(method='ffill', inplace=True)
realtime_bars.fillna(method='bfill', inplace=True)
realtime_bars.columns = assets
return realtime_bars[-bar_count:]
def get_scalar_asset_spot_value(self, asset, field, dt, data_frequency):
"""
Public API method that returns a scalar value representing the value
of the desired asset's field at either the given dt.
Parameters
----------
assets : Asset
The asset or assets whose data is desired. This cannot be
an arbitrary AssetConvertible.
field : {'open', 'high', 'low', 'close', 'volume',
'price', 'last_traded'}
The desired field of the asset.
dt : pd.Timestamp
The timestamp for the desired value.
data_frequency : str
The frequency of the data to query; i.e. whether the data is
'daily' or 'minute' bars
Returns
-------
value : float, int, or pd.Timestamp
The spot value of ``field`` for ``asset`` The return type is based
on the ``field`` requested. If the field is one of 'open', 'high',
'low', 'close', or 'price', the value will be a float. If the
``field`` is 'volume' the value will be a int. If the ``field`` is
'last_traded' the value will be a Timestamp.
"""
if data_frequency == 'minute':
data_frequency = '1m'
elif data_frequency == 'daily':
data_frequency = '1d'
prices = self.broker.get_realtime_bars([asset], data_frequency)
if field == 'last_traded':
return pd.Timestamp(prices[asset.symbol][-1:].index.get_values()[0])
elif field == 'volume':
return prices[asset.symbol][field][-1] * 100
elif field == 'price':
return prices[asset.symbol]['close'][-1]
else:
return prices[asset.symbol][field][-1] | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/data_portal_live.py | data_portal_live.py |
from functools import partial
import psycopg2
import sqlalchemy as sa
import zipline.config.data_backend
from zipline.utils.db_utils import check_and_create_engine
import pandas as pd
import logbook
import numpy as np
from numpy import (
iinfo,
nan,
)
from pandas import (
NaT,
read_csv,
to_datetime,
Timestamp,
)
from six import iteritems, viewkeys
from trading_calendars import get_calendar
from zipline.data.session_bars import CurrencyAwareSessionBarReader
from zipline.data.bar_reader import (
NoDataAfterDate,
NoDataBeforeDate,
NoDataOnDate,
)
from zipline.utils.functional import apply
from zipline.utils.input_validation import expect_element
from zipline.utils.numpy_utils import float64_dtype
from zipline.utils.memoize import lazyval
from zipline.utils.cli import maybe_show_progress
from ._equities import _compute_row_slices, _read_tape_data
logger = logbook.Logger('PSqlDailyBars')
OHLC = frozenset(['open', 'high', 'low', 'close'])
US_EQUITY_PRICING_COLUMNS = (
'open', 'high', 'low', 'close', 'volume', 'day', 'id'
)
UINT32_MAX = iinfo(np.uint32).max
TABLE = 'ohlcv_daily'
class PSQLDailyBarReader(CurrencyAwareSessionBarReader):
"""
Reader for raw pricing data written by PSQLDailyBarWriter.
Parameters
----------
table : bcolz.ctable
The ctable contaning the pricing data, with attrs corresponding to the
Attributes list below.
read_all_threshold : int
The number of equities at which; below, the data is read by reading a
slice from the carray per asset. above, the data is read by pulling
all of the data for all assets into memory and then indexing into that
array for each day and asset pair. Used to tune performance of reads
when using a small or large number of equities.
Attributes
----------
The table with which this loader interacts contains the following
attributes:
first_row : dict
Map from asset_id -> index of first row in the dataset with that id.
last_row : dict
Map from asset_id -> index of last row in the dataset with that id.
calendar_offset : dict
Map from asset_id -> calendar index of first row.
start_session_ns: int
Epoch ns of the first session used in this dataset.
end_session_ns: int
Epoch ns of the last session used in this dataset.
calendar_name: str
String identifier of trading calendar used (ie, "NYSE").
We use first_row and last_row together to quickly find ranges of rows to
load when reading an asset's data into memory.
We use calendar_offset and calendar to orient loaded blocks within a
range of queried dates.
Notes
------
A Bcolz CTable is comprised of Columns and Attributes.
The table with which this loader interacts contains the following columns:
['open', 'high', 'low', 'close', 'volume', 'day', 'id'].
The data in these columns is interpreted as follows:
- Price columns ('open', 'high', 'low', 'close') are interpreted as 1000 *
as-traded dollar value.
- Volume is interpreted as as-traded volume.
- Day is interpreted as seconds since midnight UTC, Jan 1, 1970.
- Id is the asset id of the row.
The data in each column is grouped by asset and then sorted by day within
each asset block.
The table is built to represent a long time range of data, e.g. ten years
of equity data, so the lengths of each asset block is not equal to each
other. The blocks are clipped to the known start and end date of each asset
to cut down on the number of empty values that would need to be included to
make a regular/cubic dataset.
When read across the open, high, low, close, and volume with the same
index should represent the same asset and day.
See Also
--------
zipline.data.bcolz_daily_bars.BcolzDailyBarWriter
"""
def __init__(self, path, read_all_threshold=3000):
self.conn = check_and_create_engine(path, False)
# Cache of fully read np.array for the carrays in the daily bar table.
# raw_array does not use the same cache, but it could.
# Need to test keeping the entire array in memory for the course of a
# process first.
self._spot_cols = {}
self._read_all_threshold = read_all_threshold
# caching the calendar-sessions like this prevent problems during ingestion
# where the reader is first initialized when there are still no bars
self._sessions = pd.DatetimeIndex([], dtype='datetime64[ns, UTC]', freq='C')
self._calendar_offsets_c = {}
self._first_rows_c = {}
self._last_rows_c = {}
self._first_trading_day_c = {}
@property
def sessions(self):
if self._sessions.empty:
outer_dates = pd.read_sql('SELECT MIN(day) as min_day, MAX(day) as max_day FROM ohlcv_daily', self.conn)
start_session = Timestamp(outer_dates['min_day'][0], tz='UTC')
end_session = Timestamp(outer_dates['max_day'][0], tz='UTC')
calendar_name = 'XNYS' # NYSE for POC only
cal = get_calendar(calendar_name)
self._sessions = cal.sessions_in_range(start_session, end_session)
return self._sessions
@lazyval
def first_trading_day(self):
return Timestamp(
self._first_trading_day,
unit='s',
tz='UTC'
)
@lazyval
def trading_calendar(self):
return get_calendar('XNYS')
@property
def last_available_dt(self):
return self.sessions[-1]
@property
def _calendar_offsets(self):
if not self._calendar_offsets_c:
self._calendar_offsets_c = self._get_calendar_offsets()
return self._calendar_offsets_c
def _get_calendar_offsets(self):
info = pd.read_sql('SELECT id, MIN(day) AS start FROM ohlcv_daily GROUP BY id ORDER BY id', self.conn)
sessions = self.sessions
if len(sessions) == 0:
return {}
offsets = {}
for i in range(len(info['id'])):
first_session = Timestamp(info['start'][i], tz='UTC')
offsets[info['id'][i]] = sessions.get_loc(first_session)
return offsets
@property
def _first_trading_day(self):
if not self._first_trading_day_c:
self._first_trading_day_c = self._get_first_trading_day()
return self._first_trading_day_c
@property
def _last_rows(self):
if not self._last_rows_c:
self._first_rows_c, self._last_rows_c = self._get_first_and_last_rows()
return self._last_rows_c
@property
def _first_rows(self):
if not self._first_rows_c:
self._first_rows_c, self._last_rows_c = self._get_first_and_last_rows()
return self._first_rows_c
def _get_first_and_last_rows(self):
info = pd.read_sql('SELECT id, COUNT(day) AS ct FROM ohlcv_daily GROUP BY id ORDER BY id', self.conn)
first_rows = {}
last_rows = {}
total = 0
length = len(info['id'])
for i in range(length):
total = total + info['ct'][i]
if i == 0:
first_rows[info['id'][i]] = 0
last_rows[info['id'][i]] = total - 1
if i > 0:
first_rows[info['id'][i]] = last_rows[last_id] + 1
last_id = info['id'][i]
return first_rows, last_rows
def _get_first_trading_day(self):
result = pd.read_sql('SELECT MIN(day) AS first_day FROM ohlcv_daily', self.conn)
return result.first_day.iloc[0]
def _compute_slices(self, start_idx, end_idx, assets):
"""
Compute the raw row indices to load for each asset on a query for the
given dates after applying a shift.
Parameters
----------
start_idx : int
Index of first date for which we want data.
end_idx : int
Index of last date for which we want data.
assets : pandas.Int64Index
Assets for which we want to compute row indices
Returns
-------
A 3-tuple of (first_rows, last_rows, offsets):
first_rows : np.array[intp]
Array with length == len(assets) containing the index of the first
row to load for each asset in `assets`.
last_rows : np.array[intp]
Array with length == len(assets) containing the index of the last
row to load for each asset in `assets`.
offset : np.array[intp]
Array with length == (len(asset) containing the index in a buffer
of length `dates` corresponding to the first row of each asset.
The value of offset[i] will be 0 if asset[i] existed at the start
of a query. Otherwise, offset[i] will be equal to the number of
entries in `dates` for which the asset did not yet exist.
"""
# The core implementation of the logic here is implemented in Cython
# for efficiency.
return _compute_row_slices(
self._first_rows,
self._last_rows,
self._calendar_offsets,
start_idx,
end_idx,
assets,
)
def _load_raw_arrays_date_to_index(self, date):
try:
return self.sessions.get_loc(date)
except KeyError:
raise NoDataOnDate(date)
def load_raw_arrays(self, columns, start_date, end_date, assets):
for col in columns:
self._spot_col(col)
start_idx = self._load_raw_arrays_date_to_index(start_date)
end_idx = self._load_raw_arrays_date_to_index(end_date)
first_rows, last_rows, offsets = self._compute_slices(
start_idx,
end_idx,
assets,
)
read_all = len(assets) > self._read_all_threshold
tape = _read_tape_data(
self._spot_cols,
(end_idx - start_idx + 1, len(assets)),
list(columns),
first_rows,
last_rows,
offsets,
read_all,
)
return tape
def load_raw_arrays_slow(self, columns, start_date, end_date, assets):
result = []
sessions = self.sessions[self.sessions.get_loc(start_date): self.sessions.get_loc(end_date) + 1]
for column in columns:
column_vals = []
for session in sessions:
row_vals = []
for asset in assets:
try:
row_vals.append(self.get_value(int(asset), session, column))
except NoDataBeforeDate:
row_vals.append(np.nan)
column_vals.append(row_vals)
result.append(np.array(column_vals))
return result
def _spot_col(self, colname):
"""
Get the colname from daily_bar_table and read all of it into memory,
caching the result.
Parameters
----------
colname : string
A name of a OHLCV carray in the daily_bar_table
Returns
-------
array (uint32)
Full read array of the carray in the daily_bar_table with the
given colname.
"""
try:
col = self._spot_cols[colname]
except KeyError:
result = pd.read_sql(f'SELECT {colname} FROM ohlcv_daily ORDER BY id, day', self.conn)[colname].values
col = self._spot_cols[colname] = np.array(result)
return col
def get_last_traded_dt(self, asset, day):
volumes = self._spot_col('volume')
search_day = day
while True:
try:
ix = self.sid_day_index(asset, search_day)
except NoDataBeforeDate:
return NaT
except NoDataAfterDate:
prev_day_ix = self.sessions.get_loc(search_day) - 1
if prev_day_ix > -1:
search_day = self.sessions[prev_day_ix]
continue
except NoDataOnDate:
return NaT
if volumes[ix] != 0:
return search_day
prev_day_ix = self.sessions.get_loc(search_day) - 1
if prev_day_ix > -1:
search_day = self.sessions[prev_day_ix]
else:
return NaT
def sid_day_index(self, sid, day):
"""
all data for all assets is stored sequentially. to get the right values we must find the index
for this sid and this day. so we calculate the offset in this long array.
Parameters
----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
Returns
-------
int
Index into the data tape for the given sid and day.
Raises a NoDataOnDate exception if the given day and sid is before
or after the date range of the equity.
"""
try:
day_loc = self.sessions.get_loc(day)
except Exception:
raise NoDataOnDate("day={0} is outside of calendar={1}".format(
day, self.sessions))
offset = day_loc - self._calendar_offsets[sid]
if offset < 0:
raise NoDataBeforeDate(
"No data on or before day={0} for sid={1}".format(
day, sid))
ix = self._first_rows[sid] + offset
if ix > self._last_rows[sid]:
raise NoDataAfterDate(
"No data on or after day={0} for sid={1}".format(
day, sid))
return ix
def get_value(self, sid, dt, field):
"""
Parameters
----------
sid : int
The asset identifier.
day : datetime64-like
Midnight of the day for which data is requested.
colname : string
The price field. e.g. ('open', 'high', 'low', 'close', 'volume')
Returns
-------
float
The spot price for colname of the given sid on the given day.
Raises a NoDataOnDate exception if the given day and sid is before
or after the date range of the equity.
Returns -1 if the day is within the date range, but the price is
0.
"""
ix = self.sid_day_index(sid, dt)
price = self._spot_col(field)[ix]
if field != 'volume':
if price == 0:
return nan
else:
return price
else:
return price
def currency_codes(self, sids):
# XXX: This is pretty inefficient. This reader doesn't really support
# country codes, so we always either return USD or None if we don't
# know about the sid at all.
first_rows = self._first_rows
out = []
for sid in sids:
if sid in first_rows:
out.append('USD')
else:
out.append(None)
return np.array(out, dtype=object)
class PSQLDailyBarWriter(object):
"""
Class capable of writing daily OHLCV data to disk in a format that can
be read efficiently by PSQLDailyOHLCVReader.
Parameters
----------
filename : str
The location at which we should write our output.
calendar : zipline.utils.calendar.trading_calendar
Calendar to use to compute asset calendar offsets.
start_session: pd.Timestamp
Midnight UTC session label.
end_session: pd.Timestamp
Midnight UTC session label.
See Also
--------
zipline.data.bcolz_daily_bars.BcolzDailyBarReader
"""
_csv_dtypes = {
'open': float64_dtype,
'high': float64_dtype,
'low': float64_dtype,
'close': float64_dtype,
'volume': float64_dtype,
}
def __init__(self, db_path, calendar, start_session, end_session):
self.conn = check_and_create_engine(db_path, False)
if start_session != end_session:
if not calendar.is_session(start_session):
raise ValueError(
"Start session %s is invalid!" % start_session
)
if not calendar.is_session(end_session):
raise ValueError(
"End session %s is invalid!" % end_session
)
self._start_session = start_session
self._end_session = end_session
self._calendar = calendar
try:
self.conn.connect()
except sa.exc.OperationalError:
# can't connect to db. might mean that the database is not created yey.
# let's create it. (happens in first time usage)
self.ensure_database(db_path)
self.ensure_table()
def ensure_database(self, db_path):
"""
create the bundle database. it will have the name of the bundle
:param db_path: expected db path (table). used to get the bundle name.
"""
db_config = zipline.config.data_backend.PostgresDB()
host = db_config.host
port = db_config.port
user = db_config.user
password = db_config.password
conn = psycopg2.connect(
database="",
user=user,
password=password,
host=host,
port=port
)
conn.autocommit = True
# Creating a cursor object using the cursor() method
cursor = conn.cursor()
bundle_name = db_path.split("/")[-1]
sql = f'CREATE database {bundle_name}'
# Creating a database
cursor.execute(sql)
print(f"Database {bundle_name} created successfully........")
def ensure_table(self):
metadata = sa.MetaData()
ohlcv_daily = sa.Table(
'ohlcv_daily',
metadata,
sa.Column('id', sa.Integer()),
sa.Column('day', sa.Date()),
sa.Column('open', sa.Float()),
sa.Column('high', sa.Float()),
sa.Column('low', sa.Float()),
sa.Column('close', sa.Float()),
sa.Column('volume', sa.BigInteger()),
)
sa.Index('id_day', ohlcv_daily.c.id, ohlcv_daily.c.day)
metadata.create_all(self.conn)
@property
def progress_bar_message(self):
return "Merging daily equity files:"
def progress_bar_item_show_func(self, value):
return value if value is None else str(value[0])
def write(self,
data,
assets=None,
show_progress=False,
invalid_data_behavior='warn'):
"""
Parameters
----------
data : iterable[tuple[int, pandas.DataFrame or bcolz.ctable]]
The data chunks to write. Each chunk should be a tuple of sid
and the data for that asset.
assets : set[int], optional
The assets that should be in ``data``. If this is provided
we will check ``data`` against the assets and provide better
progress information.
show_progress : bool, optional
Whether or not to show a progress bar while writing.
invalid_data_behavior : {'warn', 'raise', 'ignore'}, optional
What to do when data is encountered that is outside the range of
a uint32.
Returns
-------
table : bcolz.ctable
The newly-written table.
"""
ctx = maybe_show_progress(
(
(sid, self._write_to_postgres(sid, df, invalid_data_behavior))
for sid, df in data
),
show_progress=show_progress,
item_show_func=self.progress_bar_item_show_func,
label=self.progress_bar_message,
length=len(assets) if assets is not None else None,
)
with ctx as it:
return self._write_internal(it, assets)
def write_csvs(self,
asset_map,
show_progress=False,
invalid_data_behavior='warn'):
"""Read CSVs as DataFrames from our asset map.
Parameters
----------
asset_map : dict[int -> str]
A mapping from asset id to file path with the CSV data for that
asset
show_progress : bool
Whether or not to show a progress bar while writing.
invalid_data_behavior : {'warn', 'raise', 'ignore'}
What to do when data is encountered that is outside the range of
a uint32.
"""
read = partial(
read_csv,
parse_dates=['day'],
index_col='day',
dtype=self._csv_dtypes,
)
return self.write(
((asset, read(path)) for asset, path in iteritems(asset_map)),
assets=viewkeys(asset_map),
show_progress=show_progress,
invalid_data_behavior=invalid_data_behavior,
)
def _write_internal(self, iterator, assets):
"""
Internal implementation of write.
`iterator` should be an iterator yielding pairs of (asset, dataframe).
"""
if assets is not None:
@apply
def iterator(iterator=iterator, assets=set(assets)):
for asset_id, table in iterator:
if asset_id not in assets:
logger.warning(f"unknown asset id {asset_id}. skipping.")
continue
yield asset_id, table
for asset_id, table in iterator:
# when writing to db, drop timezone, will crash otherwise
if not table.empty:
table.index = table.index.tz_localize(None)
table.to_sql('ohlcv_daily', self.conn, if_exists='append')
def _ensure_sessions_consistency(self, data_slice, invalid_data_behavior):
"""
check that we have exactly the amount of days we expect by checking the start and end dates
counting the active days in between using the trading calendar data
"""
val = True
if not data_slice.empty:
first_day = data_slice.index[0]
last_day = data_slice.index[-1]
asset_sessions = self._calendar.sessions_in_range(first_day, last_day)
if len(data_slice) != len(asset_sessions):
err_msg = (
'Got {} rows for daily bars table with first day={}, last '
'day={}, expected {} rows.\n'
'Missing sessions: {}\n'
'Extra sessions: {}'.format(
len(data_slice),
first_day,
last_day,
len(asset_sessions),
asset_sessions.difference(
to_datetime(
np.array(data_slice.index),
unit='s',
utc=True,
)
).tolist(),
to_datetime(
np.array(data_slice.index),
unit='s',
utc=True,
).difference(asset_sessions).tolist(),
)
)
val = False
logger.warning(err_msg)
return val
@expect_element(invalid_data_behavior={'warn', 'raise', 'ignore'})
def _write_to_postgres(self, sid, data: pd.DataFrame, invalid_data_behavior):
result = self._format_df_columns_and_index(data, sid)
if not result.empty:
# set proper id
data['id'] = sid
edge_days = self._get_exisiting_data_dates_from_db(sid)
if not self._data_for_sid_already_exist_in_db(edge_days):
# this asset is still not in the DB. we write everything we got
if self._ensure_sessions_consistency(data, invalid_data_behavior):
# data is not consistent. we will not write anything to db
result = data
else:
result = pd.DataFrame(columns=data.columns)
else:
result = self._validate_data_consistency_on_edges(sid, data, edge_days, invalid_data_behavior)
return result
def _validate_data_consistency_on_edges(self, sid, data, edge_days, invalid_data_behavior):
"""
there's already data in the db for this sid. we may append data at the beginning and/or end.
before we do that, we must make sure that both segments are consistent.
note: we could make a better effort by loosing up restriction and if one segment is corrupted still accept
the other one.
"""
first_day = edge_days['first_day'][0]
last_day = edge_days['last_day'][0]
before_slice = data[data.index.tz_convert(None) < first_day]
after_slice = data[data.index.tz_convert(None) > last_day]
# check if before-slice and after-slice are aligned with data in db
# e.g. don't allow gaps in terms of sessions. should be exactly two
# sessions (sessions on the edge of the data and the slice)
consistent_data = True
if not before_slice.empty:
backward_gap = len(self._calendar.sessions_in_range(before_slice.index[-1], first_day))
if backward_gap != 2:
# max allowed gap for consistent data is 2
logger.warning(f"data for {sid} contains backward gaps {backward_gap} "
f"and not consistent. will not be written to db.")
consistent_data = False
if not after_slice.empty:
forward_gap = len(self._calendar.sessions_in_range(last_day, after_slice.index[-1]))
if forward_gap != 2:
logger.warning(f"data for {sid} contains forward gaps {forward_gap} "
f"and not consistent. will not be written to db.")
consistent_data = False
if not self._ensure_sessions_consistency(before_slice, invalid_data_behavior) or not \
self._ensure_sessions_consistency(after_slice, invalid_data_behavior):
consistent_data = False
if consistent_data:
result = before_slice.append(after_slice)
else:
result = pd.DataFrame(columns=data.columns)
return result
def _data_for_sid_already_exist_in_db(self, edges: pd.DataFrame) -> bool:
"""
edges is a query performed for sid in db. if it's empty it means the db doesn't contain data for this sid yet.
:return: bool
"""
return not pd.isnull(edges['first_day'].iloc[0])
def _get_exisiting_data_dates_from_db(self, sid):
"""
using the sid- query the db and get the dates (start and end) for data stored in db
:param sid:
:return:
"""
edge_days = pd.read_sql(
f'SELECT MAX(day) as last_day, MIN(day) as first_day '
f'FROM ohlcv_daily WHERE id = {sid}',
self.conn,
parse_dates=['last_day', 'first_day']
)
return edge_days
def _format_df_columns_and_index(self, data: pd.DataFrame, sid):
"""
make sure that the data received is in the structure we expect columns and index wise.
:param data: data from data bundle
:param sid: sid as it should be stored in db
:return: formatted data or empty df if the data is corrupted
"""
result = pd.DataFrame(columns=data.columns)
# rename index-column to day and convert it to datetime and utc
if data.index[0].tzname() != 'UTC':
data.index = [x.tz_convert('utc') for x in data.index]
data.index.rename("day", inplace=True)
# drop time-information, it will confuse the aligning-logic
data.index = data.index.normalize()
# check if we have all necessary columns
corrupted_data = False
for column in US_EQUITY_PRICING_COLUMNS:
# id not necessary
if column == 'id':
continue
if column not in list(data.columns) + [data.index.name]:
msg = f"corrupted data for :{sid}. columns must contain day, open, high, low, close, volume"
logger.warning(msg)
corrupted_data = True
break
if not corrupted_data:
# drop columns not of interest
cols_to_drop = [column for column in data.columns if column not in US_EQUITY_PRICING_COLUMNS]
data.drop(columns=cols_to_drop, inplace=True)
result = data
return result | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/psql_daily_bars.py | psql_daily_bars.py |
from abc import ABCMeta, abstractmethod, abstractproperty
from six import with_metaclass
class NoDataOnDate(Exception):
"""
Raised when a spot price cannot be found for the sid and date.
"""
pass
class NoDataBeforeDate(NoDataOnDate):
pass
class NoDataAfterDate(NoDataOnDate):
pass
class NoDataForSid(Exception):
"""
Raised when the requested sid is missing from the pricing data.
"""
pass
OHLCV = ('open', 'high', 'low', 'close', 'volume')
class BarReader(with_metaclass(ABCMeta, object)):
@abstractproperty
def data_frequency(self):
pass
@abstractmethod
def load_raw_arrays(self, columns, start_date, end_date, assets):
"""
Parameters
----------
columns : list of str
'open', 'high', 'low', 'close', or 'volume'
start_date: Timestamp
Beginning of the window range.
end_date: Timestamp
End of the window range.
assets : list of int
The asset identifiers in the window.
Returns
-------
list of np.ndarray
A list with an entry per field of ndarrays with shape
(minutes in range, sids) with a dtype of float64, containing the
values for the respective field over start and end dt range.
"""
pass
@abstractproperty
def last_available_dt(self):
"""
Returns
-------
dt : pd.Timestamp
The last session for which the reader can provide data.
"""
pass
@abstractproperty
def trading_calendar(self):
"""
Returns the zipline.utils.calendar.trading_calendar used to read
the data. Can be None (if the writer didn't specify it).
"""
pass
@abstractproperty
def first_trading_day(self):
"""
Returns
-------
dt : pd.Timestamp
The first trading day (session) for which the reader can provide
data.
"""
pass
@abstractmethod
def get_value(self, sid, dt, field):
"""
Retrieve the value at the given coordinates.
Parameters
----------
sid : int
The asset identifier.
dt : pd.Timestamp
The timestamp for the desired data point.
field : string
The OHLVC name for the desired data point.
Returns
-------
value : float|int
The value at the given coordinates, ``float`` for OHLC, ``int``
for 'volume'.
Raises
------
NoDataOnDate
If the given dt is not a valid market minute (in minute mode) or
session (in daily mode) according to this reader's tradingcalendar.
"""
pass
@abstractmethod
def get_last_traded_dt(self, asset, dt):
"""
Get the latest minute on or before ``dt`` in which ``asset`` traded.
If there are no trades on or before ``dt``, returns ``pd.NaT``.
Parameters
----------
asset : zipline.asset.Asset
The asset for which to get the last traded minute.
dt : pd.Timestamp
The minute at which to start searching for the last traded minute.
Returns
-------
last_traded : pd.Timestamp
The dt of the last trade for the given asset, using the input
dt as a vantage point.
"""
pass | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bar_reader.py | bar_reader.py |
import alpaca_trade_api as tradeapi
from datetime import timedelta
import numpy as np
from os.path import isfile, join
from pathlib import Path
import pandas as pd
import pickle
from alpaca_trade_api.common import URL
from dateutil import tz
from trading_calendars import TradingCalendar
import yaml
from zipline.data.bundles import core as bundles
from dateutil.parser import parse as date_parse
user_home = str(Path.home())
custom_data_path = join(user_home, '.zipline/custom_data')
CLIENT: tradeapi.REST = None
NY = "America/New_York"
def initialize_client():
global CLIENT
with open("polygon.yaml", mode='r') as f:
o = yaml.safe_load(f)
key = o["key_id"]
secret = o["secret"]
base_url = o["base_url"]
CLIENT = tradeapi.REST(key_id=key,
secret_key=secret,
base_url=URL(base_url))
ASSETS = None
def list_assets():
global ASSETS
if not ASSETS:
ASSETS = [_.symbol for _ in CLIENT.list_assets()]
# ASSETS = [_.ticker for _ in CLIENT.polygon.all_tickers()]
return ASSETS
# return ['AAPL', 'AA', 'TSLA', 'GOOG', 'MSFT']
def tickers_generator():
"""
Return a tuple (sid, ticker_pair)
"""
tickers_file = join(custom_data_path, 'alpaca_ticker_pairs.pickle')
if not isfile(tickers_file):
ticker_pairs = list_assets()
else:
with open(tickers_file, 'rb') as f:
ticker_pairs = pickle.load(f)[:]
return (tuple((sid, ticker)) for sid, ticker in enumerate(ticker_pairs))
def iso_date(date_str):
"""
this method will make sure that dates are formatted properly
as with isoformat
:param date_str:
:return: YYYY-MM-DD date formatted
"""
return date_parse(date_str).date().isoformat()
def get_aggs_from_polygon(dataname,
dtbegin,
dtend,
granularity,
compression):
"""
so polygon has a much more convenient api for this than alpaca because
we could insert the compression in to the api call and we don't need to
resample it. but, at this point in time, something is not working
properly and data is returned in segments. meaning, we have patches of
missing data. e.g we request data from 2020-03-01 to 2020-07-01 and we
get something like this: 2020-03-01:2020-03-15, 2020-06-25:2020-07-01
so that makes life difficult.. there's no way to know which patch will
be returned and which one we should try to get again.
so the solution must be, ask data in segments. I select an arbitrary
time window of 2 weeks, and split the calls until we get all required
data
"""
def _clear_out_of_market_hours(df):
"""
only interested in samples between 9:30, 16:00 NY time
"""
return df.between_time("09:30", "16:00")
def _fillna(df, granularity, start, end):
if granularity != 'day':
return df
if df.empty:
return df
calendar: TradingCalendar = trading_calendars.get_calendar("NYSE")
last_val = df.iloc[0]
current = start
while current <= end:
if calendar.is_session(current):
if current.replace(tzinfo=tz.gettz(NY)) in df.index:
last_val = df.loc[current.replace(tzinfo=tz.gettz(NY))]
else:
# df.loc[pytz.timezone(NY).localize(current)] = last_val
df.loc[current.replace(tzinfo=tz.gettz(NY))] = last_val
current += timedelta(days=1)
return df
if granularity == 'day':
cdl = CLIENT.polygon.historic_agg_v2(
dataname,
compression,
granularity,
_from=iso_date(dtbegin.isoformat()),
to=iso_date(dtend.isoformat())).df
cdl = _fillna(cdl, granularity, dtbegin, dtend)
else:
cdl = pd.DataFrame()
segment_start = dtbegin
segment_end = segment_start + timedelta(weeks=2) if \
dtend - dtbegin >= timedelta(weeks=2) else dtend
while segment_end <= dtend and dtend not in cdl.index:
response = CLIENT.polygon.historic_agg_v2(
dataname,
compression,
granularity,
_from=iso_date(segment_start.isoformat()),
to=iso_date(segment_end.isoformat()))
# No result from the server, most likely error
if response.df.shape[0] == 0 and cdl.shape[0] == 0:
raise Exception("received empty response")
temp = response.df
cdl = pd.concat([cdl, temp])
cdl = cdl[~cdl.index.duplicated()]
segment_start = segment_end
segment_end = segment_start + timedelta(weeks=2) if \
dtend - dtbegin >= timedelta(weeks=2) else dtend
cdl = _clear_out_of_market_hours(cdl)
return cdl
def df_generator(interval, start, end):
exchange = 'NYSE'
for sid, symbol in enumerate(list_assets()):
try:
df = get_aggs_from_polygon(symbol, start, end, 'day' if interval == '1d' else 'minute', 1)
if df.empty:
continue
start_date = df.index[0]
end_date = df.index[-1]
first_traded = start
auto_close_date = end + pd.Timedelta(days=1)
# # Check if there is any missing session; skip the ticker pair otherwise
# if interval == '1d' and len(df.index) - 1 != pd.Timedelta(end_date - start_date).days:
# # print('Missing sessions found in {}. Skip importing'.format(ticker_pair))
# continue
# elif interval == '1m' and timedelta(minutes=(len(df.index) + 60)) != end_date - start_date:
# # print('Missing sessions found in {}. Skip importing'.format(ticker_pair))
# continue
yield (sid, df.sort_index()), symbol, start, end, first_traded, auto_close_date, exchange
except Exception as e:
import traceback
traceback.print_exc()
print(f"error while processig {(sid, symbol)}: {e}")
def metadata_df():
metadata_dtype = [
('symbol', 'object'),
# ('asset_name', 'object'),
('start_date', 'datetime64[ns]'),
('end_date', 'datetime64[ns]'),
('first_traded', 'datetime64[ns]'),
('auto_close_date', 'datetime64[ns]'),
('exchange', 'object'), ]
metadata_df = pd.DataFrame(
np.empty(len(list_assets()), dtype=metadata_dtype))
return metadata_df
@bundles.register('polygon_api', calendar_name="NYSE", minutes_per_day=390)
def api_to_bundle(interval=['1m']):
def ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir
):
def minute_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in df_generator(interval='1m',
start=start_session,
end=end_session))
def daily_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in df_generator(interval='1d',
start=start_session,
end=end_session))
for _interval in interval:
metadata = metadata_df()
if _interval == '1d':
daily_bar_writer.write(daily_data_generator(), show_progress=True)
elif _interval == '1m':
minute_bar_writer.write(
minute_data_generator(), show_progress=True)
# Drop the ticker rows which have missing sessions in their data sets
metadata.dropna(inplace=True)
asset_db_writer.write(equities=metadata)
print(metadata)
adjustment_writer.write()
return ingest
if __name__ == '__main__':
from zipline.data.bundles import register
from zipline.data import bundles as bundles_module
import trading_calendars
import os
cal: TradingCalendar = trading_calendars.get_calendar('NYSE')
start_date = pd.Timestamp('2019-08-03 0:00', tz='utc')
while not cal.is_session(start_date):
start_date += timedelta(days=1)
end_date = pd.Timestamp('now', tz='utc').date() - timedelta(days=1)
while not cal.is_session(end_date):
end_date -= timedelta(days=1)
end_date = pd.Timestamp(end_date, tz='utc')
initialize_client()
register(
'polygon_api',
# api_to_bundle(interval=['1d', '1m']),
# api_to_bundle(interval=['1m']),
api_to_bundle(interval=['1d']),
calendar_name='NYSE',
start_session=start_date,
end_session=end_date
)
assets_version = ((),)[0] # just a weird way to create an empty tuple
bundles_module.ingest(
"polygon_api",
os.environ,
assets_versions=assets_version,
show_progress=True,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/___polygon_api.py | ___polygon_api.py |
import os
import bs4 as bs
from binance.client import Client
import csv
from datetime import datetime as dt
from datetime import timedelta
import numpy as np
from os import listdir, mkdir, remove
from os.path import exists, isfile, join
from pathlib import Path
import pandas as pd
import pickle
import requests
from trading_calendars import register_calendar
# from trading_calendars.exchange_calendar_binance import BinanceExchangeCalendar
import yaml
from zipline.data.bundles import core as bundles
# Set up the directories where we are going to save those csv files
user_home = str(Path.home())
csv_data_path = join(os.environ["ZIPLINE_ROOT"], 'custom_data/binance/csv')
custom_data_path = join(user_home, 'custom_data/binance')
CLIENT: Client = None
def initialize_client():
global CLIENT
with open("binance.yaml", mode='r') as f:
o = yaml.safe_load(f)
key = o["key_id"]
secret = o["secret"]
CLIENT = Client(key, secret)
def get_binance_pairs(**kwargs):
base_currencies = kwargs.get('base_currencies', '')
quote_currencies = kwargs.get('quote_currencies', '')
binance_pairs = list()
all_tickers = CLIENT.get_all_tickers()
# if not self.futures:
# all_tickers = CLIENT.get_all_tickers()
# else:
# all_tickers = CLIENT.futures_ticker()
if base_currencies and quote_currencies:
input_pairs = [x + y for x in quote_currencies for y in base_currencies]
for x, currency_pair in enumerate(all_tickers):
if base_currencies and quote_currencies:
for pair in input_pairs:
if currency_pair['symbol'] == pair.upper():
binance_pairs.append(currency_pair['symbol'])
break
elif base_currencies:
for base_currency in base_currencies:
if currency_pair['symbol'][-len(base_currency):] == base_currency.upper():
binance_pairs.append(currency_pair['symbol'])
break
elif quote_currencies:
for quote_currency in quote_currencies:
if currency_pair['symbol'][:len(quote_currency)] == quote_currency.upper():
binance_pairs.append(currency_pair['symbol'])
break
else:
binance_pairs.append(currency_pair['symbol'])
if binance_pairs:
return binance_pairs
else:
raise ValueError('Invalid Input: Binance returned no matching currency pairs.')
def tickers():
"""
Save Binance trading pair tickers to a pickle file
Return a pickle
"""
cmc_binance_url = 'https://coinmarketcap.com/exchanges/binance/'
response = requests.get(cmc_binance_url)
if response.ok:
soup = bs.BeautifulSoup(response.text, 'html.parser')
table = soup.find('table', {'id': 'exchange-markets'})
ticker_pairs = []
for row in table.findAll('tr')[1:]:
ticker_pair = row.findAll('td')[2].text
ticker_pairs.append(ticker_pair.strip().replace('/', ''))
if not exists(custom_data_path):
mkdir(custom_data_path)
with open(join(custom_data_path, 'binance_ticker_pairs.pickle'), 'wb') as f:
pickle.dump(ticker_pairs, f)
return ticker_pairs
def save_csv(reload_tickers=True, interval='1m'):
"""
Save Zipline bundle ready csv for Binance trading ticker pair
:param reload_tickers: True or False
:type reload_tickers: boolean
:param interval: Default 1m. 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h, 1d, 3d, 1w, 1M
:type interval: str
"""
if not exists(csv_data_path):
mkdir(csv_data_path)
if reload_tickers:
ticker_pairs = get_binance_pairs()
else:
ticker_pickle = join(
custom_data_path, 'binance_ticker_pairs.pickle')
with open(ticker_pickle, 'rb') as f:
ticker_pairs = pickle.load(f)
start = '2017-7-14' # Binance launch date
end = dt.utcnow().strftime('%Y-%m-%d') # Current day
csv_filenames = [csv_filename for csv_filename in listdir(
csv_data_path) if isfile(join(csv_data_path, csv_filename))]
for _interval in interval:
for ticker_pair in ticker_pairs:
filename = "Binance_{}_{}.csv".format(ticker_pair, _interval)
if csv_filenames != [] and filename in csv_filenames:
remove(join(csv_data_path, filename))
output = join(csv_data_path, filename)
klines = CLIENT.get_historical_klines_generator(
ticker_pair, _interval, start, end)
for index, kline in enumerate(klines):
with open(output, 'a+') as f:
writer = csv.writer(f)
if index == 0:
writer.writerow(
['date', 'open', 'high', 'low', 'close', 'volume'])
# Make a real copy of kline
# Binance API forbids the change of open time
line = kline[:]
del line[6:]
line[0] = np.datetime64(line[0], 'ms')
line[0] = pd.Timestamp(line[0], 'ms')
writer.writerow(line)
print('{} saved.'.format(filename))
return [file for file in listdir(csv_data_path) if isfile(join(csv_data_path, file))]
@bundles.register('binance_CSV', calendar_name="24/7", minutes_per_day=1440)
def csv_to_bundle(reload_tickers=True, reload_csv=True, interval='1m'):
def ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir
):
if reload_csv:
csv_filenames = save_csv(
reload_tickers=reload_tickers, interval=interval)
else:
csv_filenames = [file for file in listdir(
csv_data_path) if isfile(join(csv_data_path, file))]
ticker_pairs = [{'exchange': pair.split('_')[0],
'symbol': pair.split('_')[1],
'interval':pair.split('_')[2].split('.')[0],
'file_path':join(csv_data_path, pair)}
for pair in csv_filenames]
metadata_dtype = [
('symbol', 'object'),
('asset_name', 'object'),
('start_date', 'datetime64[ns]'),
('end_date', 'datetime64[ns]'),
('first_traded', 'datetime64[ns]'),
('auto_close_date', 'datetime64[ns]'),
('exchange', 'object'), ]
metadata = pd.DataFrame(
np.empty(len(ticker_pairs), dtype=metadata_dtype))
minute_data_sets = []
daily_data_sets = []
for sid, ticker_pair in enumerate(ticker_pairs):
df = pd.read_csv(ticker_pair['file_path'],
index_col=['date'],
parse_dates=['date'])
symbol = ticker_pair['symbol']
asset_name = ticker_pair['symbol']
start_date = df.index[0]
end_date = df.index[-1]
first_traded = start_date
auto_close_date = end_date + pd.Timedelta(days=1)
exchange = ticker_pair['exchange']
# Update metadata
metadata.iloc[sid] = symbol, asset_name, start_date, end_date, first_traded, auto_close_date, exchange
if ticker_pair['interval'] == '1m':
minute_data_sets.append((sid, df))
if ticker_pair['interval'] == '1d':
daily_data_sets.append((sid, df))
if minute_data_sets != []:
# Dealing with missing sessions in some data sets
for daily_data_set in daily_data_sets:
try:
minute_bar_writer.write(
[daily_data_set], show_progress=True)
except Exception as e:
print(e)
if daily_data_sets != []:
# Dealing with missing sessions in some data sets
for daily_data_set in daily_data_sets:
try:
daily_bar_writer.write(
[daily_data_set], show_progress=True)
except Exception as e:
print(e)
metadata['exchange'] = "Binance"
asset_db_writer.write(equities=metadata)
print(metadata)
adjustment_writer.write()
return ingest
if __name__ == '__main__':
from zipline.data.bundles import register
from zipline.data import bundles as bundles_module
import os
initialize_client()
register(
'binance_csv',
# csv_to_bundle(interval=['1d', '1m']),
csv_to_bundle(interval=['1m']),
# csv_to_bundle(interval=['1d']),
calendar_name='24/7',
)
assets_version = ((),)[0] # just a weird way to create an empty tuple
bundles_module.ingest(
"binance_csv",
os.environ,
pd.Timestamp.utcnow(),
assets_version,
True,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/binance_csv.py | binance_csv.py |
import collections
import alpaca_trade_api as tradeapi
from datetime import timedelta, time as dtime
import numpy as np
from pathlib import Path
import pandas as pd
import pytz
from alpaca_trade_api.common import URL
from dateutil import tz
from trading_calendars import TradingCalendar
import zipline.config
from zipline.data.bundles import core as bundles
from zipline.data.bundles.common import asset_to_sid_map
from zipline.data.bundles.universe import Universe, all_alpaca_assets, get_sp500, get_sp100, get_nasdaq100
from dateutil.parser import parse as date_parse
user_home = str(Path.home())
CLIENT: tradeapi.REST = None
NY = "America/New_York"
def initialize_client():
global CLIENT
conf = zipline.config.bundle.AlpacaConfig()
key = conf.key
secret = conf.secret
base_url = conf.base_url
CLIENT = tradeapi.REST(key_id=key,
secret_key=secret,
base_url=URL(base_url))
ASSETS = None
def list_assets():
global ASSETS
if not ASSETS:
conf = zipline.config.bundle.AlpacaConfig()
custom_asset_list = conf.custom_asset_list
if custom_asset_list:
custom_asset_list = custom_asset_list.strip().replace(" ", "").split(",")
ASSETS = list(set(custom_asset_list))
else:
try:
universe = Universe[conf.universe]
except:
universe = Universe.ALL
if universe == Universe.ALL:
ASSETS = all_alpaca_assets(CLIENT)
elif universe == Universe.SP100:
ASSETS = get_sp100()
elif universe == Universe.SP500:
ASSETS = get_sp500()
elif universe == Universe.NASDAQ100:
ASSETS = get_nasdaq100()
ASSETS = list(set(ASSETS))
return ASSETS
def iso_date(date_str):
"""
this method will make sure that dates are formatted properly
as with isoformat
:param date_str:
:return: YYYY-MM-DD date formatted
"""
return date_parse(date_str).date().isoformat()
def get_aggs_from_alpaca(symbols,
start,
end,
granularity,
compression=1):
"""
https://alpaca.markets/docs/api-documentation/api-v2/market-data/bars/
Alpaca API as a limit of 1000 records per api call. meaning, we need to
do multiple calls to get all the required data if the date range is
large.
also, the alpaca api does not support compression (or, you can't get
5 minute bars e.g) so we need to resample the received bars.
also, we need to drop out of market records.
this function does all of that.
note:
this was the old way of getting the data
response = CLIENT.get_aggs(dataname,
compression,
granularity,
self.iso_date(start_dt),
self.iso_date(end_dt))
the thing is get_aggs work nicely for days but not for minutes, and
it is not a documented API. barset on the other hand does
but we need to manipulate it to be able to work with it
smoothly and return data the same way polygon does
"""
def _iterate_api_calls():
"""
you could get max 1000 samples from the server. if we need more
than that we need to do several api calls.
currently the alpaca api supports also 5Min and 15Min so we could
optimize server communication time by addressing timeframes
"""
got_all = False
curr = end
response: pd.DataFrame = pd.DataFrame([])
while not got_all:
if granularity == 'minute' and compression == 5:
timeframe = "5Min"
elif granularity == 'minute' and compression == 15:
timeframe = "15Min"
else:
timeframe = granularity
r = CLIENT.get_barset(symbols,
timeframe,
limit=1000,
end=curr.isoformat()
)
if r:
response = r.df if response.empty else pd.concat([r.df, response])
response.sort_index(inplace=True)
if response.index[0] <= (pytz.timezone(NY).localize(
start) if not start.tzname() else start):
got_all = True
else:
delta = timedelta(days=1) if granularity == "day" \
else timedelta(minutes=1)
curr = response.index[0] - delta
else:
# no more data is available, let's return what we have
break
return response
def _fillna(df, granularity, start, end):
if granularity != 'day':
return df
if df.empty:
return df
calendar: TradingCalendar = trading_calendars.get_calendar("NYSE")
last_val = df.iloc[0]
current = start
while current <= end:
if calendar.is_session(current):
if current.replace(tzinfo=tz.gettz(NY)) in df.index:
last_val = df.loc[current.replace(tzinfo=tz.gettz(NY))]
else:
# df.loc[pytz.timezone(NY).localize(current)] = last_val
df.loc[current.replace(tzinfo=tz.gettz(NY))] = last_val
current += timedelta(days=1)
return df
def _clear_out_of_market_hours(df):
"""
only interested in samples between 9:30, 16:00 NY time
"""
return df.between_time("09:30", "16:00")
def _drop_early_samples(df):
"""
samples from server don't start at 9:30 NY time
let's drop earliest samples
"""
for i, b in df.iterrows():
if i.time() >= dtime(9, 30):
return df[i:]
def _resample(df):
"""
samples returned with certain window size (1 day, 1 minute) user
may want to work with different window size (5min)
"""
if granularity == 'minute':
sample_size = f"{compression}Min"
else:
sample_size = f"{compression}D"
df = df.resample(sample_size).agg(
collections.OrderedDict([
('open', 'first'),
('high', 'max'),
('low', 'min'),
('close', 'last'),
('volume', 'sum'),
])
)
if granularity == 'minute':
return df.between_time("09:30", "16:00")
else:
return df
if not start:
response = CLIENT.get_barset(symbols,
granularity,
limit=1000,
end=end).df
else:
response = _iterate_api_calls()
cdl = response
if granularity == 'minute':
cdl = _clear_out_of_market_hours(cdl)
cdl = _drop_early_samples(cdl)
if compression != 1:
response = _resample(cdl)
# response = _back_to_aggs(cdl)
else:
response = cdl
if granularity == 'day':
response = response[start:end] # we only want data between dates
processed = pd.DataFrame([], columns=response.columns)
for sym in response.columns.levels[0]:
df: pd.DataFrame = response[sym]
df = df.dropna()
df = _fillna(df, granularity, start, end)
if processed.empty and not df.empty:
processed = processed.reindex(df.index.values)
if not df.empty:
processed[sym] = df
return processed
MAX_PER_REQUEST_AMOUNT = 200 # Alpaca max symbols per 1 http request
def df_generator(interval, start, end, assets_to_sids):
exchange = 'NYSE'
asset_list = list_assets()
base_sid = 0
# some symbols from alpaca are duplicated, which causes an issue with zipline
# ingest process. for now, we make sure we serve one of them (for now the first one)
already_ingested = {}
for i in range(len(asset_list[::MAX_PER_REQUEST_AMOUNT])):
partial = asset_list[MAX_PER_REQUEST_AMOUNT*i:MAX_PER_REQUEST_AMOUNT*(i+1)]
df: pd.DataFrame = get_aggs_from_alpaca(partial, start, end, 'day' if interval == '1d' else 'minute', 1)
for _, symbol in enumerate(df.columns.levels[0]):
try:
sid = assets_to_sids[symbol]
# doing this makes sure not all data in df is null
# isnull returns 0 and 1 matrix.
# doing sum twice, makes sure there isn't even one NaN value
# and since we do ffill of the data, that should not happen
# if df[symbol].isnull().sum().sum() == 0:
if not df[symbol].isnull().all().all():
if symbol not in already_ingested:
first_traded = start
auto_close_date = end + pd.Timedelta(days=1)
yield (sid, df[symbol].sort_index()), symbol, start, end, first_traded, auto_close_date, exchange
already_ingested[symbol] = True
except Exception as e:
import traceback
traceback.print_exc()
print(f"error while processig {(sid + base_sid, symbol)}: {e}")
def metadata_df():
metadata_dtype = [
('symbol', 'object'),
# ('asset_name', 'object'),
('start_date', 'datetime64[ns]'),
('end_date', 'datetime64[ns]'),
('first_traded', 'datetime64[ns]'),
('auto_close_date', 'datetime64[ns]'),
('exchange', 'object'), ]
metadata_df = pd.DataFrame(
np.empty(len(list_assets()), dtype=metadata_dtype))
return metadata_df
@bundles.register('alpaca_api', calendar_name="NYSE", minutes_per_day=390)
def api_to_bundle(interval=['1m']):
def ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir
):
assets_to_sids = asset_to_sid_map(asset_db_writer.asset_finder, list_assets())
def minute_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in df_generator(interval='1m',
start=start_session,
end=end_session,
assets_to_sids=assets_to_sids))
def daily_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in df_generator(interval='1d',
start=start_session,
end=end_session,
assets_to_sids=assets_to_sids))
for _interval in interval:
metadata = metadata_df()
if _interval == '1d':
daily_bar_writer.write(daily_data_generator(), assets=assets_to_sids.values(), show_progress=True)
elif _interval == '1m':
minute_bar_writer.write(
minute_data_generator(), assets=assets_to_sids.values(), show_progress=True)
# Drop the ticker rows which have missing sessions in their data sets
metadata.dropna(inplace=True)
asset_db_writer.write(equities=metadata)
print(metadata)
adjustment_writer.write()
return ingest
if __name__ == '__main__':
from zipline.data.bundles import register
from zipline.data import bundles as bundles_module
import trading_calendars
import os
cal: TradingCalendar = trading_calendars.get_calendar('NYSE')
end_date = pd.Timestamp('now', tz='utc').date() - timedelta(days=1)
while not cal.is_session(str(end_date)):
end_date -= timedelta(days=1)
end_date = pd.Timestamp(end_date, tz='utc')
# start_date = pd.Timestamp('2020-10-03 0:00', tz='utc')
# while not cal.is_session(start_date):
# start_date += timedelta(days=1)
start_date = end_date - timedelta(days=365)
while not cal.is_session(start_date):
start_date -= timedelta(days=1)
initialize_client()
import time
start_time = time.time()
register(
'alpaca_api',
# api_to_bundle(interval=['1d', '1m']),
# api_to_bundle(interval=['1m']),
api_to_bundle(interval=['1d']),
calendar_name='NYSE',
start_session=start_date,
end_session=end_date
)
assets_version = ((),)[0] # just a weird way to create an empty tuple
bundles_module.ingest(
"alpaca_api",
os.environ,
assets_versions=assets_version,
show_progress=True,
)
print(f"--- It took {timedelta(seconds=time.time() - start_time)} ---") | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/alpaca_api.py | alpaca_api.py |
import os
import sys
from logbook import Logger, StreamHandler
from numpy import empty
from pandas import DataFrame, read_csv, Index, Timedelta, NaT
from trading_calendars import register_calendar_alias
from zipline.utils.cli import maybe_show_progress
from zipline.data.bundles import core as bundles
from zipline.data.bundles.common import asset_to_sid_map
handler = StreamHandler(sys.stdout, format_string=" | {record.message}")
logger = Logger(__name__)
logger.handlers.append(handler)
def csvdir_equities(tframes=None, csvdir=None):
"""
Generate an ingest function for custom data bundle
This function can be used in ~/.zipline/extension.py
to register bundle with custom parameters, e.g. with
a custom trading calendar.
Parameters
----------
tframes: tuple, optional
The data time frames, supported timeframes: 'daily' and 'minute'
csvdir : string, optional, default: CSVDIR environment variable
The path to the directory of this structure:
<directory>/<timeframe1>/<symbol1>.csv
<directory>/<timeframe1>/<symbol2>.csv
<directory>/<timeframe1>/<symbol3>.csv
<directory>/<timeframe2>/<symbol1>.csv
<directory>/<timeframe2>/<symbol2>.csv
<directory>/<timeframe2>/<symbol3>.csv
Returns
-------
ingest : callable
The bundle ingest function
Examples
--------
This code should be added to ~/.zipline/extension.py
.. code-block:: python
from zipline.data.bundles import csvdir_equities, register
register('custom-csvdir-bundle',
csvdir_equities(["daily", "minute"],
'/full/path/to/the/csvdir/directory'))
"""
return CSVDIRBundle(tframes, csvdir).ingest
class CSVDIRBundle:
"""
Wrapper class to call csvdir_bundle with provided
list of time frames and a path to the csvdir directory
"""
def __init__(self, tframes=None, csvdir=None):
self.tframes = tframes
self.csvdir = csvdir
def ingest(self,
environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir):
csvdir_bundle(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir,
self.tframes,
self.csvdir)
@bundles.register("csvdir")
def csvdir_bundle(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir,
tframes=None,
csvdir=None):
"""
Build a zipline data bundle from the directory with csv files.
"""
if not csvdir:
csvdir = environ.get('CSVDIR')
if not csvdir:
raise ValueError("CSVDIR environment variable is not set")
if not os.path.isdir(csvdir):
raise ValueError("%s is not a directory" % csvdir)
if not tframes:
tframes = set(["daily", "minute"]).intersection(os.listdir(csvdir))
if not tframes:
raise ValueError("'daily' and 'minute' directories "
"not found in '%s'" % csvdir)
divs_splits = {'divs': DataFrame(columns=['sid', 'amount',
'ex_date', 'record_date',
'declared_date', 'pay_date']),
'splits': DataFrame(columns=['sid', 'ratio',
'effective_date'])}
for tframe in tframes:
ddir = os.path.join(csvdir, tframe)
symbols = sorted(item.split('.csv')[0]
for item in os.listdir(ddir)
if '.csv' in item)
if not symbols:
raise ValueError("no <symbol>.csv* files found in %s" % ddir)
dtype = [('start_date', 'datetime64[ns]'),
('end_date', 'datetime64[ns]'),
('auto_close_date', 'datetime64[ns]'),
('symbol', 'object')]
metadata = DataFrame(empty(len(symbols), dtype=dtype))
if tframe == 'minute':
writer = minute_bar_writer
else:
writer = daily_bar_writer
assets_to_sids = asset_to_sid_map(asset_db_writer.asset_finder, symbols)
writer.write(_pricing_iter(ddir, symbols, metadata,
divs_splits, show_progress, assets_to_sids = assets_to_sids),
show_progress=show_progress)
# Hardcode the exchange to "CSVDIR" for all assets and (elsewhere)
# register "CSVDIR" to resolve to the NYSE calendar, because these
# are all equities and thus can use the NYSE calendar.
metadata['exchange'] = "CSVDIR"
asset_db_writer.write(equities=metadata)
divs_splits['divs']['sid'] = divs_splits['divs']['sid'].astype(int)
divs_splits['splits']['sid'] = divs_splits['splits']['sid'].astype(int)
adjustment_writer.write(splits=divs_splits['splits'],
dividends=divs_splits['divs'])
def _pricing_iter(csvdir, symbols, metadata, divs_splits, show_progress, assets_to_sids={}):
with maybe_show_progress(symbols, show_progress,
label='Loading custom pricing data: ') as it:
files = os.listdir(csvdir)
for symbol in it:
sid = assets_to_sids[symbol]
logger.debug('%s: sid %s' % (symbol, sid))
try:
fname = [fname for fname in files
if '%s.csv' % symbol in fname][0]
except IndexError:
raise ValueError("%s.csv file is not in %s" % (symbol, csvdir))
dfr = read_csv(os.path.join(csvdir, fname),
parse_dates=[0],
infer_datetime_format=True,
index_col=0).sort_index()
start_date = dfr.index[0]
end_date = dfr.index[-1]
#print(dfr)
#exit()
# The auto_close date is the day after the last trade.
ac_date = end_date + Timedelta(days=1)
metadata.loc[sid] = start_date, end_date, ac_date, symbol
if 'split' in dfr.columns:
tmp = 1. / dfr[dfr['split'] != 1.0]['split']
split = DataFrame(data=tmp.index.tolist(),
columns=['effective_date'])
split['ratio'] = tmp.tolist()
split['sid'] = sid
splits = divs_splits['splits']
index = Index(range(splits.shape[0],
splits.shape[0] + split.shape[0]))
split.set_index(index, inplace=True)
divs_splits['splits'] = splits.append(split)
if 'dividend' in dfr.columns:
# ex_date amount sid record_date declared_date pay_date
tmp = dfr[dfr['dividend'] != 0.0]['dividend']
div = DataFrame(data=tmp.index.tolist(), columns=['ex_date'])
div['record_date'] = NaT
div['declared_date'] = NaT
div['pay_date'] = NaT
div['amount'] = tmp.tolist()
div['sid'] = sid
divs = divs_splits['divs']
ind = Index(range(divs.shape[0], divs.shape[0] + div.shape[0]))
div.set_index(ind, inplace=True)
divs_splits['divs'] = divs.append(div)
yield sid, dfr
register_calendar_alias("CSVDIR", "NYSE") | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/csvdir.py | csvdir.py |
from collections import namedtuple
import errno
import os
import shutil
import warnings
import click
from logbook import Logger
import pandas as pd
from trading_calendars import get_calendar
from toolz import curry, complement, take
from ..adjustments import SQLiteAdjustmentReader, SQLiteAdjustmentWriter
from ..bcolz_daily_bars import BcolzDailyBarReader, BcolzDailyBarWriter
from ..minute_bars import (
BcolzMinuteBarReader,
BcolzMinuteBarWriter,
)
from ..psql_daily_bars import PSQLDailyBarReader, PSQLDailyBarWriter
from zipline.assets import (
AssetDBWriter,
AssetFinder,
ASSET_DB_VERSION,
)
from zipline.assets.asset_db_migrations import downgrade
from zipline.utils.cache import (
dataframe_cache,
working_dir,
working_file,
)
from zipline.utils.compat import ExitStack, mappingproxy
from zipline.utils.input_validation import ensure_timestamp, optionally
import zipline.utils.paths as pth
from zipline.utils.preprocess import preprocess
from sqlalchemy.exc import InvalidRequestError
log = Logger(__name__)
def asset_db_path(bundle_name, timestr, environ=None, db_version=None):
return pth.data_path(
asset_db_relative(bundle_name, timestr, db_version),
environ=environ,
)
def minute_equity_path(bundle_name, timestr, environ=None):
return pth.data_path(
minute_equity_relative(bundle_name, timestr),
environ=environ,
)
def daily_equity_path(bundle_name, timestr, environ=None):
return pth.data_path(
daily_equity_relative(bundle_name, timestr),
environ=environ,
)
def adjustment_db_path(bundle_name, timestr, environ=None):
return pth.data_path(
adjustment_db_relative(bundle_name, timestr),
environ=environ,
)
def cache_path(bundle_name, environ=None):
return pth.data_path(
cache_relative(bundle_name),
environ=environ,
)
def adjustment_db_relative(bundle_name, timestr):
return bundle_name, timestr, 'adjustments.sqlite'
def cache_relative(bundle_name):
return bundle_name, '.cache'
def daily_equity_relative(bundle_name, timestr):
return bundle_name, timestr, 'daily_equities.bcolz'
def minute_equity_relative(bundle_name, timestr):
return bundle_name, timestr, 'minute_equities.bcolz'
def asset_db_relative(bundle_name, timestr, db_version=None):
db_version = ASSET_DB_VERSION if db_version is None else db_version
return bundle_name, timestr, 'assets-%d.sqlite' % db_version
def external_db_path(bundle_name, environ):
import zipline.config.data_backend
path = None
if zipline.config.data_backend.db_backend_configured():
if zipline.config.data_backend.db_backend_configured() == 'postgres':
db = zipline.config.data_backend.PostgresDB()
host = db.host
port = db.port
user = db.user
password = db.password
user_pwd_str = f'{user}:{password}@' if user != '' else ''
host_port_str = f'{host}:{port}' if port != '' else f'{host}'
# we assume bundle-name as database-name
path = f'postgresql://{user_pwd_str}{host_port_str}/{bundle_name}'
else:
backend = environ['ZIPLINE_DATA_BACKEND']
raise Exception(f'Backend {backend} currently not supported')
return path
def to_bundle_ingest_dirname(ts):
"""Convert a pandas Timestamp into the name of the directory for the
ingestion.
Parameters
----------
ts : pandas.Timestamp
The time of the ingestions
Returns
-------
name : str
The name of the directory for this ingestion.
"""
return ts.isoformat().replace(':', ';')
def from_bundle_ingest_dirname(cs):
"""Read a bundle ingestion directory name into a pandas Timestamp.
Parameters
----------
cs : str
The name of the directory.
Returns
-------
ts : pandas.Timestamp
The time when this ingestion happened.
"""
return pd.Timestamp(cs.replace(';', ':'))
def ingestions_for_bundle(bundle, environ=None):
print(os.listdir(pth.data_path([bundle], environ)))
return sorted(
(from_bundle_ingest_dirname(ing)
for ing in os.listdir(pth.data_path([bundle], environ))
if not pth.hidden(ing)),
reverse=True,
)
RegisteredBundle = namedtuple(
'RegisteredBundle',
['calendar_name',
'start_session',
'end_session',
'minutes_per_day',
'ingest',
'create_writers']
)
BundleData = namedtuple(
'BundleData',
'asset_finder equity_minute_bar_reader equity_daily_bar_reader '
'adjustment_reader',
)
BundleCore = namedtuple(
'BundleCore',
'bundles register unregister ingest load clean',
)
class UnknownBundle(click.ClickException, LookupError):
"""Raised if no bundle with the given name was registered.
"""
exit_code = 1
def __init__(self, name):
super(UnknownBundle, self).__init__(
'No bundle registered with the name %r' % name,
)
self.name = name
def __str__(self):
return self.message
class BadClean(click.ClickException, ValueError):
"""Exception indicating that an invalid argument set was passed to
``clean``.
Parameters
----------
before, after, keep_last : any
The bad arguments to ``clean``.
See Also
--------
clean
"""
def __init__(self, before, after, keep_last):
super(BadClean, self).__init__(
'Cannot pass a combination of `before` and `after` with '
'`keep_last`. Must pass one. '
'Got: before=%r, after=%r, keep_last=%r\n' % (
before,
after,
keep_last,
),
)
def __str__(self):
return self.message
def _make_bundle_core():
"""Create a family of data bundle functions that read from the same
bundle mapping.
Returns
-------
bundles : mappingproxy
The mapping of bundles to bundle payloads.
register : callable
The function which registers new bundles in the ``bundles`` mapping.
unregister : callable
The function which deregisters bundles from the ``bundles`` mapping.
ingest : callable
The function which downloads and write data for a given data bundle.
load : callable
The function which loads the ingested bundles back into memory.
clean : callable
The function which cleans up data written with ``ingest``.
"""
_bundles = {} # the registered bundles
# Expose _bundles through a proxy so that users cannot mutate this
# accidentally. Users may go through `register` to update this which will
# warn when trampling another bundle.
bundles = mappingproxy(_bundles)
@curry
def register(name,
f,
calendar_name='NYSE',
start_session=None,
end_session=None,
minutes_per_day=390,
create_writers=True):
"""Register a data bundle ingest function.
Parameters
----------
name : str
The name of the bundle.
f : callable
The ingest function. This function will be passed:
environ : mapping
The environment this is being run with.
asset_db_writer : AssetDBWriter
The asset db writer to write into.
minute_bar_writer : BcolzMinuteBarWriter
The minute bar writer to write into.
daily_bar_writer : BcolzDailyBarWriter
The daily bar writer to write into.
adjustment_writer : SQLiteAdjustmentWriter
The adjustment db writer to write into.
calendar : trading_calendars.TradingCalendar
The trading calendar to ingest for.
start_session : pd.Timestamp
The first session of data to ingest.
end_session : pd.Timestamp
The last session of data to ingest.
cache : DataFrameCache
A mapping object to temporarily store dataframes.
This should be used to cache intermediates in case the load
fails. This will be automatically cleaned up after a
successful load.
show_progress : bool
Show the progress for the current load where possible.
calendar_name : str, optional
The name of a calendar used to align bundle data.
Default is 'NYSE'.
start_session : pd.Timestamp, optional
The first session for which we want data. If not provided,
or if the date lies outside the range supported by the
calendar, the first_session of the calendar is used.
end_session : pd.Timestamp, optional
The last session for which we want data. If not provided,
or if the date lies outside the range supported by the
calendar, the last_session of the calendar is used.
minutes_per_day : int, optional
The number of minutes in each normal trading day.
create_writers : bool, optional
Should the ingest machinery create the writers for the ingest
function. This can be disabled as an optimization for cases where
they are not needed, like the ``quantopian-quandl`` bundle.
Notes
-----
This function my be used as a decorator, for example:
.. code-block:: python
@register('quandl')
def quandl_ingest_function(...):
...
See Also
--------
zipline.data.bundles.bundles
"""
if name in bundles:
warnings.warn(
'Overwriting bundle with name %r' % name,
stacklevel=3,
)
# NOTE: We don't eagerly compute calendar values here because
# `register` is called at module scope in zipline, and creating a
# calendar currently takes between 0.5 and 1 seconds, which causes a
# noticeable delay on the zipline CLI.
_bundles[name] = RegisteredBundle(
calendar_name=calendar_name,
start_session=start_session,
end_session=end_session,
minutes_per_day=minutes_per_day,
ingest=f,
create_writers=create_writers,
)
return f
def unregister(name):
"""Unregister a bundle.
Parameters
----------
name : str
The name of the bundle to unregister.
Raises
------
UnknownBundle
Raised when no bundle has been registered with the given name.
See Also
--------
zipline.data.bundles.bundles
"""
try:
del _bundles[name]
except KeyError:
raise UnknownBundle(name)
def ingest(name,
environ=os.environ,
timestamp=None,
assets_versions=(),
show_progress=False):
"""Ingest data for a given bundle.
Parameters
----------
name : str
The name of the bundle.
environ : mapping, optional
The environment variables. By default this is os.environ.
timestamp : datetime, optional
The timestamp to use for the load.
By default this is the current time.
assets_versions : Iterable[int], optional
Versions of the assets db to which to downgrade.
show_progress : bool, optional
Tell the ingest function to display the progress where possible.
"""
try:
bundle = bundles[name]
except KeyError:
raise UnknownBundle(name)
calendar = get_calendar(bundle.calendar_name)
start_session = bundle.start_session
end_session = bundle.end_session
if start_session is None or start_session < calendar.first_session:
start_session = calendar.first_session
if end_session is None or end_session > calendar.last_session:
end_session = calendar.last_session
if timestamp is None:
timestamp = pd.Timestamp.utcnow()
timestamp = timestamp.tz_convert('utc').tz_localize(None)
timestr = to_bundle_ingest_dirname(timestamp)
cachepath = cache_path(name, environ=environ)
pth.ensure_directory(cachepath)
# depending on the environment we might want to get a path to
# an external postgres-db instead of one to a local sqlite-db
# also, we need an asset-finder in case we have an external db
# to make it possible to get ids for asset-symbols
db_path_external = external_db_path(name, environ)
# needs to be checkout outside of 'with' in case create_writers is false
# only 'sqlite-bcolz'-backend needs to ensure local folders
if not db_path_external:
pth.ensure_directory(pth.data_path([name, timestr], environ=environ))
with dataframe_cache(cachepath, clean_on_failure=False) as cache, \
ExitStack() as stack:
# we use `cleanup_on_failure=False` so that we don't purge the
# cache directory if the load fails in the middle
if bundle.create_writers:
wd = stack.enter_context(working_dir(
pth.data_path([], environ=environ))
)
asset_finder = None
if db_path_external:
assets_db_path = adjustments_db_path = daily_bar_writer = db_path_external
daily_bar_writer = PSQLDailyBarWriter(
db_path_external,
calendar,
start_session,
end_session,
)
daily_bar_reader = PSQLDailyBarReader(db_path_external)
minute_bar_writer = None
try:
asset_finder = AssetFinder(db_path_external)
except InvalidRequestError:
asset_finder = None
else:
pth.ensure_directory(pth.data_path([name, timestr], environ=environ))
assets_db_path = wd.getpath(*asset_db_relative(name, timestr))
adjustments_db_path = adjustment_db_path(name, timestr)
adjustments_db_path = wd.getpath(*adjustment_db_relative(name, timestr))
daily_bars_path = wd.ensure_dir(
*daily_equity_relative(name, timestr)
)
daily_bar_writer = BcolzDailyBarWriter(
daily_bars_path,
calendar,
start_session,
end_session,
)
daily_bar_reader = BcolzDailyBarReader(daily_bars_path)
minute_bar_writer = BcolzMinuteBarWriter(
wd.ensure_dir(*minute_equity_relative(name, timestr)),
calendar,
start_session,
end_session,
minutes_per_day=bundle.minutes_per_day,
)
# Do an empty write to ensure that the daily ctables exist
# when we create the SQLiteAdjustmentWriter below. The
# SQLiteAdjustmentWriter needs to open the daily ctables so
# that it can compute the adjustment ratios for the dividends.
daily_bar_writer.write(())
asset_db_writer = AssetDBWriter(assets_db_path, asset_finder)
adjustment_db_writer = stack.enter_context(
SQLiteAdjustmentWriter(
adjustments_db_path,
daily_bar_reader,
overwrite=True,
)
)
else:
daily_bar_writer = None
minute_bar_writer = None
asset_db_writer = None
adjustment_db_writer = None
if assets_versions:
raise ValueError('Need to ingest a bundle that creates '
'writers in order to downgrade the assets'
' db.')
log.info("Ingesting {}.", name)
bundle.ingest(
environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_db_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
pth.data_path([name, timestr], environ=environ),
)
for version in sorted(set(assets_versions), reverse=True):
version_path = wd.getpath(*asset_db_relative(
name, timestr, db_version=version,
))
with working_file(version_path) as wf:
shutil.copy2(assets_db_path, wf.path)
downgrade(wf.path, version)
def most_recent_data(bundle_name, timestamp, environ=None):
"""Get the path to the most recent data after ``date``for the
given bundle.
Parameters
----------
bundle_name : str
The name of the bundle to lookup.
timestamp : datetime
The timestamp to begin searching on or before.
environ : dict, optional
An environment dict to forward to zipline_root.
"""
if bundle_name not in bundles:
raise UnknownBundle(bundle_name)
try:
candidates = os.listdir(
pth.data_path([bundle_name], environ=environ),
)
return pth.data_path(
[bundle_name,
max(
filter(complement(pth.hidden), candidates),
key=from_bundle_ingest_dirname,
)],
environ=environ,
)
except (ValueError, OSError) as e:
if getattr(e, 'errno', errno.ENOENT) != errno.ENOENT:
raise
raise ValueError(
'no data for bundle {bundle!r} on or before {timestamp}\n'
'maybe you need to run: $ zipline ingest -b {bundle}'.format(
bundle=bundle_name,
timestamp=timestamp,
),
)
def load(name, environ=os.environ, timestamp=None):
"""Loads a previously ingested bundle.
Parameters
----------
name : str
The name of the bundle.
environ : mapping, optional
The environment variables. Defaults of os.environ.
timestamp : datetime, optional
The timestamp of the data to lookup.
Defaults to the current time.
Returns
-------
bundle_data : BundleData
The raw data readers for this bundle.
"""
if timestamp is None:
timestamp = pd.Timestamp.utcnow()
timestr = most_recent_data(name, timestamp, environ=environ)
db_path_external = external_db_path(name, environ)
if db_path_external:
assets_db_path = db_path_external
adjustments_db_path = db_path_external
# assets_db_path = asset_db_path(name, timestr, environ=environ)
# adjustments_db_path = adjustment_db_path(name, timestr, environ=environ)
daily_bar_reader = PSQLDailyBarReader(db_path_external)
minute_bar_reader = None
else:
assets_db_path = asset_db_path(name, timestr, environ=environ)
adjustments_db_path = adjustment_db_path(name, timestr, environ=environ)
daily_bar_reader = BcolzDailyBarReader(daily_equity_path(name, timestr, environ=environ))
minute_bar_reader = BcolzMinuteBarReader(minute_equity_path(name, timestr, environ=environ))
return BundleData(
asset_finder=AssetFinder(
assets_db_path
),
equity_minute_bar_reader=minute_bar_reader,
equity_daily_bar_reader=daily_bar_reader,
adjustment_reader=SQLiteAdjustmentReader(
adjustments_db_path
),
)
@preprocess(
before=optionally(ensure_timestamp),
after=optionally(ensure_timestamp),
)
def clean(name,
before=None,
after=None,
keep_last=None,
environ=os.environ):
"""Clean up data that was created with ``ingest`` or
``$ python -m zipline ingest``
Parameters
----------
name : str
The name of the bundle to remove data for.
before : datetime, optional
Remove data ingested before this date.
This argument is mutually exclusive with: keep_last
after : datetime, optional
Remove data ingested after this date.
This argument is mutually exclusive with: keep_last
keep_last : int, optional
Remove all but the last ``keep_last`` ingestions.
This argument is mutually exclusive with:
before
after
environ : mapping, optional
The environment variables. Defaults of os.environ.
Returns
-------
cleaned : set[str]
The names of the runs that were removed.
Raises
------
BadClean
Raised when ``before`` and or ``after`` are passed with
``keep_last``. This is a subclass of ``ValueError``.
"""
try:
all_runs = sorted(
filter(
complement(pth.hidden),
os.listdir(pth.data_path([name], environ=environ)),
),
key=from_bundle_ingest_dirname,
)
except OSError as e:
if e.errno != errno.ENOENT:
raise
raise UnknownBundle(name)
if before is after is keep_last is None:
raise BadClean(before, after, keep_last)
if ((before is not None or after is not None) and
keep_last is not None):
raise BadClean(before, after, keep_last)
if keep_last is None:
def should_clean(name):
dt = from_bundle_ingest_dirname(name)
return (
(before is not None and dt < before) or
(after is not None and dt > after)
)
elif keep_last >= 0:
last_n_dts = set(take(keep_last, reversed(all_runs)))
def should_clean(name):
return name not in last_n_dts
else:
raise BadClean(before, after, keep_last)
cleaned = set()
for run in all_runs:
if should_clean(run):
log.info("Cleaning {}.", run)
path = pth.data_path([name, run], environ=environ)
shutil.rmtree(path)
cleaned.add(path)
return cleaned
return BundleCore(bundles, register, unregister, ingest, load, clean)
bundles, register, unregister, ingest, load, clean = _make_bundle_core() | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/core.py | core.py |
import numpy as np
import pandas as pd
from alpha_vantage.timeseries import TimeSeries
from datetime import date, timedelta
from trading_calendars import TradingCalendar
from ratelimit import limits, sleep_and_retry
import zipline.config
from zipline.data.bundles import core as bundles
from zipline.data.bundles.common import asset_to_sid_map
from zipline.data.bundles.universe import Universe, get_sp500, get_sp100, get_nasdaq100, all_alpaca_assets
from zipline.data import bundles as bundles_module
import trading_calendars
import os
import time
av_config = zipline.config.bundle.AlphaVantage()
AV_FREQ_SEC = av_config.sample_frequency
AV_CALLS_PER_FREQ = av_config.max_calls_per_freq
AV_TOLERANCE_SEC = av_config.breathing_space
os.environ["ALPHAVANTAGE_API_KEY"] = av_config.api_key # make sure it's set in env variable
UNIVERSE = Universe.NASDAQ100
ASSETS = None
def list_assets():
global ASSETS
if not ASSETS:
custom_asset_list = av_config.av.get("custom_asset_list")
if custom_asset_list:
custom_asset_list = custom_asset_list.strip().replace(" ", "").split(",")
ASSETS = list(set(custom_asset_list))
else:
try:
universe = Universe[av_config.av["universe"]]
except:
universe = Universe.ALL
if universe == Universe.ALL:
# alpha vantage doesn't define a universe. we could try using alpaca's universe if the
# user defined credentials. if not, we will raise an exception.
try:
import zipline.data.bundles.alpaca_api as alpaca
alpaca.initialize_client()
ASSETS = all_alpaca_assets(alpaca.CLIENT)
except:
raise Exception("You tried to use Universe.ALL but you didn't define the alpaca credentials.")
elif universe == Universe.SP100:
ASSETS = get_sp100()
elif universe == Universe.SP500:
ASSETS = get_sp500()
elif universe == Universe.NASDAQ100:
ASSETS = get_nasdaq100()
ASSETS = list(set(ASSETS))
return ASSETS
def fill_daily_gaps(df):
"""
filling missing data. logic:
1. get start date and end date from df. (caveat: if the missing dates are at the edges this will not work)
2. use trading calendars to get all session dates between start and end
3. use difference() to get only missing dates.
4. add those dates to the original df with NaN
5. dividends get 0 and split gets 1 (meaning no split happened)
6. all the rest get ffill of the close value.
7. volume get 0
:param df:
:return:
"""
cal: TradingCalendar = trading_calendars.get_calendar('NYSE')
sessions = cal.sessions_in_range(df.index[0], df.index[-1])
if len(df.index) == len(sessions):
return df
to_fill = sessions.difference(df.index)
df = df.append(pd.DataFrame(index=to_fill)).sort_index()
# forward-fill these values regularly
df.close.fillna(method='ffill', inplace=True)
df.dividend.fillna(0, inplace=True)
df.split.fillna(1, inplace=True)
df.volume.fillna(0, inplace=True)
df.open.fillna(df.close, inplace=True)
df.high.fillna(df.close, inplace=True)
df.low.fillna(df.close, inplace=True)
df.adj_close.fillna(df.close, inplace=True)
filled = len(to_fill)
print(f'\nWarning! Filled {filled} empty values!')
return df
# purpose of this function is to encapsulate both minute- and daily-requests in one
# function to be able to properly do rate-limiting.
@sleep_and_retry
@limits(calls=AV_CALLS_PER_FREQ, period=AV_FREQ_SEC + AV_TOLERANCE_SEC)
def av_api_wrapper(symbol, interval, _slice=None):
if interval == '1m':
ts = TimeSeries(output_format='csv')
data_slice, meta_data = ts.get_intraday_extended(symbol, interval='1min', slice=_slice, adjusted='false')
return data_slice
else:
ts = TimeSeries()
data, meta_data = ts.get_daily_adjusted(symbol, outputsize='full')
return data
def av_get_data_for_symbol(symbol, start, end, interval):
if interval == '1m':
data = []
for i in range(1, 3):
for j in range(1, 13):
_slice = 'year' + str(i) + 'month' + str(j)
# print('requesting slice ' + _slice + ' for ' + symbol)
data_slice = av_api_wrapper(symbol, interval=interval, slice=_slice)
# dont know better way to convert _csv.reader to list or DataFrame
table = []
for line in data_slice:
table.append(line)
# strip header-row from csv
table = table[1:]
data = data + table
df = pd.DataFrame(data, columns=['date', 'open', 'high', 'low', 'close', 'volume'])
df.index = pd.to_datetime(df['date'])
df.index = df.index.tz_localize('UTC')
df.drop(columns=['date'], inplace=True)
else:
data = av_api_wrapper(symbol, interval)
df = pd.DataFrame.from_dict(data, orient='index')
df.index = pd.to_datetime(df.index).tz_localize('UTC')
df.rename(columns={
'1. open': 'open',
'2. high': 'high',
'3. low': 'low',
'4. close': 'close',
'5. volume': 'volume',
'5. adjusted close': 'adj_close',
'6. volume': 'volume',
'7. dividend amount': 'dividend',
'8. split coefficient': 'split'
}, inplace=True)
# fill potential gaps in data
df = fill_daily_gaps(df)
df.sort_index(inplace=True)
# data comes as strings
df['open'] = pd.to_numeric(df['open'], downcast='float')
df['high'] = pd.to_numeric(df['high'], downcast='float')
df['low'] = pd.to_numeric(df['low'], downcast='float')
df['close'] = pd.to_numeric(df['close'], downcast='float')
df['volume'] = pd.to_numeric(df['volume'], downcast='unsigned')
if 'adj_close' in df.columns:
df['adj_close'] = pd.to_numeric(df['adj_close'], downcast='float')
if 'dividend' in df.columns:
df['dividend'] = pd.to_numeric(df['dividend'], downcast='float')
if 'split' in df.columns:
df['split'] = pd.to_numeric(df['split'], downcast='float')
return df
# collect all days where there were splits and calculate split-ratio
# by 1 / split-factor. save them together with effective-date.
def calc_split(sid, df):
tmp = 1. / df[df['split'] != 1.0]['split']
split = pd.DataFrame(data=tmp.index.tolist(),
columns=['effective_date'])
split['ratio'] = tmp.tolist()
split['sid'] = np.int(sid)
# split['effective_date'] = pd.to_datetime(split['effective_date'], utc=True)
split['effective_date'] = split['effective_date'].apply(lambda x: x.timestamp())
return split
# collect all dividends and the dates when they were issued,
# fill stuff we don't know with empty-values
def calc_dividend(sid, df, sessions):
tmp = df[df['dividend'] != 0.0]['dividend']
div = pd.DataFrame(data=tmp.index.tolist(), columns=['ex_date'])
# as we do not know these values, set something as done in csvdir
# there it writes nats but in case of writing to postgres,
# pd.NaT will exceed BigInt for some reason
natValue = pd.to_datetime('1800-1-1')
div['record_date'] = natValue
div['declared_date'] = natValue
# "guess" a dividend-pay-date 10 trading-days in the future
div['pay_date'] = [sessions[sessions.get_loc(ex_date) + 10] for ex_date in div['ex_date']]
div['amount'] = tmp.tolist()
div['sid'] = np.int(sid)
# convert to string and then back to datetime, otherwise pd.concat will fail
div['ex_date'] = div['ex_date'].apply(lambda x: x.strftime('%Y-%m-%d 00:00:00'))
div['pay_date'] = div['pay_date'].apply(lambda x: x.strftime('%Y-%m-%d 00:00:00'))
return div
def df_generator(interval, start, end, divs_splits, assets_to_sids={}):
exchange = 'NYSE'
# get calendar and extend it to 20 days to the future to be able
# to set dividend-pay-date to a valid session
cal: TradingCalendar = trading_calendars.get_calendar('NYSE')
sessions = cal.sessions_in_range(start, end + timedelta(days=20))
asset_list = list_assets()
for symbol in asset_list:
try:
df = av_get_data_for_symbol(symbol, start, end, interval)
sid = assets_to_sids[symbol]
first_traded = df.index[0]
auto_close_date = df.index[-1] + pd.Timedelta(days=1)
if 'split' in df.columns:
split = calc_split(sid, df)
divs_splits['splits'] = divs_splits['splits'].append(split)
if 'dividend' in df.columns:
div = calc_dividend(sid, df, sessions)
divs_splits['divs'] = pd.concat([divs_splits['divs'], div])
yield (sid, df), symbol, symbol, start, end, first_traded, auto_close_date, exchange
except KeyboardInterrupt:
exit()
except Exception as e:
# somehow rate-limiting does not work with exceptions, throttle manually
if 'Thank you for using Alpha Vantage! Our standard API call frequency is' in str(e):
print(f'\nGot rate-limit on remote-side, retrying symbol {symbol} later')
asset_list.append(symbol)
else:
print(f'\nException for symbol {symbol}')
print(e)
def metadata_df(assets_to_sids={}):
metadata = []
sids = [sid for _, sid in assets_to_sids.items()]
metadata_dtype = [
('symbol', 'object'),
('asset_name', 'object'),
('start_date', 'datetime64[ns]'),
('end_date', 'datetime64[ns]'),
('first_traded', 'datetime64[ns]'),
('auto_close_date', 'datetime64[ns]'),
('exchange', 'object'), ]
metadata_df = pd.DataFrame(
np.empty(len(list_assets()), dtype=metadata_dtype))
metadata_df.index = sids
return metadata_df
@bundles.register('alpha_vantage', calendar_name="NYSE", minutes_per_day=390)
def api_to_bundle(interval=['1m']):
def ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir
):
divs_splits = {'divs': pd.DataFrame(columns=['sid', 'amount',
'ex_date', 'record_date',
'declared_date', 'pay_date']),
'splits': pd.DataFrame(columns=['sid', 'ratio',
'effective_date'])}
assets_to_sids = asset_to_sid_map(asset_db_writer.asset_finder, list_assets())
def minute_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in
df_generator(
interval='1m',
start=start_session,
end=end_session,
assets_to_sids=assets_to_sids,
divs_splits=divs_splits))
def daily_data_generator():
return (sid_df for (sid_df, *metadata.loc[sid_df[0]])
in df_generator(
interval='1d',
start=start_session,
end=end_session,
assets_to_sids=assets_to_sids,
divs_splits=divs_splits))
metadata = metadata_df(assets_to_sids)
assets = list_assets()
for _interval in interval:
if _interval == '1d':
daily_bar_writer.write(daily_data_generator(), assets=assets_to_sids.values(), show_progress=True,
invalid_data_behavior='raise')
elif _interval == '1m':
minute_bar_writer.write(minute_data_generator(), show_progress=True)
metadata.dropna(inplace=True)
asset_db_writer.write(equities=metadata)
# convert back wrong datatypes after pd.concat
divs_splits['splits']['sid'] = divs_splits['splits']['sid'].astype(np.int)
divs_splits['divs']['sid'] = divs_splits['divs']['sid'].astype(np.int)
divs_splits['divs']['ex_date'] = pd.to_datetime(divs_splits['divs']['ex_date'], utc=True)
divs_splits['divs']['pay_date'] = pd.to_datetime(divs_splits['divs']['pay_date'], utc=True)
adjustment_writer.write(splits=divs_splits['splits'], dividends=divs_splits['divs'])
# Drop the ticker rows which have missing sessions in their data sets
print(metadata)
return ingest
if __name__ == '__main__':
from zipline.data.bundles import register
cal: TradingCalendar = trading_calendars.get_calendar('NYSE')
# alpha-vantage has a fixed time-window, no point in changing these
start_date = pd.Timestamp('1999-11-1', tz='utc')
end_date = pd.Timestamp(date.today() - timedelta(days=1), tz='utc')
while not cal.is_session(end_date):
end_date -= timedelta(days=1)
print('ingesting alpha_vantage-data from: ' + str(start_date) + ' to: ' + str(end_date))
start_time = time.time()
register(
'alpha_vantage',
# api_to_bundle(interval=['1d', '1m']),
# api_to_bundle(interval=['1m']),
api_to_bundle(interval=['1d']),
calendar_name='NYSE',
start_session=start_date,
end_session=end_date
)
assets_version = ((),)[0] # just a weird way to create an empty tuple
bundles_module.ingest(
"alpha_vantage",
os.environ,
assets_versions=assets_version,
show_progress=True,
)
print("--- %s seconds ---" % (time.time() - start_time)) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/alpha_vantage_api.py | alpha_vantage_api.py |
import alpaca_trade_api as tradeapi
from datetime import timedelta
import numpy as np
from os.path import isfile, join
from pathlib import Path
import pandas as pd
import pickle
from alpaca_trade_api.common import URL
from dateutil import tz
from trading_calendars import TradingCalendar
import yaml
from zipline.data.bundles import core as bundles
from dateutil.parser import parse as date_parse
user_home = str(Path.home())
custom_data_path = join(user_home, '.zipline/custom_data')
CLIENT: tradeapi.REST = None
NY = "America/New_York"
def initialize_client():
global CLIENT
with open("polygon.yaml", mode='r') as f:
o = yaml.safe_load(f)
key = o["key_id"]
secret = o["secret"]
base_url = o["base_url"]
CLIENT = tradeapi.REST(key_id=key,
secret_key=secret,
base_url=URL(base_url))
ASSETS = None
def list_assets():
global ASSETS
if not ASSETS:
ASSETS = [_.symbol for _ in CLIENT.list_assets()]
# ASSETS = [_.ticker for _ in CLIENT.polygon.all_tickers()]
return ASSETS[:20]
def tickers_generator():
"""
Return a tuple (sid, ticker_pair)
"""
tickers_file = join(custom_data_path, 'alpaca_ticker_pairs.pickle')
if not isfile(tickers_file):
ticker_pairs = list_assets()
else:
with open(tickers_file, 'rb') as f:
ticker_pairs = pickle.load(f)[:]
return (tuple((sid, ticker)) for sid, ticker in enumerate(ticker_pairs))
def iso_date(date_str):
"""
this method will make sure that dates are formatted properly
as with isoformat
:param date_str:
:return: YYYY-MM-DD date formatted
"""
return date_parse(date_str).date().isoformat()
def get_aggs_from_polygon(dataname,
dtbegin,
dtend,
granularity,
compression):
"""
so polygon has a much more convenient api for this than alpaca because
we could insert the compression in to the api call and we don't need to
resample it. but, at this point in time, something is not working
properly and data is returned in segments. meaning, we have patches of
missing data. e.g we request data from 2020-03-01 to 2020-07-01 and we
get something like this: 2020-03-01:2020-03-15, 2020-06-25:2020-07-01
so that makes life difficult.. there's no way to know which patch will
be returned and which one we should try to get again.
so the solution must be, ask data in segments. I select an arbitrary
time window of 2 weeks, and split the calls until we get all required
data
"""
def _clear_out_of_market_hours(df):
"""
only interested in samples between 9:30, 16:00 NY time
"""
return df.between_time("09:30", "16:00")
def _fillna(df, granularity, start, end):
if granularity != 'day':
return df
if df.empty:
return df
calendar: TradingCalendar = trading_calendars.get_calendar("NYSE")
last_val = df.iloc[0]
current = start
while current <= end:
if calendar.is_session(current):
if current.replace(tzinfo=tz.gettz(NY)) in df.index:
last_val = df.loc[current.replace(tzinfo=tz.gettz(NY))]
else:
# df.loc[pytz.timezone(NY).localize(current)] = last_val
df.loc[current.replace(tzinfo=tz.gettz(NY))] = last_val
current += timedelta(days=1)
return df
if granularity == 'day':
cdl = CLIENT.polygon.historic_agg_v2(
dataname,
compression,
granularity,
_from=iso_date(dtbegin.isoformat()),
to=iso_date(dtend.isoformat())).df
cdl = _fillna(cdl, granularity, dtbegin, dtend)
else:
cdl = pd.DataFrame()
segment_start = dtbegin
segment_end = segment_start + timedelta(weeks=2) if \
dtend - dtbegin >= timedelta(weeks=2) else dtend
while segment_end <= dtend and dtend not in cdl.index:
response = CLIENT.polygon.historic_agg_v2(
dataname,
compression,
granularity,
_from=iso_date(segment_start.isoformat()),
to=iso_date(segment_end.isoformat()))
# No result from the server, most likely error
if response.df.shape[0] == 0 and cdl.shape[0] == 0:
raise Exception("received empty response")
temp = response.df
cdl = pd.concat([cdl, temp])
cdl = cdl[~cdl.index.duplicated()]
segment_start = segment_end
segment_end = segment_start + timedelta(weeks=2) if \
dtend - dtbegin >= timedelta(weeks=2) else dtend
cdl = _clear_out_of_market_hours(cdl)
return cdl
def df_generator(interval, start, end):
exchange = 'NYSE'
for sid, symbol in enumerate(list_assets()):
try:
df = get_aggs_from_polygon(symbol, start, end, 'day' if interval == '1d' else 'minute', 1)
if df.empty:
continue
start_date = df.index[0]
end_date = df.index[-1]
first_traded = start
auto_close_date = end + pd.Timedelta(days=1)
# # Check if there is any missing session; skip the ticker pair otherwise
# if interval == '1d' and len(df.index) - 1 != pd.Timedelta(end_date - start_date).days:
# # print('Missing sessions found in {}. Skip importing'.format(ticker_pair))
# continue
# elif interval == '1m' and timedelta(minutes=(len(df.index) + 60)) != end_date - start_date:
# # print('Missing sessions found in {}. Skip importing'.format(ticker_pair))
# continue
yield (sid, df.sort_index()), symbol, start, end, first_traded, auto_close_date, exchange
except Exception as e:
import traceback
traceback.print_exc()
print(f"error while processig {(sid, symbol)}: {e}")
def metadata_df():
metadata_dtype = [
('symbol', 'object'),
# ('asset_name', 'object'),
('start_date', 'datetime64[ns]'),
('end_date', 'datetime64[ns]'),
('first_traded', 'datetime64[ns]'),
('auto_close_date', 'datetime64[ns]'),
('exchange', 'object'), ]
metadata_df = pd.DataFrame(
np.empty(len(list_assets()), dtype=metadata_dtype))
return metadata_df
@bundles.register('polygon_api', calendar_name="NYSE", minutes_per_day=390)
def api_to_bundle(interval=['1m']):
def ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir
):
def minute_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in df_generator(interval='1m',
start=start_session,
end=end_session))
def daily_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in df_generator(interval='1d',
start=start_session,
end=end_session))
for _interval in interval:
metadata = metadata_df()
if _interval == '1d':
daily_bar_writer.write(daily_data_generator(), show_progress=True)
elif _interval == '1m':
minute_bar_writer.write(
minute_data_generator(), show_progress=True)
# Drop the ticker rows which have missing sessions in their data sets
metadata.dropna(inplace=True)
asset_db_writer.write(equities=metadata)
print(metadata)
adjustment_writer.write()
return ingest
if __name__ == '__main__':
from zipline.data.bundles import register
from zipline.data import bundles as bundles_module
import trading_calendars
import os
cal: TradingCalendar = trading_calendars.get_calendar('NYSE')
start_date = pd.Timestamp('2019-08-03 0:00', tz='utc')
while not cal.is_session(start_date):
start_date += timedelta(days=1)
end_date = pd.Timestamp('now', tz='utc').date() - timedelta(days=1)
while not cal.is_session(end_date):
end_date -= timedelta(days=1)
end_date = pd.Timestamp(end_date, tz='utc')
initialize_client()
register(
'polygon_api',
# api_to_bundle(interval=['1d', '1m']),
# api_to_bundle(interval=['1m']),
api_to_bundle(interval=['1d']),
calendar_name='NYSE',
start_session=start_date,
end_session=end_date
)
assets_version = ((),)[0] # just a weird way to create an empty tuple
bundles_module.ingest(
"polygon_api",
os.environ,
assets_versions=assets_version,
show_progress=True,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/polygon_api.py | polygon_api.py |
import bs4 as bs
from binance.client import Client
import csv
from datetime import datetime as dt
from datetime import timedelta
import numpy as np
from os import listdir, mkdir, remove
from os.path import exists, isfile, join
from pathlib import Path
import pandas as pd
import pickle
import requests
from trading_calendars import register_calendar
# from trading_calendars.exchange_calendar_binance import BinanceExchangeCalendar
import yaml
from zipline.data.bundles import core as bundles
user_home = str(Path.home())
custom_data_path = join(user_home, '.zipline/custom_data')
CLIENT: Client = None
def initialize_client():
global CLIENT
with open("binance.yaml", mode='r') as f:
o = yaml.safe_load(f)
key = o["key_id"]
secret = o["secret"]
CLIENT = Client(key, secret)
def get_binance_pairs(**kwargs):
base_currencies = kwargs.get('base_currencies', '')
quote_currencies = kwargs.get('quote_currencies', '')
binance_pairs = list()
all_tickers = CLIENT.get_all_tickers()
# if not self.futures:
# all_tickers = CLIENT.get_all_tickers()
# else:
# all_tickers = CLIENT.futures_ticker()
if base_currencies and quote_currencies:
input_pairs = [x + y for x in quote_currencies for y in base_currencies]
for x, currency_pair in enumerate(all_tickers):
if base_currencies and quote_currencies:
for pair in input_pairs:
if currency_pair['symbol'] == pair.upper():
binance_pairs.append(currency_pair['symbol'])
break
elif base_currencies:
for base_currency in base_currencies:
if currency_pair['symbol'][-len(base_currency):] == base_currency.upper():
binance_pairs.append(currency_pair['symbol'])
break
elif quote_currencies:
for quote_currency in quote_currencies:
if currency_pair['symbol'][:len(quote_currency)] == quote_currency.upper():
binance_pairs.append(currency_pair['symbol'])
break
else:
binance_pairs.append(currency_pair['symbol'])
if binance_pairs:
return binance_pairs
else:
raise ValueError('Invalid Input: Binance returned no matching currency pairs.')
def tickers():
"""
Save Binance trading pair tickers to a pickle file
Return a list of trading ticker pairs
"""
cmc_binance_url = 'https://coinmarketcap.com/exchanges/binance/'
response = requests.get(cmc_binance_url)
if response.ok:
soup = bs.BeautifulSoup(response.text, 'html.parser')
table = soup.find('table', {'id': 'exchange-markets'})
ticker_pairs = []
for row in table.findAll('tr')[1:]:
ticker_pair = row.findAll('td')[2].text
ticker_pairs.append(ticker_pair.strip().replace('/', ''))
if not exists(custom_data_path):
mkdir(custom_data_path)
with open(join(custom_data_path, 'binance_ticker_pairs.pickle'), 'wb') as f:
pickle.dump(ticker_pairs, f)
return ticker_pairs
def tickers_generator():
"""
Return a tuple (sid, ticker_pair)
"""
tickers_file = join(custom_data_path, 'binance_ticker_pairs.pickle')
if not isfile(tickers_file):
ticker_pairs = get_binance_pairs()
else:
with open(tickers_file, 'rb') as f:
ticker_pairs = pickle.load(f)[:]
return (tuple((sid, ticker)) for sid, ticker in enumerate(ticker_pairs))
def df_generator(interval):
start = '2017-7-14' # Binance launch date
end = dt.utcnow().strftime('%Y-%m-%d') # Current day
for item in tickers_generator():
try:
sid = item[0]
ticker_pair = item[1]
df = pd.DataFrame(
columns=['date', 'open', 'high', 'low', 'close', 'volume'])
symbol = ticker_pair
print(symbol, interval)
asset_name = ticker_pair
exchange = 'Binance'
klines = CLIENT.get_historical_klines_generator(
ticker_pair, interval, start, end)
for kline in klines:
line = kline[:]
del line[6:]
# Make a real copy of kline
# Binance API forbids the change of open time
line[0] = np.datetime64(line[0], 'ms')
line[0] = pd.Timestamp(line[0], 'ms')
df.loc[len(df)] = line
df['date'] = pd.to_datetime(df['date'])
df.set_index('date', inplace=True)
df = df.astype({'open': 'float64', 'high': 'float64',
'low': 'float64', 'close': 'float64', 'volume': 'float64'})
start_date = df.index[0]
end_date = df.index[-1]
first_traded = start_date
auto_close_date = end_date + pd.Timedelta(days=1)
# Check if there is any missing session; skip the ticker pair otherwise
if interval == '1d' and len(df.index) - 1 != pd.Timedelta(end_date - start_date).days:
# print('Missing sessions found in {}. Skip importing'.format(ticker_pair))
continue
elif interval == '1m' and timedelta(minutes=(len(df.index) + 60)) != end_date - start_date:
# print('Missing sessions found in {}. Skip importing'.format(ticker_pair))
continue
yield (sid, df), symbol, asset_name, start_date, end_date, first_traded, auto_close_date, exchange
except Exception as e:
print(f"error while processig {ticker_pair}: {e}")
def metadata_df():
metadata_dtype = [
('symbol', 'object'),
('asset_name', 'object'),
('start_date', 'datetime64[ns]'),
('end_date', 'datetime64[ns]'),
('first_traded', 'datetime64[ns]'),
('auto_close_date', 'datetime64[ns]'),
('exchange', 'object'), ]
metadata_df = pd.DataFrame(
np.empty(len(get_binance_pairs()), dtype=metadata_dtype))
return metadata_df
@bundles.register('binance_api', calendar_name="24/7", minutes_per_day=1440)
def api_to_bundle(interval=['1m']):
def ingest(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir
):
def minute_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in df_generator(interval='1m'))
def daily_data_generator():
return (sid_df for (sid_df, *metadata.iloc[sid_df[0]]) in df_generator(interval='1d'))
for _interval in interval:
metadata = metadata_df()
if _interval == '1d':
daily_bar_writer.write(
daily_data_generator(), show_progress=True)
elif _interval == '1m':
minute_bar_writer.write(
minute_data_generator(), show_progress=True)
# Drop the ticker rows which have missing sessions in their data sets
metadata.dropna(inplace=True)
asset_db_writer.write(equities=metadata)
print(metadata)
adjustment_writer.write()
return ingest
if __name__ == '__main__':
from zipline.data.bundles import register
from zipline.data import bundles as bundles_module
import os
initialize_client()
register(
'binance_api',
# api_to_bundle(interval=['1d', '1m']),
api_to_bundle(interval=['1m']),
# api_to_bundle(interval=['1d']),
calendar_name='24/7',
)
assets_version = ((),)[0] # just a weird way to create an empty tuple
bundles_module.ingest(
"binance_api",
os.environ,
pd.Timestamp.utcnow(),
assets_version,
True,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/binance_api.py | binance_api.py |
from io import BytesIO
import tarfile
from zipfile import ZipFile
from click import progressbar
from logbook import Logger
import pandas as pd
import requests
from six.moves.urllib.parse import urlencode
from six import iteritems
from trading_calendars import register_calendar_alias
from . import core as bundles
import numpy as np
log = Logger(__name__)
ONE_MEGABYTE = 1024 * 1024
QUANDL_DATA_URL = (
'https://www.quandl.com/api/v3/datatables/WIKI/PRICES.csv?'
)
def format_metadata_url(api_key):
""" Build the query URL for Quandl WIKI Prices metadata.
"""
query_params = [('api_key', api_key), ('qopts.export', 'true')]
return (
QUANDL_DATA_URL + urlencode(query_params)
)
def load_data_table(file,
index_col,
show_progress=False):
""" Load data table from zip file provided by Quandl.
"""
with ZipFile(file) as zip_file:
file_names = zip_file.namelist()
assert len(file_names) == 1, "Expected a single file from Quandl."
wiki_prices = file_names.pop()
with zip_file.open(wiki_prices) as table_file:
if show_progress:
log.info('Parsing raw data.')
data_table = pd.read_csv(
table_file,
parse_dates=['date'],
index_col=index_col,
usecols=[
'ticker',
'date',
'open',
'high',
'low',
'close',
'volume',
'ex-dividend',
'split_ratio',
],
)
data_table.rename(
columns={
'ticker': 'symbol',
'ex-dividend': 'ex_dividend',
},
inplace=True,
copy=False,
)
return data_table
def fetch_data_table(api_key,
show_progress,
retries):
""" Fetch WIKI Prices data table from Quandl
"""
for _ in range(retries):
try:
if show_progress:
log.info('Downloading WIKI metadata.')
metadata = pd.read_csv(
format_metadata_url(api_key)
)
# Extract link from metadata and download zip file.
table_url = metadata.loc[0, 'file.link']
if show_progress:
raw_file = download_with_progress(
table_url,
chunk_size=ONE_MEGABYTE,
label="Downloading WIKI Prices table from Quandl"
)
else:
raw_file = download_without_progress(table_url)
return load_data_table(
file=raw_file,
index_col=None,
show_progress=show_progress,
)
except Exception:
log.exception("Exception raised reading Quandl data. Retrying.")
else:
raise ValueError(
"Failed to download Quandl data after %d attempts." % (retries)
)
def gen_asset_metadata(data, show_progress):
if show_progress:
log.info('Generating asset metadata.')
data = data.groupby(
by='symbol'
).agg(
{'date': [np.min, np.max]}
)
data.reset_index(inplace=True)
data['start_date'] = data.date.amin
data['end_date'] = data.date.amax
del data['date']
data.columns = data.columns.get_level_values(0)
data['exchange'] = 'QUANDL'
data['auto_close_date'] = data['end_date'].values + pd.Timedelta(days=1)
return data
def parse_splits(data, show_progress):
if show_progress:
log.info('Parsing split data.')
data['split_ratio'] = 1.0 / data.split_ratio
data.rename(
columns={
'split_ratio': 'ratio',
'date': 'effective_date',
},
inplace=True,
copy=False,
)
return data
def parse_dividends(data, show_progress):
if show_progress:
log.info('Parsing dividend data.')
data['record_date'] = data['declared_date'] = data['pay_date'] = pd.NaT
data.rename(
columns={
'ex_dividend': 'amount',
'date': 'ex_date',
},
inplace=True,
copy=False,
)
return data
def parse_pricing_and_vol(data,
sessions,
symbol_map):
for asset_id, symbol in iteritems(symbol_map):
asset_data = data.xs(
symbol,
level=1
).reindex(
sessions.tz_localize(None)
).fillna(0.0)
yield asset_id, asset_data
@bundles.register('quandl')
def quandl_bundle(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir):
"""
quandl_bundle builds a daily dataset using Quandl's WIKI Prices dataset.
For more information on Quandl's API and how to obtain an API key,
please visit https://docs.quandl.com/docs#section-authentication
"""
api_key = environ.get('QUANDL_API_KEY')
if api_key is None:
raise ValueError(
"Please set your QUANDL_API_KEY environment variable and retry."
)
raw_data = fetch_data_table(
api_key,
show_progress,
environ.get('QUANDL_DOWNLOAD_ATTEMPTS', 5)
)
asset_metadata = gen_asset_metadata(
raw_data[['symbol', 'date']],
show_progress
)
asset_db_writer.write(asset_metadata)
symbol_map = asset_metadata.symbol
sessions = calendar.sessions_in_range(start_session, end_session)
raw_data.set_index(['date', 'symbol'], inplace=True)
daily_bar_writer.write(
parse_pricing_and_vol(
raw_data,
sessions,
symbol_map
),
show_progress=show_progress
)
raw_data.reset_index(inplace=True)
raw_data['symbol'] = raw_data['symbol'].astype('category')
raw_data['sid'] = raw_data.symbol.cat.codes
adjustment_writer.write(
splits=parse_splits(
raw_data[[
'sid',
'date',
'split_ratio',
]].loc[raw_data.split_ratio != 1],
show_progress=show_progress
),
dividends=parse_dividends(
raw_data[[
'sid',
'date',
'ex_dividend',
]].loc[raw_data.ex_dividend != 0],
show_progress=show_progress
)
)
def download_with_progress(url, chunk_size, **progress_kwargs):
"""
Download streaming data from a URL, printing progress information to the
terminal.
Parameters
----------
url : str
A URL that can be understood by ``requests.get``.
chunk_size : int
Number of bytes to read at a time from requests.
**progress_kwargs
Forwarded to click.progressbar.
Returns
-------
data : BytesIO
A BytesIO containing the downloaded data.
"""
resp = requests.get(url, stream=True)
resp.raise_for_status()
total_size = int(resp.headers['content-length'])
data = BytesIO()
with progressbar(length=total_size, **progress_kwargs) as pbar:
for chunk in resp.iter_content(chunk_size=chunk_size):
data.write(chunk)
pbar.update(len(chunk))
data.seek(0)
return data
def download_without_progress(url):
"""
Download data from a URL, returning a BytesIO containing the loaded data.
Parameters
----------
url : str
A URL that can be understood by ``requests.get``.
Returns
-------
data : BytesIO
A BytesIO containing the downloaded data.
"""
resp = requests.get(url)
resp.raise_for_status()
return BytesIO(resp.content)
QUANTOPIAN_QUANDL_URL = (
'https://s3.amazonaws.com/quantopian-public-zipline-data/quandl'
)
@bundles.register('quantopian-quandl', create_writers=False)
def quantopian_quandl_bundle(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir):
if show_progress:
data = download_with_progress(
QUANTOPIAN_QUANDL_URL,
chunk_size=ONE_MEGABYTE,
label="Downloading Bundle: quantopian-quandl",
)
else:
data = download_without_progress(QUANTOPIAN_QUANDL_URL)
with tarfile.open('r', fileobj=data) as tar:
if show_progress:
log.info("Writing data to %s." % output_dir)
tar.extractall(output_dir)
register_calendar_alias("QUANDL", "NYSE") | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/quandl.py | quandl.py |
from io import BytesIO
from zipfile import ZipFile
from click import progressbar
from logbook import Logger
import pandas as pd
import requests
from six.moves.urllib.parse import urlencode
from six import iteritems
from trading_calendars import register_calendar_alias
from zipline.data.bundles import core as bundles # looking in .zipline/extensions.py
import numpy as np
# Code from:
# Quantopian Zipline Issues:
# "Cannot find data bundle during ingest #2275"
# https://github.com/quantopian/zipline/issues/2275
log = Logger(__name__)
ONE_MEGABYTE = 1024 * 1024
QUANDL_DATA_URL = (
'https://www.quandl.com/api/v3/datatables/SHARADAR/SEP.csv?'
)
@bundles.register('sharadar-prices')
def sharadar_prices_bundle(environ,
asset_db_writer,
minute_bar_writer,
daily_bar_writer,
adjustment_writer,
calendar,
start_session,
end_session,
cache,
show_progress,
output_dir):
api_key = environ.get('QUANDL_API_KEY')
if api_key is None:
raise ValueError(
"Please set your QUANDL_API_KEY environment variable and retry."
)
###ticker2sid_map = {}
raw_data = fetch_data_table(
api_key,
show_progress,
environ.get('QUANDL_DOWNLOAD_ATTEMPTS', 5)
)
asset_metadata = gen_asset_metadata(
raw_data[['symbol', 'date']],
show_progress
)
asset_db_writer.write(asset_metadata)
symbol_map = asset_metadata.symbol
sessions = calendar.sessions_in_range(start_session, end_session)
raw_data.set_index(['date', 'symbol'], inplace=True)
daily_bar_writer.write(
parse_pricing_and_vol(
raw_data,
sessions,
symbol_map
),
show_progress=show_progress
)
raw_data.reset_index(inplace=True)
# raw_data.index = pd.DatetimeIndex(raw_data.date)
###ajjc changes
raw_data['symbol'] = raw_data['symbol'].astype('category')
raw_data['sid'] = raw_data.symbol.cat.codes
# read in Dividend History
# ajjc pharrin----------------------
###uv = raw_data.symbol.unique() # get unique m_tickers (Zacks primary key)
# iterate over all the unique securities and pack data, and metadata
# for writing
# counter of valid securites, this will be our primary key
###sec_counter = 0
###for tkr in uv:
### #df_tkr = raw_data[raw_data['symbol'] == tkr]
### ticker2sid_map[tkr] = sec_counter # record the sid for use later
### sec_counter += 1
### dfd = pd.read_csv(file_name, index_col='date',
### parse_dates=['date'], na_values=['NA'])
# drop rows where dividends == 0.0
raw_data = raw_data[raw_data["dividends"] != 0.0]
raw_data.set_index(['date', 'sid'], inplace=True)
# raw_data.loc[:, 'ex_date'] = raw_data.loc[:, 'record_date'] = raw_data.date
# raw_data.loc[:, 'declared_date'] = raw_data.loc[:, 'pay_date'] = raw_data.date
raw_data.loc[:, 'ex_date'] = raw_data.loc[:, 'record_date'] = raw_data.index.get_level_values('date')
raw_data.loc[:, 'declared_date'] = raw_data.loc[:, 'pay_date'] = raw_data.index.get_level_values('date')
# raw_data.loc[:, 'sid'] = raw_data.loc[:, 'symbol'].apply(lambda x: ticker2sid_map[x])
raw_data = raw_data.rename(columns={'dividends': 'amount'})
# raw_data = raw_data.drop(['open', 'high', 'low', 'close', 'volume','symbol'], axis=1)
raw_data.reset_index(inplace=True)
raw_data = raw_data.drop(['open', 'high', 'low', 'close', 'volume', 'symbol', 'date'], axis=1)
# raw_data = raw_data.drop(['open', 'high', 'low', 'close', 'volume', 'lastupdated', 'ticker', 'closeunadj'], axis=1)
# # format dfd to have sid
adjustment_writer.write(dividends=raw_data)
# ajjc ----------------------------------
def format_metadata_url(api_key):
""" Build the query URL for Quandl Prices metadata.
"""
query_params = [('api_key', api_key), ('qopts.export', 'true')]
return (
QUANDL_DATA_URL + urlencode(query_params)
)
def load_data_table(file,
index_col,
show_progress=False):
""" Load data table from zip file provided by Quandl.
"""
with ZipFile(file) as zip_file:
file_names = zip_file.namelist()
assert len(file_names) == 1, "Expected a single file from Quandl."
wiki_prices = file_names.pop()
with zip_file.open(wiki_prices) as table_file:
if show_progress:
log.info('Parsing raw data.')
data_table = pd.read_csv(
table_file,
parse_dates=['date'],
index_col=index_col,
usecols=[
'ticker',
'date',
'open',
'high',
'low',
'close',
'volume',
'dividends',
##'closeunadj',
##'lastupdated' #prune last two columns for zipline bundle load
],
)
data_table.rename(
columns={
'ticker': 'symbol'
},
inplace=True,
copy=False,
)
return data_table
def fetch_data_table(api_key,
show_progress,
retries):
for _ in range(retries):
try:
if show_progress:
log.info('Downloading Sharadar Price metadata.')
metadata = pd.read_csv(
format_metadata_url(api_key)
)
# Extract link from metadata and download zip file.
table_url = metadata.loc[0, 'file.link']
if show_progress:
raw_file = download_with_progress(
table_url,
chunk_size=ONE_MEGABYTE,
label="Downloading Prices table from Quandl Sharadar"
)
else:
raw_file = download_without_progress(table_url)
return load_data_table(
file=raw_file,
index_col=None,
show_progress=show_progress,
)
except Exception:
log.exception("Exception raised reading Quandl data. Retrying.")
else:
raise ValueError(
"Failed to download Quandl data after %d attempts." % (retries)
)
def gen_asset_metadata(data, show_progress):
if show_progress:
log.info('Generating asset metadata.')
data = data.groupby(
by='symbol'
).agg(
{'date': [np.min, np.max]}
)
data.reset_index(inplace=True)
data['start_date'] = data.date.amin
data['end_date'] = data.date.amax
del data['date']
data.columns = data.columns.get_level_values(0)
data['exchange'] = 'QUANDL'
data['auto_close_date'] = data['end_date'].values + pd.Timedelta(days=1)
return data
def parse_pricing_and_vol(data,
sessions,
symbol_map):
for asset_id, symbol in iteritems(symbol_map):
asset_data = data.xs(
symbol,
level=1
).reindex(
sessions.tz_localize(None)
).fillna(0.0)
yield asset_id, asset_data
def download_with_progress(url, chunk_size, **progress_kwargs):
"""
Download streaming data from a URL, printing progress information to the
terminal.
Parameters
----------
url : str
A URL that can be understood by ``requests.get``.
chunk_size : int
Number of bytes to read at a time from requests.
**progress_kwargs
Forwarded to click.progressbar.
Returns
-------
data : BytesIO
A BytesIO containing the downloaded data.
"""
resp = requests.get(url, stream=True)
resp.raise_for_status()
total_size = int(resp.headers['content-length'])
data = BytesIO()
with progressbar(length=total_size, **progress_kwargs) as pbar:
for chunk in resp.iter_content(chunk_size=chunk_size):
data.write(chunk)
pbar.update(len(chunk))
data.seek(0)
return data
def download_without_progress(url):
"""
Download data from a URL, returning a BytesIO containing the loaded data.
Parameters
----------
url : str
A URL that can be understood by ``requests.get``.
Returns
-------
data : BytesIO
A BytesIO containing the downloaded data.
"""
resp = requests.get(url)
resp.raise_for_status()
return BytesIO(resp.content)
register_calendar_alias("sharadar-prices", "NYSE") | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/bundles/sharadar.py | sharadar.py |
from interface import default, Interface
import numpy as np
import pandas as pd
from zipline.utils.sentinel import sentinel
from zipline.lib._factorize import factorize_strings
DEFAULT_FX_RATE = sentinel('DEFAULT_FX_RATE')
class FXRateReader(Interface):
"""
Interface for reading foreign exchange (fx) rates.
An FX rate reader contains one or more distinct "rates", each of which
corresponds to a collection of mappings from (quote, base, dt) ->
float. The value produced for a given (quote, base, dt) triple is the
exchange rate to use when converting from ``base`` to ``quote`` on ``dt``.
The specific set of rates contained in a particular reader is
user-defined. We infer no particular semantics from their names, other than
that they are distinct rates. Examples of possible rate names might be
things like "bid", "mid", and "ask", or "london_close", "tokyo_close",
"nyse_close".
Implementations of :class:`FXRateReader` must provide at least one method::
def get_rates(self, rate, quote, bases, dts):
which takes a rate, a quote currency, an array of base currencies, and an
array of dts, and produces a (len(dts), len(base))-shape array containing a
conversion rates for all pairs in the cartesian product of bases and dts.
Given a definition of :meth:`get_rates`, this interface automatically
generates two additional methods::
def get_rates_scalar(self, rate, quote, base, dt):
and::
def get_rates_columnar(self, rate, quote, bases, dts):
:meth:`get_rates_scalar` takes scalar-valued ``base`` and ``dt`` values,
and returns a scalar float value for the requested fx rate.
:meth:`get_rates_columnar` takes parallel arrays of ``bases`` and ``dts``
and returns a same-length array of fx rates by performing a lookup on the
(base, dt) pairs drawn from zipping together ``bases``, and ``dts``. In
other words, its behavior is equivalent to::
def get_rates_columnnar(self, rate, quote, bases, dts):
out = []
for base, dt in zip(bases, dts):
out.append(self.get_rate_scalar(rate, quote, base, dt))
return np.array(out)
"""
def get_rates(self, rate, quote, bases, dts):
"""
Load a 2D array of fx rates.
Parameters
----------
rate : str
Name of the rate to load.
quote : str
Currency code of the currency to convert into.
bases : np.array[object]
Array of codes of the currencies to convert from. The same currency
may appear multiple times.
dts : pd.DatetimeIndex
Datetimes for which to load rates. Must be sorted in ascending
order and localized to UTC.
Returns
-------
rates : np.array
Array of shape ``(len(dts), len(bases))`` containing foreign
exchange rates mapping currencies from ``bases`` to ``quote``.
The row at index i corresponds to the dt in dts[i].
The column at index j corresponds to the base currency in bases[j].
"""
@default
def get_rate_scalar(self, rate, quote, base, dt):
"""
Load a scalar FX rate value.
Parameters
----------
rate : str
Name of the rate to load.
quote : str
Currency code of the currency to convert into.
base : str
Currency code of the currency to convert from.
dt : np.datetime64 or pd.Timestamp
Datetime on which to load rate.
Returns
-------
rate : np.float64
Exchange rate from base -> quote on dt.
"""
rates_2d = self.get_rates(
rate,
quote,
bases=np.array([base], dtype=object),
dts=pd.DatetimeIndex([dt], tz='UTC'),
)
return rates_2d[0, 0]
@default
def get_rates_columnar(self, rate, quote, bases, dts):
"""
Load a 1D array of FX rates.
Parameters
----------
rate : str
Name of the rate to load.
quote : str
Currency code of the currency to convert into.
bases : np.array[object]
Array of codes of the currencies to convert from. The same currency
may appear multiple times.
dts : np.DatetimeIndex
Datetimes for which to load rates. The same value may appear
multiple times. Datetimes do not need to be sorted.
"""
if len(bases) != len(dts):
raise ValueError(
"len(bases) ({}) != len(dts) ({})".format(len(bases), len(dts))
)
bases_ix, unique_bases, _ = factorize_strings(
bases,
missing_value=None,
# Only dts need to be sorted, not bases.
sort=False,
)
# NOTE: np.unique returns unique_dts in sorted order, which is required
# for calling get_rates.
unique_dts, dts_ix = np.unique(dts.values, return_inverse=True)
rates_2d = self.get_rates(
rate,
quote,
unique_bases,
pd.DatetimeIndex(unique_dts, tz='utc')
)
return rates_2d[dts_ix, bases_ix] | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/fx/base.py | base.py |
from interface import implements
import h5py
from logbook import Logger
import numpy as np
import pandas as pd
from zipline.utils.memoize import lazyval
from zipline.utils.numpy_utils import bytes_array_to_native_str_object_array
from .base import FXRateReader, DEFAULT_FX_RATE
from .utils import check_dts, is_sorted_ascending
HDF5_FX_VERSION = 0
HDF5_FX_DEFAULT_CHUNK_SIZE = 75
INDEX = 'index'
DATA = 'data'
CURRENCIES = 'currencies'
DTS = 'dts'
RATES = 'rates'
log = Logger(__name__)
class HDF5FXRateReader(implements(FXRateReader)):
"""An FXRateReader backed by HDF5.
Parameters
----------
group : h5py.Group
Top-level group written by an :class:`HDF5FXRateWriter`.
default_rate : str
Rate to use when ``get_rates`` is called requesting the default rate.
"""
def __init__(self, group, default_rate):
self._group = group
self._default_rate = default_rate
if self.version != HDF5_FX_VERSION:
raise ValueError(
"FX Reader version ({}) != File Version ({})".format(
HDF5_FX_VERSION, self.version,
)
)
@classmethod
def from_path(cls, path, default_rate):
"""
Construct from a file path.
Parameters
----------
path : str
Path to an HDF5 fx rates file.
default_rate : str
Rate to use when ``get_rates`` is called requesting the default
rate.
"""
return cls(h5py.File(path), default_rate=default_rate)
@lazyval
def version(self):
try:
return self._group.attrs['version']
except KeyError:
# TODO: Remove this.
return 0
@lazyval
def dts(self):
"""Column labels for rate groups.
"""
raw_dts = self._group[INDEX][DTS][:].astype('M8[ns]')
if not is_sorted_ascending(raw_dts):
raise ValueError("dts are not sorted for {}!".format(self._group))
return pd.DatetimeIndex(raw_dts, tz='UTC')
@lazyval
def currencies(self):
"""Row labels for rate groups.
"""
# Currencies are stored as fixed-length bytes in the file, but we want
# `str` objects in memory.
bytes_array = self._group[INDEX][CURRENCIES][:]
objects = bytes_array_to_native_str_object_array(bytes_array)
return pd.Index(objects)
def get_rates(self, rate, quote, bases, dts):
"""Get rates to convert ``bases`` into ``quote``.
See :class:`zipline.data.fx.base.FXRateReader` for details.
"""
if rate == DEFAULT_FX_RATE:
rate = self._default_rate
check_dts(dts)
col_ixs = self.dts.searchsorted(dts, side='right') - 1
row_ixs = self.currencies.get_indexer(bases)
try:
dataset = self._group[DATA][rate][quote][RATES]
except KeyError:
raise ValueError(
"FX rates not available for rate={}, quote_currency={}."
.format(rate, quote)
)
# OPTIMIZATION: Column indices correspond to dates, which must be in
# sorted order. Rather than reading the entire dataset from h5, we can
# read just the interval from min_col to max_col inclusive
#
# However, we also need to handle two important edge cases:
#
# 1. row_ixs contains -1 for any currencies we don't know about.
# 2. col_ixs contains -1 for dts before the start of self.dts.
#
# If either of the above cases obtains, we want to return NaN for the
# corresponding output locations.
# We handle each of these cases by reading raw data into a buffer with
# one extra column and one extra row. When we then permute the raw data
# into the correct order, any row or column indices with values of -1
# will pull from the extra row/column, which will always contain NaN.
slice_begin = max(col_ixs[0], 0)
slice_end = max(col_ixs[-1], 0) + 1 # +1 to be inclusive of end date.
# Allocate a buffer full of NaNs with one extra column and row. See
# OPTIMIZATION notes above.
buf = np.full(
(len(self.currencies) + 1, slice_end - slice_begin + 1),
np.nan,
)
buf[:-1, :-1] = dataset[:, slice_begin:slice_end]
# Permute the rows into place, pulling from the empty NaN locations for
# row and column indices of -1.
out = buf[:, col_ixs - slice_begin][row_ixs]
# Transpose everything to maintain dts as row labels, currencies as col
# labels which is expected everywhere else.
return out.transpose()
class HDF5FXRateWriter(object):
"""Writer class for HDF5 files consumed by HDF5FXRateReader.
"""
def __init__(self, group, date_chunk_size=HDF5_FX_DEFAULT_CHUNK_SIZE):
self._group = group
self._date_chunk_size = date_chunk_size
def write(self, dts, currencies, data):
"""Write data to the file.
Parameters
----------
dts : pd.DatetimeIndex
Index of row labels for rates to be written.
currencies : np.array[object]
Array of column labels for rates to be written.
data : iterator[(str, str, np.array[float64])]
Iterator of (rate, quote_currency, array) tuples. Each array
should be of shape ``(len(dts), len(currencies))``, and should
contain a table of rates where each column is a timeseries of rates
mapping its column label's currency to ``quote_currency``.
"""
if len(currencies):
chunks = (len(currencies), min(self._date_chunk_size, len(dts)))
else:
# h5py crashes if we provide chunks for empty data.
chunks = None
self._write_metadata()
self._write_index_group(dts, currencies)
self._write_data_group(dts, currencies, data, chunks)
def _write_metadata(self):
self._group.attrs['version'] = HDF5_FX_VERSION
self._group.attrs['last_updated_utc'] = str(pd.Timestamp.utcnow())
def _write_index_group(self, dts, currencies):
"""Write content of /index.
"""
if not is_sorted_ascending(dts):
raise ValueError("dts is not sorted")
for c in currencies:
if not isinstance(c, str) or len(c) != 3:
raise ValueError("Invalid currency: {!r}".format(c))
index_group = self._group.create_group(INDEX)
self._log_writing(INDEX, DTS)
index_group.create_dataset(DTS, data=dts.astype('int64'))
self._log_writing(INDEX, CURRENCIES)
index_group.create_dataset(CURRENCIES, data=currencies.astype('S3'))
def _write_data_group(self, dts, currencies, data, chunks):
"""Write content of /data.
"""
data_group = self._group.create_group(DATA)
expected_shape = (len(dts), len(currencies))
for rate, quote, array in data:
if array.shape != expected_shape:
raise ValueError(
"Unexpected shape for rate={}, quote={}."
"\nExpected shape: {}. Got {}."
.format(rate, quote, expected_shape, array.shape)
)
self._log_writing(DATA, rate, quote)
target = data_group.require_group('/'.join((rate, quote)))
# Transpose the rates array so that the hdf5 file holds arrays
# with currencies as row labels and dates as column labels. This
# helps with compression, as the *rows* (rather than the columns)
# all have similar values, which lends itself to the HDF5 file's
# C-contiguous storage.
target.create_dataset(RATES,
data=array.transpose(),
chunks=chunks,
compression='lzf',
shuffle=True)
def _log_writing(self, *path):
log.debug("Writing {}", '/'.join(path)) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/data/fx/hdf5.py | hdf5.py |
import sys
from textwrap import dedent
class _Sentinel(object):
"""Base class for Sentinel objects.
"""
__slots__ = ('__weakref__',)
def is_sentinel(obj):
return isinstance(obj, _Sentinel)
def sentinel(name, doc=None):
try:
value = sentinel._cache[name] # memoized
except KeyError:
pass
else:
if doc == value.__doc__:
return value
raise ValueError(dedent(
"""\
New sentinel value %r conflicts with an existing sentinel of the
same name.
Old sentinel docstring: %r
New sentinel docstring: %r
The old sentinel was created at: %s
Resolve this conflict by changing the name of one of the sentinels.
""",
) % (name, value.__doc__, doc, value._created_at))
try:
frame = sys._getframe(1)
except ValueError:
frame = None
if frame is None:
created_at = '<unknown>'
else:
created_at = '%s:%s' % (frame.f_code.co_filename, frame.f_lineno)
@object.__new__ # bind a single instance to the name 'Sentinel'
class Sentinel(_Sentinel):
__doc__ = doc
__name__ = name
# store created_at so that we can report this in case of a duplicate
# name violation
_created_at = created_at
def __new__(cls):
raise TypeError('cannot create %r instances' % name)
def __repr__(self):
return 'sentinel(%r)' % name
def __reduce__(self):
return sentinel, (name, doc)
def __deepcopy__(self, _memo):
return self
def __copy__(self):
return self
cls = type(Sentinel)
try:
cls.__module__ = frame.f_globals['__name__']
except (AttributeError, KeyError):
# Couldn't get the name from the calling scope, just use None.
# AttributeError is when frame is None, KeyError is when f_globals
# doesn't hold '__name__'
cls.__module__ = None
sentinel._cache[name] = Sentinel # cache result
return Sentinel
sentinel._cache = {} | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/sentinel.py | sentinel.py |
import datetime
from copy import deepcopy
import numpy as np
import pandas as pd
def _ensure_index(x):
if not isinstance(x, pd.Index):
x = pd.Index(sorted(x))
return x
class RollingPanel(object):
"""
Preallocation strategies for rolling window over expanding data set
Restrictions: major_axis can only be a DatetimeIndex for now
"""
def __init__(self,
window,
items,
sids,
cap_multiple=2,
dtype=np.float64,
initial_dates=None):
self._pos = window
self._window = window
self.items = _ensure_index(items)
self.minor_axis = _ensure_index(sids)
self.cap_multiple = cap_multiple
self.dtype = dtype
if initial_dates is None:
self.date_buf = np.empty(self.cap, dtype='M8[ns]') * pd.NaT
elif len(initial_dates) != window:
raise ValueError('initial_dates must be of length window')
else:
self.date_buf = np.hstack(
(
initial_dates,
np.empty(
window * (cap_multiple - 1),
dtype='datetime64[ns]',
),
),
)
self.buffer = self._create_buffer()
@property
def cap(self):
return self.cap_multiple * self._window
@property
def _start_index(self):
return self._pos - self._window
@property
def start_date(self):
return self.date_buf[self._start_index]
def oldest_frame(self, raw=False):
"""
Get the oldest frame in the panel.
"""
if raw:
return self.buffer.values[:, self._start_index, :]
return self.buffer.iloc[:, self._start_index, :]
def set_minor_axis(self, minor_axis):
self.minor_axis = _ensure_index(minor_axis)
self.buffer = self.buffer.reindex(minor_axis=self.minor_axis)
def set_items(self, items):
self.items = _ensure_index(items)
self.buffer = self.buffer.reindex(items=self.items)
def _create_buffer(self):
panel = pd.Panel(
items=self.items,
minor_axis=self.minor_axis,
major_axis=range(self.cap),
dtype=self.dtype,
)
return panel
def extend_back(self, missing_dts):
"""
Resizes the buffer to hold a new window with a new cap_multiple.
If cap_multiple is None, then the old cap_multiple is used.
"""
delta = len(missing_dts)
if not delta:
raise ValueError(
'missing_dts must be a non-empty index',
)
self._window += delta
self._pos += delta
self.date_buf = self.date_buf.copy()
self.date_buf.resize(self.cap)
self.date_buf = np.roll(self.date_buf, delta)
old_vals = self.buffer.values
shape = old_vals.shape
nan_arr = np.empty((shape[0], delta, shape[2]))
nan_arr.fill(np.nan)
new_vals = np.column_stack(
(nan_arr,
old_vals,
np.empty((shape[0], delta * (self.cap_multiple - 1), shape[2]))),
)
self.buffer = pd.Panel(
data=new_vals,
items=self.items,
minor_axis=self.minor_axis,
major_axis=np.arange(self.cap),
dtype=self.dtype,
)
# Fill the delta with the dates we calculated.
where = slice(self._start_index, self._start_index + delta)
self.date_buf[where] = missing_dts
def add_frame(self, tick, frame, minor_axis=None, items=None):
"""
"""
if self._pos == self.cap:
self._roll_data()
values = frame
if isinstance(frame, pd.DataFrame):
values = frame.values
self.buffer.values[:, self._pos, :] = values.astype(self.dtype)
self.date_buf[self._pos] = tick
self._pos += 1
def get_current(self, item=None, raw=False, start=None, end=None):
"""
Get a Panel that is the current data in view. It is not safe to persist
these objects because internal data might change
"""
item_indexer = slice(None)
if item:
item_indexer = self.items.get_loc(item)
start_index = self._start_index
end_index = self._pos
# get inital date window
where = slice(start_index, end_index)
current_dates = self.date_buf[where]
def convert_datelike_to_long(dt):
if isinstance(dt, pd.Timestamp):
return dt.asm8
if isinstance(dt, datetime.datetime):
return np.datetime64(dt)
return dt
# constrict further by date
if start:
start = convert_datelike_to_long(start)
start_index += current_dates.searchsorted(start)
if end:
end = convert_datelike_to_long(end)
_end = current_dates.searchsorted(end, 'right')
end_index -= len(current_dates) - _end
where = slice(start_index, end_index)
values = self.buffer.values[item_indexer, where, :]
current_dates = self.date_buf[where]
if raw:
# return copy so we can change it without side effects here
return values.copy()
major_axis = pd.DatetimeIndex(deepcopy(current_dates), tz='utc')
if values.ndim == 3:
return pd.Panel(values, self.items, major_axis, self.minor_axis,
dtype=self.dtype)
elif values.ndim == 2:
return pd.DataFrame(values, major_axis, self.minor_axis,
dtype=self.dtype)
def set_current(self, panel):
"""
Set the values stored in our current in-view data to be values of the
passed panel. The passed panel must have the same indices as the panel
that would be returned by self.get_current.
"""
where = slice(self._start_index, self._pos)
self.buffer.values[:, where, :] = panel.values
def current_dates(self):
where = slice(self._start_index, self._pos)
return pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
def _roll_data(self):
"""
Roll window worth of data up to position zero.
Save the effort of having to expensively roll at each iteration
"""
self.buffer.values[:, :self._window, :] = \
self.buffer.values[:, -self._window:, :]
self.date_buf[:self._window] = self.date_buf[-self._window:]
self._pos = self._window
@property
def window_length(self):
return self._window
class MutableIndexRollingPanel(object):
"""
A version of RollingPanel that exists for backwards compatibility with
batch_transform. This is a copy to allow behavior of RollingPanel to drift
away from this without breaking this class.
This code should be considered frozen, and should not be used in the
future. Instead, see RollingPanel.
"""
def __init__(self, window, items, sids, cap_multiple=2, dtype=np.float64):
self._pos = 0
self._window = window
self.items = _ensure_index(items)
self.minor_axis = _ensure_index(sids)
self.cap_multiple = cap_multiple
self.cap = cap_multiple * window
self.dtype = dtype
self.date_buf = np.empty(self.cap, dtype='M8[ns]')
self.buffer = self._create_buffer()
def _oldest_frame_idx(self):
return max(self._pos - self._window, 0)
def oldest_frame(self, raw=False):
"""
Get the oldest frame in the panel.
"""
if raw:
return self.buffer.values[:, self._oldest_frame_idx(), :]
return self.buffer.iloc[:, self._oldest_frame_idx(), :]
def set_sids(self, sids):
self.minor_axis = _ensure_index(sids)
self.buffer = self.buffer.reindex(minor_axis=self.minor_axis)
def _create_buffer(self):
panel = pd.Panel(
items=self.items,
minor_axis=self.minor_axis,
major_axis=range(self.cap),
dtype=self.dtype,
)
return panel
def get_current(self):
"""
Get a Panel that is the current data in view. It is not safe to persist
these objects because internal data might change
"""
where = slice(self._oldest_frame_idx(), self._pos)
major_axis = pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
return pd.Panel(self.buffer.values[:, where, :], self.items,
major_axis, self.minor_axis, dtype=self.dtype)
def set_current(self, panel):
"""
Set the values stored in our current in-view data to be values of the
passed panel. The passed panel must have the same indices as the panel
that would be returned by self.get_current.
"""
where = slice(self._oldest_frame_idx(), self._pos)
self.buffer.values[:, where, :] = panel.values
def current_dates(self):
where = slice(self._oldest_frame_idx(), self._pos)
return pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
def _roll_data(self):
"""
Roll window worth of data up to position zero.
Save the effort of having to expensively roll at each iteration
"""
self.buffer.values[:, :self._window, :] = \
self.buffer.values[:, -self._window:, :]
self.date_buf[:self._window] = self.date_buf[-self._window:]
self._pos = self._window
def add_frame(self, tick, frame, minor_axis=None, items=None):
"""
"""
if self._pos == self.cap:
self._roll_data()
if isinstance(frame, pd.DataFrame):
minor_axis = frame.columns
items = frame.index
if set(minor_axis).difference(set(self.minor_axis)) or \
set(items).difference(set(self.items)):
self._update_buffer(frame)
vals = frame.T.astype(self.dtype)
self.buffer.loc[:, self._pos, :] = vals
self.date_buf[self._pos] = tick
self._pos += 1
def _update_buffer(self, frame):
# Get current frame as we only need to care about the data that is in
# the active window
old_buffer = self.get_current()
if self._pos >= self._window:
# Don't count the last major_axis entry if we're past our window,
# since it's about to roll off the end of the panel.
old_buffer = old_buffer.iloc[:, 1:, :]
nans = pd.isnull(old_buffer)
# Find minor_axes that have only nans
# Note that minor is axis 2
non_nan_cols = set(old_buffer.minor_axis[~np.all(nans, axis=(0, 1))])
# Determine new columns to be added
new_cols = set(frame.columns).difference(non_nan_cols)
# Update internal minor axis
self.minor_axis = _ensure_index(new_cols.union(non_nan_cols))
# Same for items (fields)
# Find items axes that have only nans
# Note that items is axis 0
non_nan_items = set(old_buffer.items[~np.all(nans, axis=(1, 2))])
new_items = set(frame.index).difference(non_nan_items)
self.items = _ensure_index(new_items.union(non_nan_items))
# :NOTE:
# There is a simpler and 10x faster way to do this:
#
# Reindex buffer to update axes (automatically adds nans)
# self.buffer = self.buffer.reindex(items=self.items,
# major_axis=np.arange(self.cap),
# minor_axis=self.minor_axis)
#
# However, pandas==0.12.0, for which we remain backwards compatible,
# has a bug in .reindex() that this triggers. Using .update() as before
# seems to work fine.
new_buffer = self._create_buffer()
new_buffer.update(
self.buffer.loc[non_nan_items, :, non_nan_cols])
self.buffer = new_buffer | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/data.py | data.py |
from functools import reduce
from operator import itemgetter
from pprint import pformat
from six import viewkeys, iteritems
from six.moves import map, zip
from toolz import curry, flip
from .sentinel import sentinel
@curry
def apply(f, *args, **kwargs):
"""Apply a function to arguments.
Parameters
----------
f : callable
The function to call.
*args, **kwargs
**kwargs
Arguments to feed to the callable.
Returns
-------
a : any
The result of ``f(*args, **kwargs)``
Examples
--------
>>> from toolz.curried.operator import add, sub
>>> fs = add(1), sub(1)
>>> tuple(map(apply, fs, (1, 2)))
(2, -1)
Class decorator
>>> instance = apply
>>> @instance
... class obj:
... def f(self):
... return 'f'
...
>>> obj.f()
'f'
>>> issubclass(obj, object)
Traceback (most recent call last):
...
TypeError: issubclass() arg 1 must be a class
>>> isinstance(obj, type)
False
See Also
--------
unpack_apply
mapply
"""
return f(*args, **kwargs)
# Alias for use as a class decorator.
instance = apply
def mapall(funcs, seq):
"""
Parameters
----------
funcs : iterable[function]
Sequence of functions to map over `seq`.
seq : iterable
Sequence over which to map funcs.
Yields
------
elem : object
Concatenated result of mapping each ``func`` over ``seq``.
Examples
--------
>>> list(mapall([lambda x: x + 1, lambda x: x - 1], [1, 2, 3]))
[2, 3, 4, 0, 1, 2]
"""
for func in funcs:
for elem in seq:
yield func(elem)
def same(*values):
"""
Check if all values in a sequence are equal.
Returns True on empty sequences.
Examples
--------
>>> same(1, 1, 1, 1)
True
>>> same(1, 2, 1)
False
>>> same()
True
"""
if not values:
return True
first, rest = values[0], values[1:]
return all(value == first for value in rest)
def _format_unequal_keys(dicts):
return pformat([sorted(d.keys()) for d in dicts])
def dzip_exact(*dicts):
"""
Parameters
----------
*dicts : iterable[dict]
A sequence of dicts all sharing the same keys.
Returns
-------
zipped : dict
A dict whose keys are the union of all keys in *dicts, and whose values
are tuples of length len(dicts) containing the result of looking up
each key in each dict.
Raises
------
ValueError
If dicts don't all have the same keys.
Examples
--------
>>> result = dzip_exact({'a': 1, 'b': 2}, {'a': 3, 'b': 4})
>>> result == {'a': (1, 3), 'b': (2, 4)}
True
"""
if not same(*map(viewkeys, dicts)):
raise ValueError(
"dict keys not all equal:\n\n%s" % _format_unequal_keys(dicts)
)
return {k: tuple(d[k] for d in dicts) for k in dicts[0]}
def _gen_unzip(it, elem_len):
"""Helper for unzip which checks the lengths of each element in it.
Parameters
----------
it : iterable[tuple]
An iterable of tuples. ``unzip`` should map ensure that these are
already tuples.
elem_len : int or None
The expected element length. If this is None it is infered from the
length of the first element.
Yields
------
elem : tuple
Each element of ``it``.
Raises
------
ValueError
Raised when the lengths do not match the ``elem_len``.
"""
try:
elem = next(it)
except:
# in python 3.7 this raises a RuntimeError: generator raised StopIteration
return
first_elem_len = len(elem)
if elem_len is not None and elem_len != first_elem_len:
raise ValueError(
'element at index 0 was length %d, expected %d' % (
first_elem_len,
elem_len,
)
)
else:
elem_len = first_elem_len
yield elem
for n, elem in enumerate(it, 1):
if len(elem) != elem_len:
raise ValueError(
'element at index %d was length %d, expected %d' % (
n,
len(elem),
elem_len,
),
)
yield elem
def unzip(seq, elem_len=None):
"""Unzip a length n sequence of length m sequences into m seperate length
n sequences.
Parameters
----------
seq : iterable[iterable]
The sequence to unzip.
elem_len : int, optional
The expected length of each element of ``seq``. If not provided this
will be infered from the length of the first element of ``seq``. This
can be used to ensure that code like: ``a, b = unzip(seq)`` does not
fail even when ``seq`` is empty.
Returns
-------
seqs : iterable[iterable]
The new sequences pulled out of the first iterable.
Raises
------
ValueError
Raised when ``seq`` is empty and ``elem_len`` is not provided.
Raised when elements of ``seq`` do not match the given ``elem_len`` or
the length of the first element of ``seq``.
Examples
--------
>>> seq = [('a', 1), ('b', 2), ('c', 3)]
>>> cs, ns = unzip(seq)
>>> cs
('a', 'b', 'c')
>>> ns
(1, 2, 3)
# checks that the elements are the same length
>>> seq = [('a', 1), ('b', 2), ('c', 3, 'extra')]
>>> cs, ns = unzip(seq)
Traceback (most recent call last):
...
ValueError: element at index 2 was length 3, expected 2
# allows an explicit element length instead of infering
>>> seq = [('a', 1, 'extra'), ('b', 2), ('c', 3)]
>>> cs, ns = unzip(seq, 2)
Traceback (most recent call last):
...
ValueError: element at index 0 was length 3, expected 2
# handles empty sequences when a length is given
>>> cs, ns = unzip([], elem_len=2)
>>> cs == ns == ()
True
Notes
-----
This function will force ``seq`` to completion.
"""
ret = tuple(zip(*_gen_unzip(map(tuple, seq), elem_len)))
if ret:
return ret
if elem_len is None:
raise ValueError("cannot unzip empty sequence without 'elem_len'")
return ((),) * elem_len
_no_default = sentinel('_no_default')
def getattrs(value, attrs, default=_no_default):
"""
Perform a chained application of ``getattr`` on ``value`` with the values
in ``attrs``.
If ``default`` is supplied, return it if any of the attribute lookups fail.
Parameters
----------
value : object
Root of the lookup chain.
attrs : iterable[str]
Sequence of attributes to look up.
default : object, optional
Value to return if any of the lookups fail.
Returns
-------
result : object
Result of the lookup sequence.
Examples
--------
>>> class EmptyObject(object):
... pass
...
>>> obj = EmptyObject()
>>> obj.foo = EmptyObject()
>>> obj.foo.bar = "value"
>>> getattrs(obj, ('foo', 'bar'))
'value'
>>> getattrs(obj, ('foo', 'buzz'))
Traceback (most recent call last):
...
AttributeError: 'EmptyObject' object has no attribute 'buzz'
>>> getattrs(obj, ('foo', 'buzz'), 'default')
'default'
"""
try:
for attr in attrs:
value = getattr(value, attr)
except AttributeError:
if default is _no_default:
raise
value = default
return value
@curry
def set_attribute(name, value):
"""
Decorator factory for setting attributes on a function.
Doesn't change the behavior of the wrapped function.
Examples
--------
>>> @set_attribute('__name__', 'foo')
... def bar():
... return 3
...
>>> bar()
3
>>> bar.__name__
'foo'
"""
def decorator(f):
setattr(f, name, value)
return f
return decorator
# Decorators for setting the __name__ and __doc__ properties of a decorated
# function.
# Example:
with_name = set_attribute('__name__')
with_doc = set_attribute('__doc__')
def foldr(f, seq, default=_no_default):
"""Fold a function over a sequence with right associativity.
Parameters
----------
f : callable[any, any]
The function to reduce the sequence with.
The first argument will be the element of the sequence; the second
argument will be the accumulator.
seq : iterable[any]
The sequence to reduce.
default : any, optional
The starting value to reduce with. If not provided, the sequence
cannot be empty, and the last value of the sequence will be used.
Returns
-------
folded : any
The folded value.
Notes
-----
This functions works by reducing the list in a right associative way.
For example, imagine we are folding with ``operator.add`` or ``+``:
.. code-block:: python
foldr(add, seq) -> seq[0] + (seq[1] + (seq[2] + (...seq[-1], default)))
In the more general case with an arbitrary function, ``foldr`` will expand
like so:
.. code-block:: python
foldr(f, seq) -> f(seq[0], f(seq[1], f(seq[2], ...f(seq[-1], default))))
For a more in depth discussion of left and right folds, see:
`https://en.wikipedia.org/wiki/Fold_(higher-order_function)`_
The images in that page are very good for showing the differences between
``foldr`` and ``foldl`` (``reduce``).
.. note::
For performance reasons is is best to pass a strict (non-lazy) sequence,
for example, a list.
See Also
--------
:func:`functools.reduce`
:func:`sum`
"""
return reduce(
flip(f),
reversed(seq),
*(default,) if default is not _no_default else ()
)
def invert(d):
"""
Invert a dictionary into a dictionary of sets.
>>> invert({'a': 1, 'b': 2, 'c': 1}) # doctest: +SKIP
{1: {'a', 'c'}, 2: {'b'}}
"""
out = {}
for k, v in iteritems(d):
try:
out[v].add(k)
except KeyError:
out[v] = {k}
return out
def keysorted(d):
"""Get the items from a dict, sorted by key.
Example
-------
>>> keysorted({'c': 1, 'b': 2, 'a': 3})
[('a', 3), ('b', 2), ('c', 1)]
"""
return sorted(iteritems(d), key=itemgetter(0)) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/functional.py | functional.py |
from six.moves import map as imap
from toolz import compose, identity
class ApplyAsyncResult(object):
"""An object that boxes results for calls to
:meth:`~zipline.utils.pool.SequentialPool.apply_async`.
Parameters
----------
value : any
The result of calling the function, or any exception that was raised.
successful : bool
If ``True``, ``value`` is the return value of the function.
If ``False``, ``value`` is the exception that was raised when calling
the functions.
"""
def __init__(self, value, successful):
self._value = value
self._successful = successful
def successful(self):
"""Did the function execute without raising an exception?
"""
return self._successful
def get(self):
"""Return the result of calling the function or reraise any exceptions
that were raised.
"""
if not self._successful:
raise self._value
return self._value
def ready(self):
"""Has the function finished executing.
Notes
-----
In the :class:`~zipline.utils.pool.SequentialPool` case, this is always
``True``.
"""
return True
def wait(self):
"""Wait until the function is finished executing.
Notes
-----
In the :class:`~zipline.utils.pool.SequentialPool` case, this is a nop
because the function is computed eagerly in the same thread as the
call to :meth:`~zipline.utils.pool.SequentialPool.apply_async`.
"""
pass
class SequentialPool(object):
"""A dummy pool object that iterates sequentially in a single thread.
Methods
-------
map(f: callable[A, B], iterable: iterable[A]) -> list[B]
Apply a function to each of the elements of ``iterable``.
imap(f: callable[A, B], iterable: iterable[A]) -> iterable[B]
Lazily apply a function to each of the elements of ``iterable``.
imap_unordered(f: callable[A, B], iterable: iterable[A]) -> iterable[B]
Lazily apply a function to each of the elements of ``iterable`` but
yield values as they become available. The resulting iterable is
unordered.
Notes
-----
This object is useful for testing to mock out the ``Pool`` interface
provided by gevent or multiprocessing.
See Also
--------
:class:`multiprocessing.Pool`
"""
map = staticmethod(compose(list, imap))
imap = imap_unordered = staticmethod(imap)
@staticmethod
def apply_async(f, args=(), kwargs=None, callback=None):
"""Apply a function but emulate the API of an asynchronous call.
Parameters
----------
f : callable
The function to call.
args : tuple, optional
The positional arguments.
kwargs : dict, optional
The keyword arguments.
Returns
-------
future : ApplyAsyncResult
The result of calling the function boxed in a future-like api.
Notes
-----
This calls the function eagerly but wraps it so that ``SequentialPool``
can be used where a :class:`multiprocessing.Pool` or
:class:`gevent.pool.Pool` would be used.
"""
try:
value = (identity if callback is None else callback)(
f(*args, **kwargs or {}),
)
successful = True
except Exception as e:
value = e
successful = False
return ApplyAsyncResult(value, successful)
@staticmethod
def apply(f, args=(), kwargs=None):
"""Apply a function.
Parameters
----------
f : callable
The function to call.
args : tuple, optional
The positional arguments.
kwargs : dict, optional
The keyword arguments.
Returns
-------
result : any
f(*args, **kwargs)
"""
return f(*args, **kwargs or {})
@staticmethod
def close():
pass
@staticmethod
def join():
pass | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/pool.py | pool.py |
import re
from six import iteritems
from textwrap import dedent
from toolz import curry
PIPELINE_DOWNSAMPLING_FREQUENCY_DOC = dedent(
"""\
frequency : {'year_start', 'quarter_start', 'month_start', 'week_start'}
A string indicating desired sampling dates:
* 'year_start' -> first trading day of each year
* 'quarter_start' -> first trading day of January, April, July, October
* 'month_start' -> first trading day of each month
* 'week_start' -> first trading_day of each week
"""
)
PIPELINE_ALIAS_NAME_DOC = dedent(
"""\
name : str
The name to alias this term as.
""",
)
def pad_lines_after_first(prefix, s):
"""Apply a prefix to each line in s after the first."""
return ('\n' + prefix).join(s.splitlines())
def format_docstring(owner_name, docstring, formatters):
"""
Template ``formatters`` into ``docstring``.
Parameters
----------
owner_name : str
The name of the function or class whose docstring is being templated.
Only used for error messages.
docstring : str
The docstring to template.
formatters : dict[str -> str]
Parameters for a a str.format() call on ``docstring``.
Multi-line values in ``formatters`` will have leading whitespace padded
to match the leading whitespace of the substitution string.
"""
# Build a dict of parameters to a vanilla format() call by searching for
# each entry in **formatters and applying any leading whitespace to each
# line in the desired substitution.
format_params = {}
for target, doc_for_target in iteritems(formatters):
# Search for '{name}', with optional leading whitespace.
regex = re.compile(r'^(\s*)' + '({' + target + '})$', re.MULTILINE)
matches = regex.findall(docstring)
if not matches:
raise ValueError(
"Couldn't find template for parameter {!r} in docstring "
"for {}."
"\nParameter name must be alone on a line surrounded by "
"braces.".format(target, owner_name),
)
elif len(matches) > 1:
raise ValueError(
"Couldn't found multiple templates for parameter {!r}"
"in docstring for {}."
"\nParameter should only appear once.".format(
target, owner_name
)
)
(leading_whitespace, _) = matches[0]
format_params[target] = pad_lines_after_first(
leading_whitespace,
doc_for_target,
)
return docstring.format(**format_params)
def templated_docstring(**docs):
"""
Decorator allowing the use of templated docstrings.
Examples
--------
>>> @templated_docstring(foo='bar')
... def my_func(self, foo):
... '''{foo}'''
...
>>> my_func.__doc__
'bar'
"""
def decorator(f):
f.__doc__ = format_docstring(f.__name__, f.__doc__, docs)
return f
return decorator
@curry
def copydoc(from_, to):
"""Copies the docstring from one function to another.
Parameters
----------
from_ : any
The object to copy the docstring from.
to : any
The object to copy the docstring to.
Returns
-------
to : any
``to`` with the docstring from ``from_``
"""
to.__doc__ = from_.__doc__
return to | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/sharedoc.py | sharedoc.py |
from abc import ABCMeta, abstractmethod
from six import with_metaclass, iteritems
# Consistent error to be thrown in various cases regarding overriding
# `final` attributes.
_type_error = TypeError('Cannot override final attribute')
def bases_mro(bases):
"""
Yield classes in the order that methods should be looked up from the
base classes of an object.
"""
for base in bases:
for class_ in base.__mro__:
yield class_
def is_final(name, mro):
"""
Checks if `name` is a `final` object in the given `mro`.
We need to check the mro because we need to directly go into the __dict__
of the classes. Because `final` objects are descriptor, we need to grab
them _BEFORE_ the `__call__` is invoked.
"""
return any(isinstance(getattr(c, '__dict__', {}).get(name), final)
for c in bases_mro(mro))
class FinalMeta(type):
"""A metaclass template for classes the want to prevent subclassess from
overriding a some methods or attributes.
"""
def __new__(mcls, name, bases, dict_):
for k, v in iteritems(dict_):
if is_final(k, bases):
raise _type_error
setattr_ = dict_.get('__setattr__')
if setattr_ is None:
# No `__setattr__` was explicitly defined, look up the super
# class's. `bases[0]` will have a `__setattr__` because
# `object` does so we don't need to worry about the mro.
setattr_ = bases[0].__setattr__
if not is_final('__setattr__', bases) \
and not isinstance(setattr_, final):
# implicitly make the `__setattr__` a `final` object so that
# users cannot just avoid the descriptor protocol.
dict_['__setattr__'] = final(setattr_)
return super(FinalMeta, mcls).__new__(mcls, name, bases, dict_)
def __setattr__(self, name, value):
"""This stops the `final` attributes from being reassigned on the
class object.
"""
if is_final(name, self.__mro__):
raise _type_error
super(FinalMeta, self).__setattr__(name, value)
class final(with_metaclass(ABCMeta)):
"""
An attribute that cannot be overridden.
This is like the final modifier in Java.
Example usage:
>>> from six import with_metaclass
>>> class C(with_metaclass(FinalMeta, object)):
... @final
... def f(self):
... return 'value'
...
This constructs a class with final method `f`. This cannot be overridden
on the class object or on any instance. You cannot override this by
subclassing `C`; attempting to do so will raise a `TypeError` at class
construction time.
"""
def __new__(cls, attr):
# Decide if this is a method wrapper or an attribute wrapper.
# We are going to cache the `callable` check by creating a
# method or attribute wrapper.
if hasattr(attr, '__get__'):
return object.__new__(finaldescriptor)
else:
return object.__new__(finalvalue)
def __init__(self, attr):
self._attr = attr
def __set__(self, instance, value):
"""
`final` objects cannot be reassigned. This is the most import concept
about `final`s.
Unlike a `property` object, this will raise a `TypeError` when you
attempt to reassign it.
"""
raise _type_error
@abstractmethod
def __get__(self, instance, owner):
raise NotImplementedError('__get__')
class finalvalue(final):
"""
A wrapper for a non-descriptor attribute.
"""
def __get__(self, instance, owner):
return self._attr
class finaldescriptor(final):
"""
A final wrapper around a descriptor.
"""
def __get__(self, instance, owner):
return self._attr.__get__(instance, owner) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/final.py | final.py |
import warnings
from datetime import datetime
from os import listdir
import os.path
import pandas as pd
import pytz
import zipline
from zipline.errors import SymbolNotFound
from zipline.finance.asset_restrictions import SecurityListRestrictions
from zipline.zipline_warnings import ZiplineDeprecationWarning
DATE_FORMAT = "%Y%m%d"
zipline_dir = os.path.dirname(zipline.__file__)
SECURITY_LISTS_DIR = os.path.join(zipline_dir, 'resources', 'security_lists')
class SecurityList(object):
def __init__(self, data, current_date_func, asset_finder):
"""
data: a nested dictionary:
knowledge_date -> lookup_date ->
{add: [symbol list], 'delete': []}, delete: [symbol list]}
current_date_func: function taking no parameters, returning
current datetime
"""
self.data = data
self._cache = {}
self._knowledge_dates = self.make_knowledge_dates(self.data)
self.current_date = current_date_func
self.count = 0
self._current_set = set()
self.asset_finder = asset_finder
def make_knowledge_dates(self, data):
knowledge_dates = sorted(
[pd.Timestamp(k) for k in data.keys()])
return knowledge_dates
def __iter__(self):
warnings.warn(
'Iterating over security_lists is deprecated. Use '
'`for sid in <security_list>.current_securities(dt)` instead.',
category=ZiplineDeprecationWarning,
stacklevel=2
)
return iter(self.current_securities(self.current_date()))
def __contains__(self, item):
warnings.warn(
'Evaluating inclusion in security_lists is deprecated. Use '
'`sid in <security_list>.current_securities(dt)` instead.',
category=ZiplineDeprecationWarning,
stacklevel=2
)
return item in self.current_securities(self.current_date())
def current_securities(self, dt):
for kd in self._knowledge_dates:
if dt < kd:
break
if kd in self._cache:
self._current_set = self._cache[kd]
continue
for effective_date, changes in iter(self.data[kd].items()):
self.update_current(
effective_date,
changes['add'],
self._current_set.add
)
self.update_current(
effective_date,
changes['delete'],
self._current_set.remove
)
self._cache[kd] = self._current_set
return self._current_set
def update_current(self, effective_date, symbols, change_func):
for symbol in symbols:
try:
asset = self.asset_finder.lookup_symbol(
symbol,
as_of_date=effective_date
)
# Pass if no Asset exists for the symbol
except SymbolNotFound:
continue
change_func(asset.sid)
class SecurityListSet(object):
# provide a cut point to substitute other security
# list implementations.
security_list_type = SecurityList
def __init__(self, current_date_func, asset_finder):
self.current_date_func = current_date_func
self.asset_finder = asset_finder
self._leveraged_etf = None
@property
def leveraged_etf_list(self):
if self._leveraged_etf is None:
self._leveraged_etf = self.security_list_type(
load_from_directory('leveraged_etf_list'),
self.current_date_func,
asset_finder=self.asset_finder
)
return self._leveraged_etf
@property
def restrict_leveraged_etfs(self):
return SecurityListRestrictions(self.leveraged_etf_list)
def load_from_directory(list_name):
"""
To resolve the symbol in the LEVERAGED_ETF list,
the date on which the symbol was in effect is needed.
Furthermore, to maintain a point in time record of our own maintenance
of the restricted list, we need a knowledge date. Thus, restricted lists
are dictionaries of datetime->symbol lists.
new symbols should be entered as a new knowledge date entry.
This method assumes a directory structure of:
SECURITY_LISTS_DIR/listname/knowledge_date/lookup_date/add.txt
SECURITY_LISTS_DIR/listname/knowledge_date/lookup_date/delete.txt
The return value is a dictionary with:
knowledge_date -> lookup_date ->
{add: [symbol list], 'delete': [symbol list]}
"""
data = {}
dir_path = os.path.join(SECURITY_LISTS_DIR, list_name)
for kd_name in listdir(dir_path):
kd = datetime.strptime(kd_name, DATE_FORMAT).replace(
tzinfo=pytz.utc)
data[kd] = {}
kd_path = os.path.join(dir_path, kd_name)
for ld_name in listdir(kd_path):
ld = datetime.strptime(ld_name, DATE_FORMAT).replace(
tzinfo=pytz.utc)
data[kd][ld] = {}
ld_path = os.path.join(kd_path, ld_name)
for fname in listdir(ld_path):
fpath = os.path.join(ld_path, fname)
with open(fpath) as f:
symbols = f.read().splitlines()
data[kd][ld][fname] = symbols
return data | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/security_list.py | security_list.py |
import pandas as pd
import pytz
# import warnings
from datetime import datetime
from dateutil import rrule
from functools import partial
# from zipline.zipline_warnings import ZiplineDeprecationWarning
# IMPORTANT: This module is deprecated and is only here for temporary backwards
# compatibility. Look at the `trading-calendars`
# module, as well as the calendar definitions in `trading_calendars`.
# TODO: The new calendar API is currently in flux, so the deprecation
# warning for this module is currently disabled. Re-enable once
# the new API is stabilized.
#
# warnings.warn(
# "The `tradingcalendar` module is deprecated. See the "
# "`trading-calendars` module, as well as the "
# "calendar definitions in `trading-calendars`.",
# category=ZiplineDeprecationWarning,
# stacklevel=1,
# )
start = pd.Timestamp('1990-01-01', tz='UTC')
end_base = pd.Timestamp('today', tz='UTC')
# Give an aggressive buffer for logic that needs to use the next trading
# day or minute.
end = end_base + pd.Timedelta(days=365)
def canonicalize_datetime(dt):
# Strip out any HHMMSS or timezone info in the user's datetime, so that
# all the datetimes we return will be 00:00:00 UTC.
return datetime(dt.year, dt.month, dt.day, tzinfo=pytz.utc)
def get_non_trading_days(start, end):
non_trading_rules = []
start = canonicalize_datetime(start)
end = canonicalize_datetime(end)
weekends = rrule.rrule(
rrule.YEARLY,
byweekday=(rrule.SA, rrule.SU),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(weekends)
new_years = rrule.rrule(
rrule.MONTHLY,
byyearday=1,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(new_years)
new_years_sunday = rrule.rrule(
rrule.MONTHLY,
byyearday=2,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(new_years_sunday)
mlk_day = rrule.rrule(
rrule.MONTHLY,
bymonth=1,
byweekday=(rrule.MO(+3)),
cache=True,
dtstart=datetime(1998, 1, 1, tzinfo=pytz.utc),
until=end
)
non_trading_rules.append(mlk_day)
presidents_day = rrule.rrule(
rrule.MONTHLY,
bymonth=2,
byweekday=(rrule.MO(3)),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(presidents_day)
good_friday = rrule.rrule(
rrule.DAILY,
byeaster=-2,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(good_friday)
memorial_day = rrule.rrule(
rrule.MONTHLY,
bymonth=5,
byweekday=(rrule.MO(-1)),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(memorial_day)
july_4th = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=4,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(july_4th)
july_4th_sunday = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=5,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(july_4th_sunday)
july_4th_saturday = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=3,
byweekday=rrule.FR,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(july_4th_saturday)
labor_day = rrule.rrule(
rrule.MONTHLY,
bymonth=9,
byweekday=(rrule.MO(1)),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(labor_day)
thanksgiving = rrule.rrule(
rrule.MONTHLY,
bymonth=11,
byweekday=(rrule.TH(4)),
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(thanksgiving)
christmas = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=25,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(christmas)
christmas_sunday = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=26,
byweekday=rrule.MO,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(christmas_sunday)
# If Christmas is a Saturday then 24th, a Friday is observed.
christmas_saturday = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=24,
byweekday=rrule.FR,
cache=True,
dtstart=start,
until=end
)
non_trading_rules.append(christmas_saturday)
non_trading_ruleset = rrule.rruleset()
for rule in non_trading_rules:
non_trading_ruleset.rrule(rule)
non_trading_days = non_trading_ruleset.between(start, end, inc=True)
# Add September 11th closings
# https://en.wikipedia.org/wiki/Aftermath_of_the_September_11_attacks
# Due to the terrorist attacks, the stock market did not open on 9/11/2001
# It did not open again until 9/17/2001.
#
# September 2001
# Su Mo Tu We Th Fr Sa
# 1
# 2 3 4 5 6 7 8
# 9 10 11 12 13 14 15
# 16 17 18 19 20 21 22
# 23 24 25 26 27 28 29
# 30
for day_num in range(11, 17):
non_trading_days.append(
datetime(2001, 9, day_num, tzinfo=pytz.utc))
# Add closings due to Hurricane Sandy in 2012
# https://en.wikipedia.org/wiki/Hurricane_sandy
#
# The stock exchange was closed due to Hurricane Sandy's
# impact on New York.
# It closed on 10/29 and 10/30, reopening on 10/31
# October 2012
# Su Mo Tu We Th Fr Sa
# 1 2 3 4 5 6
# 7 8 9 10 11 12 13
# 14 15 16 17 18 19 20
# 21 22 23 24 25 26 27
# 28 29 30 31
for day_num in range(29, 31):
non_trading_days.append(
datetime(2012, 10, day_num, tzinfo=pytz.utc))
# Misc closings from NYSE listing.
# http://www.nyse.com/pdfs/closings.pdf
#
# National Days of Mourning
# - President Richard Nixon
non_trading_days.append(datetime(1994, 4, 27, tzinfo=pytz.utc))
# - President Ronald W. Reagan - June 11, 2004
non_trading_days.append(datetime(2004, 6, 11, tzinfo=pytz.utc))
# - President Gerald R. Ford - Jan 2, 2007
non_trading_days.append(datetime(2007, 1, 2, tzinfo=pytz.utc))
non_trading_days.sort()
return pd.DatetimeIndex(non_trading_days)
non_trading_days = get_non_trading_days(start, end)
trading_day = pd.tseries.offsets.CDay(holidays=non_trading_days)
def get_trading_days(start, end, trading_day=trading_day):
return pd.date_range(start=start.date(),
end=end.date(),
freq=trading_day).tz_localize('UTC')
trading_days = get_trading_days(start, end)
def get_early_closes(start, end):
# 1:00 PM close rules based on
# https://quant.stackexchange.com/questions/4083/nyse-early-close-rules-july-4th-and-dec-25th # noqa
# and verified against http://www.nyse.com/pdfs/closings.pdf
# These rules are valid starting in 1993
start = canonicalize_datetime(start)
end = canonicalize_datetime(end)
start = max(start, datetime(1993, 1, 1, tzinfo=pytz.utc))
end = max(end, datetime(1993, 1, 1, tzinfo=pytz.utc))
# Not included here are early closes prior to 1993
# or unplanned early closes
early_close_rules = []
day_after_thanksgiving = rrule.rrule(
rrule.MONTHLY,
bymonth=11,
# 4th Friday isn't correct if month starts on Friday, so restrict to
# day range:
byweekday=(rrule.FR),
bymonthday=range(23, 30),
cache=True,
dtstart=start,
until=end
)
early_close_rules.append(day_after_thanksgiving)
christmas_eve = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=24,
byweekday=(rrule.MO, rrule.TU, rrule.WE, rrule.TH),
cache=True,
dtstart=start,
until=end
)
early_close_rules.append(christmas_eve)
friday_after_christmas = rrule.rrule(
rrule.MONTHLY,
bymonth=12,
bymonthday=26,
byweekday=rrule.FR,
cache=True,
dtstart=start,
# valid 1993-2007
until=min(end, datetime(2007, 12, 31, tzinfo=pytz.utc))
)
early_close_rules.append(friday_after_christmas)
day_before_independence_day = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=3,
byweekday=(rrule.MO, rrule.TU, rrule.TH),
cache=True,
dtstart=start,
until=end
)
early_close_rules.append(day_before_independence_day)
day_after_independence_day = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=5,
byweekday=rrule.FR,
cache=True,
dtstart=start,
# starting in 2013: wednesday before independence day
until=min(end, datetime(2012, 12, 31, tzinfo=pytz.utc))
)
early_close_rules.append(day_after_independence_day)
wednesday_before_independence_day = rrule.rrule(
rrule.MONTHLY,
bymonth=7,
bymonthday=3,
byweekday=rrule.WE,
cache=True,
# starting in 2013
dtstart=max(start, datetime(2013, 1, 1, tzinfo=pytz.utc)),
until=max(end, datetime(2013, 1, 1, tzinfo=pytz.utc))
)
early_close_rules.append(wednesday_before_independence_day)
early_close_ruleset = rrule.rruleset()
for rule in early_close_rules:
early_close_ruleset.rrule(rule)
early_closes = early_close_ruleset.between(start, end, inc=True)
# Misc early closings from NYSE listing.
# http://www.nyse.com/pdfs/closings.pdf
#
# New Year's Eve
nye_1999 = datetime(1999, 12, 31, tzinfo=pytz.utc)
if start <= nye_1999 and nye_1999 <= end:
early_closes.append(nye_1999)
early_closes.sort()
return pd.DatetimeIndex(early_closes)
early_closes = get_early_closes(start, end)
def get_open_and_close(day, early_closes):
market_open = pd.Timestamp(
datetime(
year=day.year,
month=day.month,
day=day.day,
hour=9,
minute=31),
tz='US/Eastern').tz_convert('UTC')
# 1 PM if early close, 4 PM otherwise
close_hour = 13 if day in early_closes else 16
market_close = pd.Timestamp(
datetime(
year=day.year,
month=day.month,
day=day.day,
hour=close_hour),
tz='US/Eastern').tz_convert('UTC')
return market_open, market_close
def get_open_and_closes(trading_days, early_closes, get_open_and_close):
open_and_closes = pd.DataFrame(index=trading_days,
columns=('market_open', 'market_close'))
get_o_and_c = partial(get_open_and_close, early_closes=early_closes)
open_and_closes['market_open'], open_and_closes['market_close'] = \
zip(*open_and_closes.index.map(get_o_and_c))
return open_and_closes
open_and_closes = get_open_and_closes(trading_days, early_closes,
get_open_and_close) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/tradingcalendar.py | tradingcalendar.py |
from ctypes import (
Structure,
c_ubyte,
c_uint,
c_ulong,
c_ulonglong,
c_ushort,
sizeof,
)
import numpy as np
import pandas as pd
from six.moves import range
_inttypes_map = {
sizeof(t) - 1: t for t in {
c_ubyte,
c_uint,
c_ulong,
c_ulonglong,
c_ushort
}
}
_inttypes = pd.Series(_inttypes_map).\
reindex(range(max(_inttypes_map.keys())+1)).\
bfill().\
tolist()
def enum(option, *options):
"""
Construct a new enum object.
Parameters
----------
*options : iterable of str
The names of the fields for the enum.
Returns
-------
enum
A new enum collection.
Examples
--------
>>> e = enum('a', 'b', 'c')
>>> e
<enum: ('a', 'b', 'c')>
>>> e.a
0
>>> e.b
1
>>> e.a in e
True
>>> tuple(e)
(0, 1, 2)
Notes
-----
Identity checking is not guaranteed to work with enum members, instead
equality checks should be used. From CPython's documentation:
"The current implementation keeps an array of integer objects for all
integers between -5 and 256, when you create an int in that range you
actually just get back a reference to the existing object. So it should be
possible to change the value of 1. I suspect the behaviour of Python in
this case is undefined. :-)"
"""
options = (option,) + options
rangeob = range(len(options))
try:
inttype = _inttypes[int(np.log2(len(options) - 1)) // 8]
except IndexError:
raise OverflowError(
'Cannot store enums with more than sys.maxsize elements, got %d' %
len(options),
)
class _enum(Structure):
_fields_ = [(o, inttype) for o in options]
def __iter__(self):
return iter(rangeob)
def __contains__(self, value):
return 0 <= value < len(options)
def __repr__(self):
return '<enum: %s>' % (
('%d fields' % len(options))
if len(options) > 10 else
repr(options)
)
return _enum(*rangeob) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/enum.py | enum.py |
from operator import attrgetter
import six
def compose_types(a, *cs):
"""Compose multiple classes together.
Parameters
----------
*mcls : tuple[type]
The classes that you would like to compose
Returns
-------
cls : type
A type that subclasses all of the types in ``mcls``.
Notes
-----
A common use case for this is to build composed metaclasses, for example,
imagine you have some simple metaclass ``M`` and some instance of ``M``
named ``C`` like so:
.. code-block:: python
>>> class M(type):
... def __new__(mcls, name, bases, dict_):
... dict_['ayy'] = 'lmao'
... return super(M, mcls).__new__(mcls, name, bases, dict_)
>>> from six import with_metaclass
>>> class C(with_metaclass(M, object)):
... pass
We now want to create a sublclass of ``C`` that is also an abstract class.
We can use ``compose_types`` to create a new metaclass that is a subclass
of ``M`` and ``ABCMeta``. This is needed because a subclass of a class
with a metaclass must have a metaclass which is a subclass of the metaclass
of the superclass.
.. code-block:: python
>>> from abc import ABCMeta, abstractmethod
>>> class D(with_metaclass(compose_types(M, ABCMeta), C)):
... @abstractmethod
... def f(self):
... raise NotImplementedError('f')
We can see that this class has both metaclasses applied to it:
.. code-block:: python
>>> D.ayy
'lmao'
>>> D()
Traceback (most recent call last):
...
TypeError: Can't instantiate abstract class D with abstract methods f
An important note here is that ``M`` did not use ``type.__new__`` and
instead used ``super()``. This is to support cooperative multiple
inheritance which is needed for ``compose_types`` to work as intended.
After we have composed these types ``M.__new__``\'s super will actually
go to ``ABCMeta.__new__`` and not ``type.__new__``.
Always using ``super()`` to dispatch to your superclass is best practices
anyways so most classes should compose without much special considerations.
"""
if not cs:
# if there are no types to compose then just return the single type
return a
mcls = (a,) + cs
return type(
'compose_types(%s)' % ', '.join(map(attrgetter('__name__'), mcls)),
mcls,
{},
)
def with_metaclasses(metaclasses, *bases):
"""Make a class inheriting from ``bases`` whose metaclass inherits from
all of ``metaclasses``.
Like :func:`six.with_metaclass`, but allows multiple metaclasses.
Parameters
----------
metaclasses : iterable[type]
A tuple of types to use as metaclasses.
*bases : tuple[type]
A tuple of types to use as bases.
Returns
-------
base : type
A subtype of ``bases`` whose metaclass is a subtype of ``metaclasses``.
Notes
-----
The metaclasses must be written to support cooperative multiple
inheritance. This means that they must delegate all calls to ``super()``
instead of inlining their super class by name.
"""
return six.with_metaclass(compose_types(*metaclasses), *bases) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/metautils.py | metautils.py |
from collections import OrderedDict
from datetime import datetime
from distutils.version import StrictVersion
from warnings import (
catch_warnings,
filterwarnings,
)
import six
import numpy as np
from numpy import (
array_equal,
broadcast,
busday_count,
datetime64,
diff,
dtype,
empty,
flatnonzero,
hstack,
isnan,
nan,
vectorize,
where
)
from numpy.lib.stride_tricks import as_strided
from toolz import flip
numpy_version = StrictVersion(np.__version__)
uint8_dtype = dtype('uint8')
bool_dtype = dtype('bool')
uint32_dtype = dtype('uint32')
uint64_dtype = dtype('uint64')
int64_dtype = dtype('int64')
float32_dtype = dtype('float32')
float64_dtype = dtype('float64')
complex128_dtype = dtype('complex128')
datetime64D_dtype = dtype('datetime64[D]')
datetime64ns_dtype = dtype('datetime64[ns]')
object_dtype = dtype('O')
# We use object arrays for strings.
categorical_dtype = object_dtype
make_datetime64ns = flip(datetime64, 'ns')
make_datetime64D = flip(datetime64, 'D')
# Array compare that works across versions of numpy
try:
assert_array_compare = np.testing.utils.assert_array_compare
except AttributeError:
assert_array_compare = np.testing.assert_array_compare
NaTmap = {
dtype('datetime64[%s]' % unit): datetime64('NaT', unit)
for unit in ('ns', 'us', 'ms', 's', 'm', 'D')
}
def NaT_for_dtype(dtype):
"""Retrieve NaT with the same units as ``dtype``.
Parameters
----------
dtype : dtype-coercable
The dtype to lookup the NaT value for.
Returns
-------
NaT : dtype
The NaT value for the given dtype.
"""
return NaTmap[np.dtype(dtype)]
NaTns = NaT_for_dtype(datetime64ns_dtype)
NaTD = NaT_for_dtype(datetime64D_dtype)
_FILLVALUE_DEFAULTS = {
bool_dtype: False,
float32_dtype: nan,
float64_dtype: nan,
datetime64ns_dtype: NaTns,
object_dtype: None,
}
INT_DTYPES_BY_SIZE_BYTES = OrderedDict([
(1, dtype('int8')),
(2, dtype('int16')),
(4, dtype('int32')),
(8, dtype('int64')),
])
UNSIGNED_INT_DTYPES_BY_SIZE_BYTES = OrderedDict([
(1, dtype('uint8')),
(2, dtype('uint16')),
(4, dtype('uint32')),
(8, dtype('uint64')),
])
def int_dtype_with_size_in_bytes(size):
try:
return INT_DTYPES_BY_SIZE_BYTES[size]
except KeyError:
raise ValueError("No integral dtype whose size is %d bytes." % size)
def unsigned_int_dtype_with_size_in_bytes(size):
try:
return UNSIGNED_INT_DTYPES_BY_SIZE_BYTES[size]
except KeyError:
raise ValueError(
"No unsigned integral dtype whose size is %d bytes." % size
)
class NoDefaultMissingValue(Exception):
pass
def make_kind_check(python_types, numpy_kind):
"""
Make a function that checks whether a scalar or array is of a given kind
(e.g. float, int, datetime, timedelta).
"""
def check(value):
if hasattr(value, 'dtype'):
return value.dtype.kind == numpy_kind
return isinstance(value, python_types)
return check
is_float = make_kind_check(float, 'f')
is_int = make_kind_check(int, 'i')
is_datetime = make_kind_check(datetime, 'M')
is_object = make_kind_check(object, 'O')
def coerce_to_dtype(dtype, value):
"""
Make a value with the specified numpy dtype.
Only datetime64[ns] and datetime64[D] are supported for datetime dtypes.
"""
name = dtype.name
if name.startswith('datetime64'):
if name == 'datetime64[D]':
return make_datetime64D(value)
elif name == 'datetime64[ns]':
return make_datetime64ns(value)
else:
raise TypeError(
"Don't know how to coerce values of dtype %s" % dtype
)
return dtype.type(value)
def default_missing_value_for_dtype(dtype):
"""
Get the default fill value for `dtype`.
"""
try:
return _FILLVALUE_DEFAULTS[dtype]
except KeyError:
raise NoDefaultMissingValue(
"No default value registered for dtype %s." % dtype
)
def repeat_first_axis(array, count):
"""
Restride `array` to repeat `count` times along the first axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape (count,) + array.shape, composed of `array` repeated
`count` times along the first axis.
Example
-------
>>> from numpy import arange
>>> a = arange(3); a
array([0, 1, 2])
>>> repeat_first_axis(a, 2)
array([[0, 1, 2],
[0, 1, 2]])
>>> repeat_first_axis(a, 4)
array([[0, 1, 2],
[0, 1, 2],
[0, 1, 2],
[0, 1, 2]])
Notes
----
The resulting array will share memory with `array`. If you need to assign
to the input or output, you should probably make a copy first.
See Also
--------
repeat_last_axis
"""
return as_strided(array, (count,) + array.shape, (0,) + array.strides)
def repeat_last_axis(array, count):
"""
Restride `array` to repeat `count` times along the last axis.
Parameters
----------
array : np.array
The array to restride.
count : int
Number of times to repeat `array`.
Returns
-------
result : array
Array of shape array.shape + (count,) composed of `array` repeated
`count` times along the last axis.
Example
-------
>>> from numpy import arange
>>> a = arange(3); a
array([0, 1, 2])
>>> repeat_last_axis(a, 2)
array([[0, 0],
[1, 1],
[2, 2]])
>>> repeat_last_axis(a, 4)
array([[0, 0, 0, 0],
[1, 1, 1, 1],
[2, 2, 2, 2]])
Notes
----
The resulting array will share memory with `array`. If you need to assign
to the input or output, you should probably make a copy first.
See Also
--------
repeat_last_axis
"""
return as_strided(array, array.shape + (count,), array.strides + (0,))
def rolling_window(array, length):
"""
Restride an array of shape
(X_0, ... X_N)
into an array of shape
(length, X_0 - length + 1, ... X_N)
where each slice at index i along the first axis is equivalent to
result[i] = array[length * i:length * (i + 1)]
Parameters
----------
array : np.ndarray
The base array.
length : int
Length of the synthetic first axis to generate.
Returns
-------
out : np.ndarray
Example
-------
>>> from numpy import arange
>>> a = arange(25).reshape(5, 5)
>>> a
array([[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]])
>>> rolling_window(a, 2)
array([[[ 0, 1, 2, 3, 4],
[ 5, 6, 7, 8, 9]],
<BLANKLINE>
[[ 5, 6, 7, 8, 9],
[10, 11, 12, 13, 14]],
<BLANKLINE>
[[10, 11, 12, 13, 14],
[15, 16, 17, 18, 19]],
<BLANKLINE>
[[15, 16, 17, 18, 19],
[20, 21, 22, 23, 24]]])
"""
orig_shape = array.shape
if not orig_shape:
raise IndexError("Can't restride a scalar.")
elif orig_shape[0] <= length:
raise IndexError(
"Can't restride array of shape {shape} with"
" a window length of {len}".format(
shape=orig_shape,
len=length,
)
)
num_windows = (orig_shape[0] - length + 1)
new_shape = (num_windows, length) + orig_shape[1:]
new_strides = (array.strides[0],) + array.strides
return as_strided(array, new_shape, new_strides)
# Sentinel value that isn't NaT.
_notNaT = make_datetime64D(0)
iNaT = int(NaTns.view(int64_dtype))
assert iNaT == NaTD.view(int64_dtype), "iNaTns != iNaTD"
def isnat(obj):
"""
Check if a value is np.NaT.
"""
if obj.dtype.kind not in ('m', 'M'):
raise ValueError("%s is not a numpy datetime or timedelta")
return obj.view(int64_dtype) == iNaT
def is_missing(data, missing_value):
"""
Generic is_missing function that handles NaN and NaT.
"""
if is_float(data) and isnan(missing_value):
return isnan(data)
elif is_datetime(data) and isnat(missing_value):
return isnat(data)
elif is_object(data) and missing_value is None:
# XXX: Older versions of numpy returns True/False for array ==
# None. Work around this by boxing None in a 1x1 array, which causes
# numpy to do the broadcasted comparison we want.
return data == np.array([missing_value])
return (data == missing_value)
def same(x, y):
"""
Check if two scalar values are "the same".
Returns True if `x == y`, or if x and y are both NaN or both NaT.
"""
if is_float(x) and isnan(x) and is_float(y) and isnan(y):
return True
elif is_datetime(x) and isnat(x) and is_datetime(y) and isnat(y):
return True
else:
return x == y
def busday_count_mask_NaT(begindates, enddates, out=None):
"""
Simple of numpy.busday_count that returns `float` arrays rather than int
arrays, and handles `NaT`s by returning `NaN`s where the inputs were `NaT`.
Doesn't support custom weekdays or calendars, but probably should in the
future.
See Also
--------
np.busday_count
"""
if out is None:
out = empty(broadcast(begindates, enddates).shape, dtype=float)
beginmask = isnat(begindates)
endmask = isnat(enddates)
out = busday_count(
# Temporarily fill in non-NaT values.
where(beginmask, _notNaT, begindates),
where(endmask, _notNaT, enddates),
out=out,
)
# Fill in entries where either comparison was NaT with nan in the output.
out[beginmask | endmask] = nan
return out
class WarningContext(object):
"""
Re-usable contextmanager for contextually managing warnings.
"""
def __init__(self, *warning_specs):
self._warning_specs = warning_specs
self._catchers = []
def __enter__(self):
catcher = catch_warnings()
catcher.__enter__()
self._catchers.append(catcher)
for args, kwargs in self._warning_specs:
filterwarnings(*args, **kwargs)
return self
def __exit__(self, *exc_info):
catcher = self._catchers.pop()
return catcher.__exit__(*exc_info)
def ignore_nanwarnings():
"""
Helper for building a WarningContext that ignores warnings from numpy's
nanfunctions.
"""
return WarningContext(
(
('ignore',),
{'category': RuntimeWarning, 'module': 'numpy.lib.nanfunctions'},
)
)
def vectorized_is_element(array, choices):
"""
Check if each element of ``array`` is in choices.
Parameters
----------
array : np.ndarray
choices : object
Object implementing __contains__.
Returns
-------
was_element : np.ndarray[bool]
Array indicating whether each element of ``array`` was in ``choices``.
"""
return vectorize(choices.__contains__, otypes=[bool])(array)
def as_column(a):
"""
Convert an array of shape (N,) into an array of shape (N, 1).
This is equivalent to `a[:, np.newaxis]`.
Parameters
----------
a : np.ndarray
Example
-------
>>> import numpy as np
>>> a = np.arange(5)
>>> a
array([0, 1, 2, 3, 4])
>>> as_column(a)
array([[0],
[1],
[2],
[3],
[4]])
>>> as_column(a).shape
(5, 1)
"""
if a.ndim != 1:
raise ValueError(
"as_column expected an 1-dimensional array, "
"but got an array of shape %s" % (a.shape,)
)
return a[:, None]
def changed_locations(a, include_first):
"""
Compute indices of values in ``a`` that differ from the previous value.
Parameters
----------
a : np.ndarray
The array on which to indices of change.
include_first : bool
Whether or not to consider the first index of the array as "changed".
Example
-------
>>> import numpy as np
>>> changed_locations(np.array([0, 0, 5, 5, 1, 1]), include_first=False)
array([2, 4], dtype=int32)
>>> changed_locations(np.array([0, 0, 5, 5, 1, 1]), include_first=True)
array([0, 2, 4])
"""
if a.ndim > 1:
raise ValueError("indices_of_changed_values only supports 1D arrays.")
indices = flatnonzero(diff(a)) + 1
if not include_first:
return indices
return hstack([[0], indices])
def compare_datetime_arrays(x, y):
"""
Compare datetime64 ndarrays, treating NaT values as equal.
"""
return array_equal(x.view('int64'), y.view('int64'))
def bytes_array_to_native_str_object_array(a):
"""Convert an array of dtype S to an object array containing `str`.
"""
if six.PY2:
return a.astype(object)
else:
return a.astype(str).astype(object) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/numpy_utils.py | numpy_utils.py |
import functools
import inspect
from operator import methodcaller
import sys
from six import PY2
if PY2:
from abc import ABCMeta
from types import DictProxyType
from cgi import escape as escape_html
import contextlib
from contextlib2 import ExitStack
from ctypes import py_object, pythonapi
_new_mappingproxy = pythonapi.PyDictProxy_New
_new_mappingproxy.argtypes = [py_object]
_new_mappingproxy.restype = py_object
# Make mappingproxy a "class" so that we can use multipledispatch
# with it or do an ``isinstance(ob, mappingproxy)`` check in Python 2.
# You will never actually get an instance of this object, you will just
# get instances of ``types.DictProxyType``; however, ``mappingproxy`` is
# registered as a virtual super class so ``isinstance`` and ``issubclass``
# will work as expected. The only thing that will appear strange is that:
# ``type(mappingproxy({})) is not mappingproxy``, but you shouldn't do
# that.
class mappingproxy(object):
__metaclass__ = ABCMeta
def __new__(cls, *args, **kwargs):
return _new_mappingproxy(*args, **kwargs)
mappingproxy.register(DictProxyType)
# clear names not imported in the other branch
del DictProxyType
del ABCMeta
del py_object
del pythonapi
def exc_clear():
sys.exc_clear()
def consistent_round(val):
return round(val)
def update_wrapper(wrapper,
wrapped,
assigned=functools.WRAPPER_ASSIGNMENTS,
updated=functools.WRAPPER_UPDATES):
"""Backport of Python 3's functools.update_wrapper for __wrapped__.
"""
for attr in assigned:
try:
value = getattr(wrapped, attr)
except AttributeError:
pass
else:
setattr(wrapper, attr, value)
for attr in updated:
getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
# Issue #17482: set __wrapped__ last so we don't inadvertently copy it
# from the wrapped function when updating __dict__
wrapper.__wrapped__ = wrapped
# Return the wrapper so this can be used as a decorator via partial()
return wrapper
def wraps(wrapped,
assigned=functools.WRAPPER_ASSIGNMENTS,
updated=functools.WRAPPER_UPDATES):
"""Decorator factory to apply update_wrapper() to a wrapper function
Returns a decorator that invokes update_wrapper() with the decorated
function as the wrapper argument and the arguments to wraps() as the
remaining arguments. Default arguments are as for update_wrapper().
This is a convenience function to simplify applying partial() to
update_wrapper().
"""
return functools.partial(update_wrapper, wrapped=wrapped,
assigned=assigned, updated=updated)
values_as_list = methodcaller('values')
# This is deprecated in python 3.6+.
getargspec = inspect.getargspec
# Updated version of contextlib.contextmanager that uses our updated
# `wraps` to preserve function signatures.
@wraps(contextlib.contextmanager)
def contextmanager(f):
@wraps(f)
def helper(*args, **kwargs):
return contextlib.GeneratorContextManager(f(*args, **kwargs))
return helper
else:
from contextlib import contextmanager, ExitStack
from html import escape as escape_html
from types import MappingProxyType as mappingproxy
from math import ceil
def exc_clear():
# exc_clear was removed in Python 3. The except statement automatically
# clears the exception.
pass
def consistent_round(val):
if (val % 1) >= 0.5:
return ceil(val)
else:
return round(val)
update_wrapper = functools.update_wrapper
wraps = functools.wraps
def values_as_list(dictionary):
"""Return the dictionary values as a list without forcing a copy
in Python 2.
"""
return list(dictionary.values())
def getargspec(f):
full_argspec = inspect.getfullargspec(f)
return inspect.ArgSpec(
args=full_argspec.args,
varargs=full_argspec.varargs,
keywords=full_argspec.varkw,
defaults=full_argspec.defaults,
)
unicode = type(u'')
__all__ = [
'PY2',
'ExitStack',
'consistent_round',
'contextmanager',
'escape_html',
'exc_clear',
'mappingproxy',
'unicode',
'update_wrapper',
'values_as_list',
'wraps',
] | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/compat.py | compat.py |
from collections import MutableMapping
import errno
from functools import partial
import os
import pickle
from distutils import dir_util
from shutil import rmtree, move
from tempfile import mkdtemp, NamedTemporaryFile
import pandas as pd
from .compat import PY2
from .context_tricks import nop_context
from .paths import ensure_directory
from .sentinel import sentinel
class Expired(Exception):
"""Marks that a :class:`CachedObject` has expired.
"""
ExpiredCachedObject = sentinel('ExpiredCachedObject')
AlwaysExpired = sentinel('AlwaysExpired')
class CachedObject(object):
"""
A simple struct for maintaining a cached object with an expiration date.
Parameters
----------
value : object
The object to cache.
expires : datetime-like
Expiration date of `value`. The cache is considered invalid for dates
**strictly greater** than `expires`.
Examples
--------
>>> from pandas import Timestamp, Timedelta
>>> expires = Timestamp('2014', tz='UTC')
>>> obj = CachedObject(1, expires)
>>> obj.unwrap(expires - Timedelta('1 minute'))
1
>>> obj.unwrap(expires)
1
>>> obj.unwrap(expires + Timedelta('1 minute'))
... # doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
Expired: 2014-01-01 00:00:00+00:00
"""
def __init__(self, value, expires):
self._value = value
self._expires = expires
@classmethod
def expired(cls):
"""Construct a CachedObject that's expired at any time.
"""
return cls(ExpiredCachedObject, expires=AlwaysExpired)
def unwrap(self, dt):
"""
Get the cached value.
Returns
-------
value : object
The cached value.
Raises
------
Expired
Raised when `dt` is greater than self.expires.
"""
expires = self._expires
if expires is AlwaysExpired or expires < dt:
raise Expired(self._expires)
return self._value
def _unsafe_get_value(self):
"""You almost certainly shouldn't use this."""
return self._value
class ExpiringCache(object):
"""
A cache of multiple CachedObjects, which returns the wrapped the value
or raises and deletes the CachedObject if the value has expired.
Parameters
----------
cache : dict-like, optional
An instance of a dict-like object which needs to support at least:
`__del__`, `__getitem__`, `__setitem__`
If `None`, than a dict is used as a default.
cleanup : callable, optional
A method that takes a single argument, a cached object, and is called
upon expiry of the cached object, prior to deleting the object. If not
provided, defaults to a no-op.
Examples
--------
>>> from pandas import Timestamp, Timedelta
>>> expires = Timestamp('2014', tz='UTC')
>>> value = 1
>>> cache = ExpiringCache()
>>> cache.set('foo', value, expires)
>>> cache.get('foo', expires - Timedelta('1 minute'))
1
>>> cache.get('foo', expires + Timedelta('1 minute'))
Traceback (most recent call last):
...
KeyError: 'foo'
"""
def __init__(self, cache=None, cleanup=lambda value_to_clean: None):
if cache is not None:
self._cache = cache
else:
self._cache = {}
self.cleanup = cleanup
def get(self, key, dt):
"""Get the value of a cached object.
Parameters
----------
key : any
The key to lookup.
dt : datetime
The time of the lookup.
Returns
-------
result : any
The value for ``key``.
Raises
------
KeyError
Raised if the key is not in the cache or the value for the key
has expired.
"""
try:
return self._cache[key].unwrap(dt)
except Expired:
self.cleanup(self._cache[key]._unsafe_get_value())
del self._cache[key]
raise KeyError(key)
def set(self, key, value, expiration_dt):
"""Adds a new key value pair to the cache.
Parameters
----------
key : any
The key to use for the pair.
value : any
The value to store under the name ``key``.
expiration_dt : datetime
When should this mapping expire? The cache is considered invalid
for dates **strictly greater** than ``expiration_dt``.
"""
self._cache[key] = CachedObject(value, expiration_dt)
class dataframe_cache(MutableMapping):
"""A disk-backed cache for dataframes.
``dataframe_cache`` is a mutable mapping from string names to pandas
DataFrame objects.
This object may be used as a context manager to delete the cache directory
on exit.
Parameters
----------
path : str, optional
The directory path to the cache. Files will be written as
``path/<keyname>``.
lock : Lock, optional
Thread lock for multithreaded/multiprocessed access to the cache.
If not provided no locking will be used.
clean_on_failure : bool, optional
Should the directory be cleaned up if an exception is raised in the
context manager.
serialize : {'msgpack', 'pickle:<n>'}, optional
How should the data be serialized. If ``'pickle'`` is passed, an
optional pickle protocol can be passed like: ``'pickle:3'`` which says
to use pickle protocol 3.
Notes
-----
The syntax ``cache[:]`` will load all key:value pairs into memory as a
dictionary.
The cache uses a temporary file format that is subject to change between
versions of zipline.
"""
def __init__(self,
path=None,
lock=None,
clean_on_failure=True,
serialization='msgpack'):
self.path = path if path is not None else mkdtemp()
self.lock = lock if lock is not None else nop_context
self.clean_on_failure = clean_on_failure
if serialization == 'msgpack':
self.serialize = self._serialize_msgpack
self.deserialize = self._read_msgpack
self._protocol = None
else:
s = serialization.split(':', 1)
if s[0] != 'pickle':
raise ValueError(
"'serialization' must be either 'msgpack' or 'pickle[:n]'",
)
self._protocol = int(s[1]) if len(s) == 2 else None
self.serialize = self._serialize_pickle
self.deserialize = (
pickle.load if PY2 else
partial(pickle.load, encoding='latin-1')
)
ensure_directory(self.path)
def _read_msgpack(self):
pass
def _serialize_msgpack(self, df, path):
print('serialize msgpack')
exit()
def _serialize_pickle(self, df, path):
with open(path, 'wb') as f:
pickle.dump(df, f, protocol=self._protocol)
def _keypath(self, key):
return os.path.join(self.path, key)
def __enter__(self):
return self
def __exit__(self, type_, value, tb):
if not (self.clean_on_failure or value is None):
# we are not cleaning up after a failure and there was an exception
return
with self.lock:
rmtree(self.path)
def __getitem__(self, key):
if key == slice(None):
return dict(self.items())
with self.lock:
try:
with open(self._keypath(key), 'rb') as f:
return self.deserialize(f)
except IOError as e:
if e.errno != errno.ENOENT:
raise
raise KeyError(key)
def __setitem__(self, key, value):
with self.lock:
self.serialize(value, self._keypath(key))
def __delitem__(self, key):
with self.lock:
try:
os.remove(self._keypath(key))
except OSError as e:
if e.errno == errno.ENOENT:
# raise a keyerror if this directory did not exist
raise KeyError(key)
# reraise the actual oserror otherwise
raise
def __iter__(self):
return iter(os.listdir(self.path))
def __len__(self):
return len(os.listdir(self.path))
def __repr__(self):
return '<%s: keys={%s}>' % (
type(self).__name__,
', '.join(map(repr, sorted(self))),
)
class working_file(object):
"""A context manager for managing a temporary file that will be moved
to a non-temporary location if no exceptions are raised in the context.
Parameters
----------
final_path : str
The location to move the file when committing.
*args, **kwargs
Forwarded to NamedTemporaryFile.
Notes
-----
The file is moved on __exit__ if there are no exceptions.
``working_file`` uses :func:`shutil.move` to move the actual files,
meaning it has as strong of guarantees as :func:`shutil.move`.
"""
def __init__(self, final_path, *args, **kwargs):
self._tmpfile = NamedTemporaryFile(delete=False, *args, **kwargs)
self._final_path = final_path
@property
def path(self):
"""Alias for ``name`` to be consistent with
:class:`~zipline.utils.cache.working_dir`.
"""
return self._tmpfile.name
def _commit(self):
"""Sync the temporary file to the final path.
"""
move(self.path, self._final_path)
def __enter__(self):
self._tmpfile.__enter__()
return self
def __exit__(self, *exc_info):
self._tmpfile.__exit__(*exc_info)
if exc_info[0] is None:
self._commit()
class working_dir(object):
"""A context manager for managing a temporary directory that will be moved
to a non-temporary location if no exceptions are raised in the context.
Parameters
----------
final_path : str
The location to move the file when committing.
*args, **kwargs
Forwarded to tmp_dir.
Notes
-----
The file is moved on __exit__ if there are no exceptions.
``working_dir`` uses :func:`dir_util.copy_tree` to move the actual files,
meaning it has as strong of guarantees as :func:`dir_util.copy_tree`.
"""
def __init__(self, final_path, *args, **kwargs):
self.path = mkdtemp()
self._final_path = final_path
def ensure_dir(self, *path_parts):
"""Ensures a subdirectory of the working directory.
Parameters
----------
path_parts : iterable[str]
The parts of the path after the working directory.
"""
path = self.getpath(*path_parts)
ensure_directory(path)
return path
def getpath(self, *path_parts):
"""Get a path relative to the working directory.
Parameters
----------
path_parts : iterable[str]
The parts of the path after the working directory.
"""
return os.path.join(self.path, *path_parts)
def _commit(self):
"""Sync the temporary directory to the final path.
"""
dir_util.copy_tree(self.path, self._final_path)
def __enter__(self):
return self
def __exit__(self, *exc_info):
if exc_info[0] is None:
self._commit()
rmtree(self.path) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/cache.py | cache.py |
from contextlib import contextmanager
from copy import deepcopy
from itertools import product
import operator as op
import warnings
import numpy as np
import pandas as pd
from distutils.version import StrictVersion
from trading_calendars.utils.pandas_utils import days_at_time # noqa: reexport
pandas_version = StrictVersion(pd.__version__)
new_pandas = pandas_version >= StrictVersion('0.19')
skip_pipeline_new_pandas = \
'Pipeline categoricals are not yet compatible with pandas >=0.19'
if pandas_version >= StrictVersion('0.20'):
def normalize_date(dt):
"""
Normalize datetime.datetime value to midnight. Returns datetime.date as
a datetime.datetime at midnight
Returns
-------
normalized : datetime.datetime or Timestamp
"""
return dt.normalize()
else:
from pandas.tseries.tools import normalize_date # noqa
def july_5th_holiday_observance(datetime_index):
return datetime_index[datetime_index.year != 2013]
def explode(df):
"""
Take a DataFrame and return a triple of
(df.index, df.columns, df.values)
"""
return df.index, df.columns, df.values
def _time_to_micros(time):
"""Convert a time into microseconds since midnight.
Parameters
----------
time : datetime.time
The time to convert.
Returns
-------
us : int
The number of microseconds since midnight.
Notes
-----
This does not account for leap seconds or daylight savings.
"""
seconds = time.hour * 60 * 60 + time.minute * 60 + time.second
return 1000000 * seconds + time.microsecond
_opmap = dict(zip(
product((True, False), repeat=3),
product((op.le, op.lt), (op.le, op.lt), (op.and_, op.or_)),
))
def mask_between_time(dts, start, end, include_start=True, include_end=True):
"""Return a mask of all of the datetimes in ``dts`` that are between
``start`` and ``end``.
Parameters
----------
dts : pd.DatetimeIndex
The index to mask.
start : time
Mask away times less than the start.
end : time
Mask away times greater than the end.
include_start : bool, optional
Inclusive on ``start``.
include_end : bool, optional
Inclusive on ``end``.
Returns
-------
mask : np.ndarray[bool]
A bool array masking ``dts``.
See Also
--------
:meth:`pandas.DatetimeIndex.indexer_between_time`
"""
# This function is adapted from
# `pandas.Datetime.Index.indexer_between_time` which was originally
# written by Wes McKinney, Chang She, and Grant Roch.
time_micros = dts._get_time_micros()
start_micros = _time_to_micros(start)
end_micros = _time_to_micros(end)
left_op, right_op, join_op = _opmap[
bool(include_start),
bool(include_end),
start_micros <= end_micros,
]
return join_op(
left_op(start_micros, time_micros),
right_op(time_micros, end_micros),
)
def find_in_sorted_index(dts, dt):
"""
Find the index of ``dt`` in ``dts``.
This function should be used instead of `dts.get_loc(dt)` if the index is
large enough that we don't want to initialize a hash table in ``dts``. In
particular, this should always be used on minutely trading calendars.
Parameters
----------
dts : pd.DatetimeIndex
Index in which to look up ``dt``. **Must be sorted**.
dt : pd.Timestamp
``dt`` to be looked up.
Returns
-------
ix : int
Integer index such that dts[ix] == dt.
Raises
------
KeyError
If dt is not in ``dts``.
"""
ix = dts.searchsorted(dt)
if ix == len(dts) or dts[ix] != dt:
raise LookupError("{dt} is not in {dts}".format(dt=dt, dts=dts))
return ix
def nearest_unequal_elements(dts, dt):
"""
Find values in ``dts`` closest but not equal to ``dt``.
Returns a pair of (last_before, first_after).
When ``dt`` is less than any element in ``dts``, ``last_before`` is None.
When ``dt`` is greater any element in ``dts``, ``first_after`` is None.
``dts`` must be unique and sorted in increasing order.
Parameters
----------
dts : pd.DatetimeIndex
Dates in which to search.
dt : pd.Timestamp
Date for which to find bounds.
"""
if not dts.is_unique:
raise ValueError("dts must be unique")
if not dts.is_monotonic_increasing:
raise ValueError("dts must be sorted in increasing order")
if not len(dts):
return None, None
sortpos = dts.searchsorted(dt, side='left')
try:
sortval = dts[sortpos]
except IndexError:
# dt is greater than any value in the array.
return dts[-1], None
if dt < sortval:
lower_ix = sortpos - 1
upper_ix = sortpos
elif dt == sortval:
lower_ix = sortpos - 1
upper_ix = sortpos + 1
else:
lower_ix = sortpos
upper_ix = sortpos + 1
lower_value = dts[lower_ix] if lower_ix >= 0 else None
upper_value = dts[upper_ix] if upper_ix < len(dts) else None
return lower_value, upper_value
def timedelta_to_integral_seconds(delta):
"""
Convert a pd.Timedelta to a number of seconds as an int.
"""
return int(delta.total_seconds())
def timedelta_to_integral_minutes(delta):
"""
Convert a pd.Timedelta to a number of minutes as an int.
"""
return timedelta_to_integral_seconds(delta) // 60
@contextmanager
def ignore_pandas_nan_categorical_warning():
with warnings.catch_warnings():
# Pandas >= 0.18 doesn't like null-ish values in categories, but
# avoiding that requires a broader change to how missing values are
# handled in pipeline, so for now just silence the warning.
warnings.filterwarnings(
'ignore',
category=FutureWarning,
)
yield
# pd==.21.x enumarted list from pd.Index.get_indexers_list()
# https://github.com/pandas-dev/pandas/blob/0.21.x/pandas/core/indexing.py#L29
# in pd==1 now mixins. maybe do not have to remove these?
_INDEXER_NAMES = ['_ix', '_iloc', '_loc', '_at', '_iat']
def clear_dataframe_indexer_caches(df):
"""
Clear cached attributes from a pandas DataFrame.
By default pandas memoizes indexers (`iloc`, `loc`, `ix`, etc.) objects on
DataFrames, resulting in refcycles that can lead to unexpectedly long-lived
DataFrames. This function attempts to clear those cycles by deleting the
cached indexers from the frame.
Parameters
----------
df : pd.DataFrame
"""
for attr in _INDEXER_NAMES:
try:
delattr(df, attr)
except AttributeError:
pass
def categorical_df_concat(df_list, inplace=False):
"""
Prepare list of pandas DataFrames to be used as input to pd.concat.
Ensure any columns of type 'category' have the same categories across each
dataframe.
Parameters
----------
df_list : list
List of dataframes with same columns.
inplace : bool
True if input list can be modified. Default is False.
Returns
-------
concatenated : df
Dataframe of concatenated list.
"""
if not inplace:
df_list = deepcopy(df_list)
# Assert each dataframe has the same columns/dtypes
df = df_list[0]
if not all([(df.dtypes.equals(df_i.dtypes)) for df_i in df_list[1:]]):
raise ValueError("Input DataFrames must have the same columns/dtypes.")
categorical_columns = df.columns[df.dtypes == 'category']
for col in categorical_columns:
new_categories = _sort_set_none_first(
_union_all(frame[col].cat.categories for frame in df_list)
)
with ignore_pandas_nan_categorical_warning():
for df in df_list:
df[col].cat.set_categories(new_categories, inplace=True)
return pd.concat(df_list)
def _union_all(iterables):
"""Union entries in ``iterables`` into a set.
"""
return set().union(*iterables)
def _sort_set_none_first(set_):
"""Sort a set, sorting ``None`` before other elements, if present.
"""
if None in set_:
set_.remove(None)
out = [None]
out.extend(sorted(set_))
set_.add(None)
return out
else:
return sorted(set_)
def empty_dataframe(*columns):
"""Create an empty dataframe with columns of particular types.
Parameters
----------
*columns
The (column_name, column_dtype) pairs.
Returns
-------
typed_dataframe : pd.DataFrame
The empty typed dataframe.
Examples
--------
>>> df = empty_dataframe(
... ('a', 'int64'),
... ('b', 'float64'),
... ('c', 'datetime64[ns]'),
... )
>>> df
Empty DataFrame
Columns: [a, b, c]
Index: []
df.dtypes
a int64
b float64
c datetime64[ns]
dtype: object
"""
return pd.DataFrame(np.array([], dtype=list(columns)))
def check_indexes_all_same(indexes, message="Indexes are not equal."):
"""Check that a list of Index objects are all equal.
Parameters
----------
indexes : iterable[pd.Index]
Iterable of indexes to check.
Raises
------
ValueError
If the indexes are not all the same.
"""
iterator = iter(indexes)
first = next(iterator)
for other in iterator:
same = (first == other)
if not same.all():
bad_loc = np.flatnonzero(~same)[0]
raise ValueError(
"{}\nFirst difference is at index {}: "
"{} != {}".format(
message, bad_loc, first[bad_loc], other[bad_loc]
),
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/pandas_utils.py | pandas_utils.py |
from errno import EEXIST
import os
from os.path import exists, expanduser, join
import pandas as pd
def hidden(path):
"""Check if a path is hidden.
Parameters
----------
path : str
A filepath.
"""
return os.path.split(path)[1].startswith('.')
def ensure_directory(path):
"""
Ensure that a directory named "path" exists.
"""
try:
os.makedirs(path)
except OSError as exc:
if exc.errno == EEXIST and os.path.isdir(path):
return
raise
def ensure_directory_containing(path):
"""
Ensure that the directory containing `path` exists.
This is just a convenience wrapper for doing::
ensure_directory(os.path.dirname(path))
"""
ensure_directory(os.path.dirname(path))
def ensure_file(path):
"""
Ensure that a file exists. This will create any parent directories needed
and create an empty file if it does not exist.
Parameters
----------
path : str
The file path to ensure exists.
"""
ensure_directory_containing(path)
open(path, 'a+').close() # touch the file
def update_modified_time(path, times=None):
"""
Updates the modified time of an existing file. This will create any
parent directories needed and create an empty file if it does not exist.
Parameters
----------
path : str
The file path to update.
times : tuple
A tuple of size two; access time and modified time
"""
ensure_directory_containing(path)
os.utime(path, times)
def last_modified_time(path):
"""
Get the last modified time of path as a Timestamp.
"""
return pd.Timestamp(os.path.getmtime(path), unit='s', tz='UTC')
def modified_since(path, dt):
"""
Check whether `path` was modified since `dt`.
Returns False if path doesn't exist.
Parameters
----------
path : str
Path to the file to be checked.
dt : pd.Timestamp
The date against which to compare last_modified_time(path).
Returns
-------
was_modified : bool
Will be ``False`` if path doesn't exists, or if its last modified date
is earlier than or equal to `dt`
"""
return exists(path) and last_modified_time(path) > dt
def zipline_root(environ=None):
"""
Get the root directory for all zipline-managed files.
For testing purposes, this accepts a dictionary to interpret as the os
environment.
Parameters
----------
environ : dict, optional
A dict to interpret as the os environment.
Returns
-------
root : string
Path to the zipline root dir.
"""
if environ is None:
environ = os.environ
root = environ.get('ZIPLINE_ROOT', None)
if root is None:
root = expanduser('~/.zipline')
return root
def zipline_path(paths, environ=None):
"""
Get a path relative to the zipline root.
Parameters
----------
paths : list[str]
List of requested path pieces.
environ : dict, optional
An environment dict to forward to zipline_root.
Returns
-------
newpath : str
The requested path joined with the zipline root.
"""
return join(zipline_root(environ=environ), *paths)
def default_extension(environ=None):
"""
Get the path to the default zipline extension file.
Parameters
----------
environ : dict, optional
An environment dict to forwart to zipline_root.
Returns
-------
default_extension_path : str
The file path to the default zipline extension file.
"""
return zipline_path(['extension.py'], environ=environ)
def data_root(environ=None):
"""
The root directory for zipline data files.
Parameters
----------
environ : dict, optional
An environment dict to forward to zipline_root.
Returns
-------
data_root : str
The zipline data root.
"""
return zipline_path(['data'], environ=environ)
def ensure_data_root(environ=None):
"""
Ensure that the data root exists.
"""
ensure_directory(data_root(environ=environ))
def data_path(paths, environ=None):
"""
Get a path relative to the zipline data directory.
Parameters
----------
paths : iterable[str]
List of requested path pieces.
environ : dict, optional
An environment dict to forward to zipline_root.
Returns
-------
newpath : str
The requested path joined with the zipline data root.
"""
return zipline_path(['data'] + list(paths), environ=environ)
def cache_root(environ=None):
"""
The root directory for zipline cache files.
Parameters
----------
environ : dict, optional
An environment dict to forward to zipline_root.
Returns
-------
cache_root : str
The zipline cache root.
"""
return zipline_path(['cache'], environ=environ)
def ensure_cache_root(environ=None):
"""
Ensure that the data root exists.
"""
ensure_directory(cache_root(environ=environ))
def cache_path(paths, environ=None):
"""
Get a path relative to the zipline cache directory.
Parameters
----------
paths : iterable[str]
List of requested path pieces.
environ : dict, optional
An environment dict to forward to zipline_root.
Returns
-------
newpath : str
The requested path joined with the zipline cache root.
"""
return zipline_path(['cache'] + list(paths), environ=environ) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/paths.py | paths.py |
from collections import namedtuple
from itertools import chain
from six.moves import map, zip_longest
from zipline.errors import ZiplineError
from zipline.utils.compat import getargspec
Argspec = namedtuple('Argspec', ['args', 'starargs', 'kwargs'])
def singleton(cls):
instances = {}
def getinstance():
if cls not in instances:
instances[cls] = cls()
return instances[cls]
return getinstance
@singleton
class Ignore(object):
def __str__(self):
return 'Argument.ignore'
__repr__ = __str__
@singleton
class NoDefault(object):
def __str__(self):
return 'Argument.no_default'
__repr__ = __str__
@singleton
class AnyDefault(object):
def __str__(self):
return 'Argument.any_default'
__repr__ = __str__
class Argument(namedtuple('Argument', ['name', 'default'])):
"""
An argument to a function.
Argument.no_default is a value representing no default to the argument.
Argument.ignore is a value that says you should ignore the default value.
"""
no_default = NoDefault()
any_default = AnyDefault()
ignore = Ignore()
def __new__(cls, name=ignore, default=ignore):
return super(Argument, cls).__new__(cls, name, default)
def __str__(self):
if self.has_no_default(self) or self.ignore_default(self):
return str(self.name)
else:
return '='.join([str(self.name), str(self.default)])
def __repr__(self):
return 'Argument(%s, %s)' % (repr(self.name), repr(self.default))
def _defaults_match(self, arg):
return any(map(Argument.ignore_default, [self, arg])) \
or (self.default is Argument.any_default and
arg.default is not Argument.no_default) \
or (arg.default is Argument.any_default and
self.default is not Argument.no_default) \
or self.default == arg.default
def _names_match(self, arg):
return self.name == arg.name \
or self.name is Argument.ignore \
or arg.name is Argument.ignore
def matches(self, arg):
return self._names_match(arg) and self._defaults_match(arg)
__eq__ = matches
@staticmethod
def parse_argspec(callable_):
"""
Takes a callable and returns a tuple with the list of Argument objects,
the name of *args, and the name of **kwargs.
If *args or **kwargs is not present, it will be None.
This returns a namedtuple called Argspec that has three fields named:
args, starargs, and kwargs.
"""
args, varargs, keywords, defaults = getargspec(callable_)
defaults = list(defaults or [])
if getattr(callable_, '__self__', None) is not None:
# This is a bound method, drop the self param.
args = args[1:]
first_default = len(args) - len(defaults)
return Argspec(
[Argument(arg, Argument.no_default
if n < first_default else defaults[n - first_default])
for n, arg in enumerate(args)],
varargs,
keywords,
)
@staticmethod
def has_no_default(arg):
return arg.default is Argument.no_default
@staticmethod
def ignore_default(arg):
return arg.default is Argument.ignore
def _expect_extra(expected, present, exc_unexpected, exc_missing, exc_args):
"""
Checks for the presence of an extra to the argument list. Raises expections
if this is unexpected or if it is missing and expected.
"""
if present:
if not expected:
raise exc_unexpected(*exc_args)
elif expected and expected is not Argument.ignore:
raise exc_missing(*exc_args)
def verify_callable_argspec(callable_,
expected_args=Argument.ignore,
expect_starargs=Argument.ignore,
expect_kwargs=Argument.ignore):
"""
Checks the callable_ to make sure that it satisfies the given
expectations.
expected_args should be an iterable of Arguments in the order you expect to
receive them.
expect_starargs means that the function should or should not take a *args
param. expect_kwargs says the callable should or should not take **kwargs
param.
If expected_args, expect_starargs, or expect_kwargs is Argument.ignore,
then the checks related to that argument will not occur.
Example usage:
callable_check(
f,
[Argument('a'), Argument('b', 1)],
expect_starargs=True,
expect_kwargs=Argument.ignore
)
"""
if not callable(callable_):
raise NotCallable(callable_)
expected_arg_list = list(
expected_args if expected_args is not Argument.ignore else []
)
args, starargs, kwargs = Argument.parse_argspec(callable_)
exc_args = callable_, args, starargs, kwargs
# Check the *args.
_expect_extra(
expect_starargs,
starargs,
UnexpectedStarargs,
NoStarargs,
exc_args,
)
# Check the **kwargs.
_expect_extra(
expect_kwargs,
kwargs,
UnexpectedKwargs,
NoKwargs,
exc_args,
)
if expected_args is Argument.ignore:
# Ignore the argument list checks.
return
if len(args) < len(expected_arg_list):
# One or more argument that we expected was not present.
raise NotEnoughArguments(
callable_,
args,
starargs,
kwargs,
[arg for arg in expected_arg_list if arg not in args],
)
elif len(args) > len(expected_arg_list):
raise TooManyArguments(
callable_, args, starargs, kwargs
)
# Empty argument that will not match with any actual arguments.
missing_arg = Argument(object(), object())
for expected, provided in zip_longest(expected_arg_list,
args,
fillvalue=missing_arg):
if not expected.matches(provided):
raise MismatchedArguments(
callable_, args, starargs, kwargs
)
class BadCallable(TypeError, AssertionError, ZiplineError):
"""
The given callable is not structured in the expected way.
"""
_lambda_name = (lambda: None).__name__
def __init__(self, callable_, args, starargs, kwargs):
self.callable_ = callable_
self.args = args
self.starargs = starargs
self.kwargsname = kwargs
self.kwargs = {}
def format_callable(self):
if self.callable_.__name__ == self._lambda_name:
fmt = '%s %s'
name = 'lambda'
else:
fmt = '%s(%s)'
name = self.callable_.__name__
return fmt % (
name,
', '.join(
chain(
(str(arg) for arg in self.args),
('*' + sa for sa in (self.starargs,) if sa is not None),
('**' + ka for ka in (self.kwargsname,) if ka is not None),
)
)
)
@property
def msg(self):
return str(self)
class NoStarargs(BadCallable):
def __str__(self):
return '%s does not allow for *args' % self.format_callable()
class UnexpectedStarargs(BadCallable):
def __str__(self):
return '%s should not allow for *args' % self.format_callable()
class NoKwargs(BadCallable):
def __str__(self):
return '%s does not allow for **kwargs' % self.format_callable()
class UnexpectedKwargs(BadCallable):
def __str__(self):
return '%s should not allow for **kwargs' % self.format_callable()
class NotCallable(BadCallable):
"""
The provided 'callable' is not actually a callable.
"""
def __init__(self, callable_):
self.callable_ = callable_
def __str__(self):
return '%s is not callable' % self.format_callable()
def format_callable(self):
try:
return self.callable_.__name__
except AttributeError:
return str(self.callable_)
class NotEnoughArguments(BadCallable):
"""
The callback does not accept enough arguments.
"""
def __init__(self, callable_, args, starargs, kwargs, missing_args):
super(NotEnoughArguments, self).__init__(
callable_, args, starargs, kwargs
)
self.missing_args = missing_args
def __str__(self):
missing_args = list(map(str, self.missing_args))
return '%s is missing argument%s: %s' % (
self.format_callable(),
's' if len(missing_args) > 1 else '',
', '.join(missing_args),
)
class TooManyArguments(BadCallable):
"""
The callback cannot be called by passing the expected number of arguments.
"""
def __str__(self):
return '%s accepts too many arguments' % self.format_callable()
class MismatchedArguments(BadCallable):
"""
The argument lists are of the same lengths, but not in the correct order.
"""
def __str__(self):
return '%s accepts mismatched parameters' % self.format_callable() | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/argcheck.py | argcheck.py |
@object.__new__
class nop_context(object):
"""A nop context manager.
"""
def __enter__(self):
pass
def __exit__(self, *excinfo):
pass
def _nop(*args, **kwargs):
pass
class CallbackManager(object):
"""Create a context manager from a pre-execution callback and a
post-execution callback.
Parameters
----------
pre : (...) -> any, optional
A pre-execution callback. This will be passed ``*args`` and
``**kwargs``.
post : (...) -> any, optional
A post-execution callback. This will be passed ``*args`` and
``**kwargs``.
Notes
-----
The enter value of this context manager will be the result of calling
``pre(*args, **kwargs)``
Examples
--------
>>> def pre(where):
... print('entering %s block' % where)
>>> def post(where):
... print('exiting %s block' % where)
>>> manager = CallbackManager(pre, post)
>>> with manager('example'):
... print('inside example block')
entering example block
inside example block
exiting example block
These are reusable with different args:
>>> with manager('another'):
... print('inside another block')
entering another block
inside another block
exiting another block
"""
def __init__(self, pre=None, post=None):
self.pre = pre if pre is not None else _nop
self.post = post if post is not None else _nop
def __call__(self, *args, **kwargs):
return _ManagedCallbackContext(self.pre, self.post, args, kwargs)
# special case, if no extra args are passed make this a context manager
# which forwards no args to pre and post
def __enter__(self):
return self.pre()
def __exit__(self, *excinfo):
self.post()
class _ManagedCallbackContext(object):
def __init__(self, pre, post, args, kwargs):
self._pre = pre
self._post = post
self._args = args
self._kwargs = kwargs
def __enter__(self):
return self._pre(*self._args, **self._kwargs)
def __exit__(self, *excinfo):
self._post(*self._args, **self._kwargs) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/context_tricks.py | context_tricks.py |
import operator as op
from six import PY2
from toolz import peek
from zipline.utils.functional import foldr
if PY2:
class range(object):
"""Lazy range object with constant time containment check.
The arguments are the same as ``range``.
"""
__slots__ = 'start', 'stop', 'step'
def __init__(self, stop, *args):
if len(args) > 2:
raise TypeError(
'range takes at most 3 arguments (%d given)' % len(args)
)
if not args:
self.start = 0
self.stop = stop
self.step = 1
else:
self.start = stop
self.stop = args[0]
try:
self.step = args[1]
except IndexError:
self.step = 1
if self.step == 0:
raise ValueError('range step must not be zero')
def __iter__(self):
"""
Examples
--------
>>> list(range(1))
[0]
>>> list(range(5))
[0, 1, 2, 3, 4]
>>> list(range(1, 5))
[1, 2, 3, 4]
>>> list(range(0, 5, 2))
[0, 2, 4]
>>> list(range(5, 0, -1))
[5, 4, 3, 2, 1]
>>> list(range(5, 0, 1))
[]
"""
n = self.start
stop = self.stop
step = self.step
cmp_ = op.lt if step > 0 else op.gt
while cmp_(n, stop):
yield n
n += step
_ops = (
(op.gt, op.ge),
(op.le, op.lt),
)
def __contains__(self, other, _ops=_ops):
# Algorithm taken from CPython
# Objects/rangeobject.c:range_contains_long
start = self.start
step = self.step
cmp_start, cmp_stop = _ops[step > 0]
return (
cmp_start(start, other) and
cmp_stop(other, self.stop) and
(other - start) % step == 0
)
del _ops
def __len__(self):
"""
Examples
--------
>>> len(range(1))
1
>>> len(range(5))
5
>>> len(range(1, 5))
4
>>> len(range(0, 5, 2))
3
>>> len(range(5, 0, -1))
5
>>> len(range(5, 0, 1))
0
"""
# Algorithm taken from CPython
# rangeobject.c:compute_range_length
step = self.step
if step > 0:
low = self.start
high = self.stop
else:
low = self.stop
high = self.start
step = -step
if low >= high:
return 0
return (high - low - 1) // step + 1
def __repr__(self):
return '%s(%s, %s%s)' % (
type(self).__name__,
self.start,
self.stop,
(', ' + str(self.step)) if self.step != 1 else '',
)
def __hash__(self):
return hash((type(self), self.start, self.stop, self.step))
def __eq__(self, other):
"""
Examples
--------
>>> range(1) == range(1)
True
>>> range(0, 5, 2) == range(0, 5, 2)
True
>>> range(5, 0, -2) == range(5, 0, -2)
True
>>> range(1) == range(2)
False
>>> range(0, 5, 2) == range(0, 5, 3)
False
"""
return all(
getattr(self, attr) == getattr(other, attr)
for attr in self.__slots__
)
else:
range = range
def from_tuple(tup):
"""Convert a tuple into a range with error handling.
Parameters
----------
tup : tuple (len 2 or 3)
The tuple to turn into a range.
Returns
-------
range : range
The range from the tuple.
Raises
------
ValueError
Raised when the tuple length is not 2 or 3.
"""
if len(tup) not in (2, 3):
raise ValueError(
'tuple must contain 2 or 3 elements, not: %d (%r' % (
len(tup),
tup,
),
)
return range(*tup)
def maybe_from_tuple(tup_or_range):
"""Convert a tuple into a range but pass ranges through silently.
This is useful to ensure that input is a range so that attributes may
be accessed with `.start`, `.stop` or so that containment checks are
constant time.
Parameters
----------
tup_or_range : tuple or range
A tuple to pass to from_tuple or a range to return.
Returns
-------
range : range
The input to convert to a range.
Raises
------
ValueError
Raised when the input is not a tuple or a range. ValueError is also
raised if the input is a tuple whose length is not 2 or 3.
"""
if isinstance(tup_or_range, tuple):
return from_tuple(tup_or_range)
elif isinstance(tup_or_range, range):
return tup_or_range
raise ValueError(
'maybe_from_tuple expects a tuple or range, got %r: %r' % (
type(tup_or_range).__name__,
tup_or_range,
),
)
def _check_steps(a, b):
"""Check that the steps of ``a`` and ``b`` are both 1.
Parameters
----------
a : range
The first range to check.
b : range
The second range to check.
Raises
------
ValueError
Raised when either step is not 1.
"""
if a.step != 1:
raise ValueError('a.step must be equal to 1, got: %s' % a.step)
if b.step != 1:
raise ValueError('b.step must be equal to 1, got: %s' % b.step)
def overlap(a, b):
"""Check if two ranges overlap.
Parameters
----------
a : range
The first range.
b : range
The second range.
Returns
-------
overlaps : bool
Do these ranges overlap.
Notes
-----
This function does not support ranges with step != 1.
"""
_check_steps(a, b)
return a.stop >= b.start and b.stop >= a.start
def merge(a, b):
"""Merge two ranges with step == 1.
Parameters
----------
a : range
The first range.
b : range
The second range.
"""
_check_steps(a, b)
return range(min(a.start, b.start), max(a.stop, b.stop))
def _combine(n, rs):
"""helper for ``_group_ranges``
"""
try:
r, rs = peek(rs)
except StopIteration:
yield n
return
if overlap(n, r):
yield merge(n, r)
next(rs)
for r in rs:
yield r
else:
yield n
for r in rs:
yield r
def group_ranges(ranges):
"""Group any overlapping ranges into a single range.
Parameters
----------
ranges : iterable[ranges]
A sorted sequence of ranges to group.
Returns
-------
grouped : iterable[ranges]
A sorted sequence of ranges with overlapping ranges merged together.
"""
return foldr(_combine, ranges, ())
def sorted_diff(rs, ss):
try:
r, rs = peek(rs)
except StopIteration:
return
try:
s, ss = peek(ss)
except StopIteration:
for r in rs:
yield r
return
rtup = (r.start, r.stop)
stup = (s.start, s.stop)
if rtup == stup:
next(rs)
next(ss)
elif rtup < stup:
yield next(rs)
else:
next(ss)
for t in sorted_diff(rs, ss):
yield t
def intersecting_ranges(ranges):
"""Return any ranges that intersect.
Parameters
----------
ranges : iterable[ranges]
A sequence of ranges to check for intersections.
Returns
-------
intersections : iterable[ranges]
A sequence of all of the ranges that intersected in ``ranges``.
Examples
--------
>>> ranges = [range(0, 1), range(2, 5), range(4, 7)]
>>> list(intersecting_ranges(ranges))
[range(2, 5), range(4, 7)]
>>> ranges = [range(0, 1), range(2, 3)]
>>> list(intersecting_ranges(ranges))
[]
>>> ranges = [range(0, 1), range(1, 2)]
>>> list(intersecting_ranges(ranges))
[range(0, 1), range(1, 2)]
"""
ranges = sorted(ranges, key=op.attrgetter('start'))
return sorted_diff(ranges, group_ranges(ranges)) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/range.py | range.py |
import click
import os
import sys
import warnings
from functools import partial
import pandas as pd
try:
from pygments import highlight
from pygments.lexers import PythonLexer
from pygments.formatters import TerminalFormatter
PYGMENTS = True
except ImportError:
PYGMENTS = False
import logbook
import pandas as pd
import six
from toolz import concatv
from trading_calendars import get_calendar
from zipline.data import bundles
from zipline.data.benchmarks import get_benchmark_returns_from_file
from zipline.data.data_portal import DataPortal
from zipline.data.data_portal_live import DataPortalLive
from zipline.finance import metrics
from zipline.finance.trading import SimulationParameters
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.loaders import USEquityPricingLoader
import zipline.utils.paths as pth
from zipline.extensions import load
from zipline.errors import SymbolNotFound
from zipline.algorithm import TradingAlgorithm, NoBenchmark
from zipline.algorithm_live import LiveTradingAlgorithm
from zipline.finance.blotter import Blotter
log = logbook.Logger(__name__)
class _RunAlgoError(click.ClickException, ValueError):
"""Signal an error that should have a different message if invoked from
the cli.
Parameters
----------
pyfunc_msg : str
The message that will be shown when called as a python function.
cmdline_msg : str, optional
The message that will be shown on the command line. If not provided,
this will be the same as ``pyfunc_msg`
"""
exit_code = 1
def __init__(self, pyfunc_msg, cmdline_msg=None):
if cmdline_msg is None:
cmdline_msg = pyfunc_msg
super(_RunAlgoError, self).__init__(cmdline_msg)
self.pyfunc_msg = pyfunc_msg
def __str__(self):
return self.pyfunc_msg
def _run(handle_data,
initialize,
before_trading_start,
analyze,
algofile,
algotext,
defines,
data_frequency,
capital_base,
bundle,
bundle_timestamp,
start,
end,
output,
trading_calendar,
print_algo,
metrics_set,
local_namespace,
environ,
blotter,
benchmark_spec,
broker,
state_filename,
realtime_bar_target,
performance_callback,
stop_execution_callback,
teardown,
execution_id):
"""Run a backtest for the given algorithm.
This is shared between the cli and :func:`zipline.run_algo`.
zipline-trader additions:
broker - wrapper to connect to a real broker
state_filename - saving the context of the algo to be able to restart
performance_callback - a callback to send performance results everyday and not only at the end of the backtest.
this allows to run live, and monitor the performance of the algorithm
stop_execution_callback - A callback to check if execution should be stopped. it is used to be able to stop live
trading (also simulation could be stopped using this) execution. if the callback returns True, then algo
execution will be aborted.
teardown - algo method like handle_data() or before_trading_start() that is called when the algo execution stops
execution_id - unique id to identify this execution (backtest or live instance)
"""
bundle_data = bundles.load(
bundle,
environ,
bundle_timestamp,
)
if trading_calendar is None:
trading_calendar = get_calendar('XNYS')
# date parameter validation
if trading_calendar.session_distance(start, end) < 1:
raise _RunAlgoError(
'There are no trading days between %s and %s' % (
start.date(),
end.date(),
),
)
benchmark_sid, benchmark_returns = benchmark_spec.resolve(
asset_finder=bundle_data.asset_finder,
start_date=start,
end_date=end,
)
emission_rate = 'daily'
if broker:
emission_rate = 'minute'
# if we run zipline as a command line tool, these will probably not be initiated
if not start:
start = pd.Timestamp.utcnow()
if not end:
# in cli mode, sessions are 1 day only. and it will be re-ran each day by user
end = start + pd.Timedelta('1 day')
if algotext is not None:
if local_namespace:
ip = get_ipython() # noqa
namespace = ip.user_ns
else:
namespace = {}
for assign in defines:
try:
name, value = assign.split('=', 2)
except ValueError:
raise ValueError(
'invalid define %r, should be of the form name=value' %
assign,
)
try:
# evaluate in the same namespace so names may refer to
# eachother
namespace[name] = eval(value, namespace)
except Exception as e:
raise ValueError(
'failed to execute definition for name %r: %s' % (name, e),
)
elif defines:
raise _RunAlgoError(
'cannot pass define without `algotext`',
"cannot pass '-D' / '--define' without '-t' / '--algotext'",
)
else:
namespace = {}
if algofile is not None:
algotext = algofile.read()
if print_algo:
if PYGMENTS:
highlight(
algotext,
PythonLexer(),
TerminalFormatter(),
outfile=sys.stdout,
)
else:
click.echo(algotext)
#first_trading_day = \
# bundle_data.equity_minute_bar_reader.first_trading_day
first_trading_day = \
bundle_data.equity_daily_bar_reader.first_trading_day
DataPortalClass = (partial(DataPortalLive, broker)
if broker
else DataPortal)
data = DataPortalClass(
bundle_data.asset_finder,
trading_calendar=trading_calendar,
first_trading_day=first_trading_day,
equity_minute_reader=bundle_data.equity_minute_bar_reader,
equity_daily_reader=bundle_data.equity_daily_bar_reader,
adjustment_reader=bundle_data.adjustment_reader,
)
pipeline_loader = USEquityPricingLoader.without_fx(
bundle_data.equity_daily_bar_reader,
bundle_data.adjustment_reader,
)
def choose_loader(column):
# TODO Domain bypass
return pipeline_loader
if column in USEquityPricing.columns:
return pipeline_loader
raise ValueError(
"No PipelineLoader registered for column %s." % column
)
if isinstance(metrics_set, six.string_types):
try:
metrics_set = metrics.load(metrics_set)
except ValueError as e:
raise _RunAlgoError(str(e))
if isinstance(blotter, six.string_types):
try:
blotter = load(Blotter, blotter)
except ValueError as e:
raise _RunAlgoError(str(e))
TradingAlgorithmClass = (partial(LiveTradingAlgorithm,
broker=broker,
state_filename=state_filename,
realtime_bar_target=realtime_bar_target)
if broker else TradingAlgorithm)
try:
perf = TradingAlgorithmClass(
namespace=namespace,
data_portal=data,
get_pipeline_loader=choose_loader,
trading_calendar=trading_calendar,
sim_params=SimulationParameters(
start_session=start,
end_session=end,
trading_calendar=trading_calendar,
capital_base=capital_base,
emission_rate=emission_rate,
data_frequency=data_frequency,
execution_id=execution_id
),
metrics_set=metrics_set,
blotter=blotter,
benchmark_returns=benchmark_returns,
benchmark_sid=benchmark_sid,
performance_callback=performance_callback,
stop_execution_callback=stop_execution_callback,
**{
'initialize': initialize,
'handle_data': handle_data,
'before_trading_start': before_trading_start,
'analyze': analyze,
'teardown': teardown,
} if algotext is None else {
'algo_filename': getattr(algofile, 'name', '<algorithm>'),
'script': algotext,
}
).run()
except NoBenchmark:
raise _RunAlgoError(
(
'No ``benchmark_spec`` was provided, and'
' ``zipline.api.set_benchmark`` was not called in'
' ``initialize``.'
),
(
"Neither '--benchmark-symbol' nor '--benchmark-sid' was"
" provided, and ``zipline.api.set_benchmark`` was not called"
" in ``initialize``. Did you mean to pass '--no-benchmark'?"
),
)
if output == '-':
click.echo(str(perf))
elif output != os.devnull: # make the zipline magic not write any data
perf.to_pickle(output)
return perf
# All of the loaded extensions. We don't want to load an extension twice.
_loaded_extensions = set()
def load_extensions(default, extensions, strict, environ, reload=False):
"""Load all of the given extensions. This should be called by run_algo
or the cli.
Parameters
----------
default : bool
Load the default exension (~/.zipline/extension.py)?
extension : iterable[str]
The paths to the extensions to load. If the path ends in ``.py`` it is
treated as a script and executed. If it does not end in ``.py`` it is
treated as a module to be imported.
strict : bool
Should failure to load an extension raise. If this is false it will
still warn.
environ : mapping
The environment to use to find the default extension path.
reload : bool, optional
Reload any extensions that have already been loaded.
"""
if default:
default_extension_path = pth.default_extension(environ=environ)
pth.ensure_file(default_extension_path)
# put the default extension first so other extensions can depend on
# the order they are loaded
extensions = concatv([default_extension_path], extensions)
for ext in extensions:
if ext in _loaded_extensions and not reload:
continue
try:
# load all of the zipline extensionss
if ext.endswith('.py'):
with open(ext) as f:
ns = {}
six.exec_(compile(f.read(), ext, 'exec'), ns, ns)
else:
__import__(ext)
except Exception as e:
if strict:
# if `strict` we should raise the actual exception and fail
raise
# without `strict` we should just log the failure
warnings.warn(
'Failed to load extension: %r\n%s' % (ext, e),
stacklevel=2
)
else:
_loaded_extensions.add(ext)
def run_algorithm(start,
end,
initialize,
capital_base,
handle_data=None,
before_trading_start=None,
analyze=None,
teardown=None,
data_frequency='daily',
bundle='quantopian-quandl',
bundle_timestamp=None,
trading_calendar=None,
metrics_set='default',
benchmark_returns=None,
default_extension=True,
extensions=(),
strict_extensions=True,
environ=os.environ,
blotter='default',
broker=None,
performance_callback=None,
stop_execution_callback=None,
execution_id=None,
state_filename=None,
realtime_bar_target=None
):
"""
Run a trading algorithm.
Parameters
----------
start : datetime
The start date of the backtest.
end : datetime
The end date of the backtest..
initialize : callable[context -> None]
The initialize function to use for the algorithm. This is called once
at the very begining of the backtest and should be used to set up
any state needed by the algorithm.
capital_base : float
The starting capital for the backtest.
handle_data : callable[(context, BarData) -> None], optional
The handle_data function to use for the algorithm. This is called
every minute when ``data_frequency == 'minute'`` or every day
when ``data_frequency == 'daily'``.
before_trading_start : callable[(context, BarData) -> None], optional
The before_trading_start function for the algorithm. This is called
once before each trading day (after initialize on the first day).
analyze : callable[(context, pd.DataFrame) -> None], optional
The analyze function to use for the algorithm. This function is called
once at the end of the backtest and is passed the context and the
performance data.
data_frequency : {'daily', 'minute'}, optional
The data frequency to run the algorithm at.
bundle : str, optional
The name of the data bundle to use to load the data to run the backtest
with. This defaults to 'quantopian-quandl'.
bundle_timestamp : datetime, optional
The datetime to lookup the bundle data for. This defaults to the
current time.
trading_calendar : TradingCalendar, optional
The trading calendar to use for your backtest.
metrics_set : iterable[Metric] or str, optional
The set of metrics to compute in the simulation. If a string is passed,
resolve the set with :func:`zipline.finance.metrics.load`.
benchmark_returns : pd.Series, optional
Series of returns to use as the benchmark.
default_extension : bool, optional
Should the default zipline extension be loaded. This is found at
``$ZIPLINE_ROOT/extension.py``
extensions : iterable[str], optional
The names of any other extensions to load. Each element may either be
a dotted module path like ``a.b.c`` or a path to a python file ending
in ``.py`` like ``a/b/c.py``.
strict_extensions : bool, optional
Should the run fail if any extensions fail to load. If this is false,
a warning will be raised instead.
environ : mapping[str -> str], optional
The os environment to use. Many extensions use this to get parameters.
This defaults to ``os.environ``.
blotter : str or zipline.finance.blotter.Blotter, optional
Blotter to use with this algorithm. If passed as a string, we look for
a blotter construction function registered with
``zipline.extensions.register`` and call it with no parameters.
Default is a :class:`zipline.finance.blotter.SimulationBlotter` that
never cancels orders.
broker : instance of zipline.gens.brokers.broker.Broker
performance_callback : a callback to send performance results everyday and not only at the end of the backtest.
this allows to run live, and monitor the performance of the algorithm
stop_execution_callback : A callback to check if execution should be stopped. it is used to be able to stop live
trading (also simulation could be stopped using this) execution. if the callback returns
True, then algo execution will be aborted.
teardown : algo method like handle_data() or before_trading_start() that is called when the algo execution stops
and allows the developer to nicely kill the algo execution
execution_id : unique id to identify this execution instance (backtest or live) will be used to mark and get logs
for this specific execution instance.
state_filename : path to pickle file storing the algorithm "context" (similar to self)
Returns
-------
perf : pd.DataFrame
The daily performance of the algorithm.
See Also
--------
zipline.data.bundles.bundles : The available data bundles.
"""
load_extensions(default_extension, extensions, strict_extensions, environ)
benchmark_spec = BenchmarkSpec.from_returns(benchmark_returns)
return _run(
handle_data=handle_data,
initialize=initialize,
before_trading_start=before_trading_start,
analyze=analyze,
teardown=teardown,
algofile=None,
algotext=None,
defines=(),
data_frequency=data_frequency,
capital_base=capital_base,
bundle=bundle,
bundle_timestamp=bundle_timestamp,
start=start,
end=end,
output=os.devnull,
trading_calendar=trading_calendar,
print_algo=False,
metrics_set=metrics_set,
local_namespace=False,
environ=environ,
blotter=blotter,
benchmark_spec=benchmark_spec,
broker=broker,
state_filename=state_filename,
realtime_bar_target=realtime_bar_target,
performance_callback=performance_callback,
stop_execution_callback=stop_execution_callback,
execution_id=execution_id
)
class BenchmarkSpec(object):
"""
Helper for different ways we can get benchmark data for the Zipline CLI and
zipline.utils.run_algo.run_algorithm.
Parameters
----------
benchmark_returns : pd.Series, optional
Series of returns to use as the benchmark.
benchmark_file : str or file
File containing a csv with `date` and `return` columns, to be read as
the benchmark.
benchmark_sid : int, optional
Sid of the asset to use as a benchmark.
benchmark_symbol : str, optional
Symbol of the asset to use as a benchmark. Symbol will be looked up as
of the end date of the backtest.
no_benchmark : bool
Flag indicating that no benchmark is configured. Benchmark-dependent
metrics will be calculated using a dummy benchmark of all-zero returns.
"""
def __init__(self,
benchmark_returns,
benchmark_file,
benchmark_sid,
benchmark_symbol,
no_benchmark):
self.benchmark_returns = benchmark_returns
self.benchmark_file = benchmark_file
self.benchmark_sid = benchmark_sid
self.benchmark_symbol = benchmark_symbol
self.no_benchmark = no_benchmark
@classmethod
def from_cli_params(cls,
benchmark_sid,
benchmark_symbol,
benchmark_file,
no_benchmark):
return cls(
benchmark_returns=None,
benchmark_sid=benchmark_sid,
benchmark_symbol=benchmark_symbol,
benchmark_file=benchmark_file,
no_benchmark=no_benchmark,
)
@classmethod
def from_returns(cls, benchmark_returns):
return cls(
benchmark_returns=benchmark_returns,
benchmark_file=None,
benchmark_sid=None,
benchmark_symbol=None,
no_benchmark=benchmark_returns is None,
)
def resolve(self, asset_finder, start_date, end_date):
"""
Resolve inputs into values to be passed to TradingAlgorithm.
Returns a pair of ``(benchmark_sid, benchmark_returns)`` with at most
one non-None value. Both values may be None if no benchmark source has
been configured.
Parameters
----------
asset_finder : zipline.assets.AssetFinder
Asset finder for the algorithm to be run.
start_date : pd.Timestamp
Start date of the algorithm to be run.
end_date : pd.Timestamp
End date of the algorithm to be run.
Returns
-------
benchmark_sid : int
Sid to use as benchmark.
benchmark_returns : pd.Series
Series of returns to use as benchmark.
"""
if self.benchmark_returns is not None:
benchmark_sid = None
benchmark_returns = self.benchmark_returns
elif self.benchmark_file is not None:
benchmark_sid = None
benchmark_returns = get_benchmark_returns_from_file(
self.benchmark_file,
)
elif self.benchmark_sid is not None:
benchmark_sid = self.benchmark_sid
benchmark_returns = None
elif self.benchmark_symbol is not None:
try:
asset = asset_finder.lookup_symbol(
self.benchmark_symbol,
as_of_date=end_date,
)
benchmark_sid = asset.sid
benchmark_returns = None
except SymbolNotFound:
raise _RunAlgoError(
"Symbol %r as a benchmark not found in this bundle."
% self.benchmark_symbol
)
elif self.no_benchmark:
benchmark_sid = None
benchmark_returns = self._zero_benchmark_returns(
start_date=start_date,
end_date=end_date,
)
else:
log.warn(
"No benchmark configured. "
"Assuming algorithm calls set_benchmark."
)
log.warn(
"Pass --benchmark-sid, --benchmark-symbol, or"
" --benchmark-file to set a source of benchmark returns."
)
log.warn(
"Pass --no-benchmark to use a dummy benchmark "
"of zero returns.",
)
benchmark_sid = None
benchmark_returns = None
return benchmark_sid, benchmark_returns
@staticmethod
def _zero_benchmark_returns(start_date, end_date):
return pd.Series(
index=pd.date_range(start_date, end_date, tz='utc'),
data=0.0,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/run_algo.py | run_algo.py |
from datetime import tzinfo
from functools import partial
from operator import attrgetter
from numpy import dtype
import pandas as pd
from pytz import timezone
from six import iteritems, string_types, PY3
from toolz import valmap, complement, compose
import toolz.curried.operator as op
from zipline.utils.compat import wraps
from zipline.utils.functional import getattrs
from zipline.utils.preprocess import call, preprocess
if PY3:
_qualified_name = attrgetter('__qualname__')
else:
def _qualified_name(obj):
"""
Return the fully-qualified name (ignoring inner classes) of a type.
"""
# If the obj has an explicitly-set __qualname__, use it.
try:
return getattr(obj, '__qualname__')
except AttributeError:
pass
# If not, build our own __qualname__ as best we can.
module = obj.__module__
if module in ('__builtin__', '__main__', 'builtins'):
return obj.__name__
return '.'.join([module, obj.__name__])
def verify_indices_all_unique(obj):
"""
Check that all axes of a pandas object are unique.
Parameters
----------
obj : pd.Series / pd.DataFrame / pd.Panel
The object to validate.
Returns
-------
obj : pd.Series / pd.DataFrame / pd.Panel
The validated object, unchanged.
Raises
------
ValueError
If any axis has duplicate entries.
"""
axis_names = [
('index',), # Series
('index', 'columns'), # DataFrame
('items', 'major_axis', 'minor_axis') # Panel
][obj.ndim - 1] # ndim = 1 should go to entry 0,
for axis_name, index in zip(axis_names, obj.axes):
if index.is_unique:
continue
raise ValueError(
"Duplicate entries in {type}.{axis}: {dupes}.".format(
type=type(obj).__name__,
axis=axis_name,
dupes=sorted(index[index.duplicated()]),
)
)
return obj
def optionally(preprocessor):
"""Modify a preprocessor to explicitly allow `None`.
Parameters
----------
preprocessor : callable[callable, str, any -> any]
A preprocessor to delegate to when `arg is not None`.
Returns
-------
optional_preprocessor : callable[callable, str, any -> any]
A preprocessor that delegates to `preprocessor` when `arg is not None`.
Examples
--------
>>> def preprocessor(func, argname, arg):
... if not isinstance(arg, int):
... raise TypeError('arg must be int')
... return arg
...
>>> @preprocess(a=optionally(preprocessor))
... def f(a):
... return a
...
>>> f(1) # call with int
1
>>> f('a') # call with not int
Traceback (most recent call last):
...
TypeError: arg must be int
>>> f(None) is None # call with explicit None
True
"""
@wraps(preprocessor)
def wrapper(func, argname, arg):
return arg if arg is None else preprocessor(func, argname, arg)
return wrapper
def ensure_upper_case(func, argname, arg):
if isinstance(arg, string_types):
return arg.upper()
else:
raise TypeError(
"{0}() expected argument '{1}' to"
" be a string, but got {2} instead.".format(
func.__name__,
argname,
arg,
),
)
def ensure_dtype(func, argname, arg):
"""
Argument preprocessor that converts the input into a numpy dtype.
Examples
--------
>>> import numpy as np
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(dtype=ensure_dtype)
... def foo(dtype):
... return dtype
...
>>> foo(float)
dtype('float64')
"""
try:
return dtype(arg)
except TypeError:
raise TypeError(
"{func}() couldn't convert argument "
"{argname}={arg!r} to a numpy dtype.".format(
func=_qualified_name(func),
argname=argname,
arg=arg,
),
)
def ensure_timezone(func, argname, arg):
"""Argument preprocessor that converts the input into a tzinfo object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(tz=ensure_timezone)
... def foo(tz):
... return tz
>>> foo('utc')
<UTC>
"""
if isinstance(arg, tzinfo):
return arg
if isinstance(arg, string_types):
return timezone(arg)
raise TypeError(
"{func}() couldn't convert argument "
"{argname}={arg!r} to a timezone.".format(
func=_qualified_name(func),
argname=argname,
arg=arg,
),
)
def ensure_timestamp(func, argname, arg):
"""Argument preprocessor that converts the input into a pandas Timestamp
object.
Examples
--------
>>> from zipline.utils.preprocess import preprocess
>>> @preprocess(ts=ensure_timestamp)
... def foo(ts):
... return ts
>>> foo('2014-01-01')
Timestamp('2014-01-01 00:00:00')
"""
try:
return pd.Timestamp(arg)
except ValueError as e:
raise TypeError(
"{func}() couldn't convert argument "
"{argname}={arg!r} to a pandas Timestamp.\n"
"Original error was: {t}: {e}".format(
func=_qualified_name(func),
argname=argname,
arg=arg,
t=_qualified_name(type(e)),
e=e,
),
)
def expect_dtypes(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected numpy dtypes.
Examples
--------
>>> from numpy import dtype, arange, int8, float64
>>> @expect_dtypes(x=dtype(int8))
... def foo(x, y):
... return x, y
...
>>> foo(arange(3, dtype=int8), 'foo')
(array([0, 1, 2], dtype=int8), 'foo')
>>> foo(arange(3, dtype=float64), 'foo') # doctest: +NORMALIZE_WHITESPACE
... # doctest: +ELLIPSIS
Traceback (most recent call last):
...
TypeError: ...foo() expected a value with dtype 'int8' for argument 'x',
but got 'float64' instead.
"""
for name, type_ in iteritems(named):
if not isinstance(type_, (dtype, tuple)):
raise TypeError(
"expect_dtypes() expected a numpy dtype or tuple of dtypes"
" for argument {name!r}, but got {dtype} instead.".format(
name=name, dtype=dtype,
)
)
if isinstance(__funcname, str):
def get_funcname(_):
return __funcname
else:
get_funcname = __funcname
@preprocess(dtypes=call(lambda x: x if isinstance(x, tuple) else (x,)))
def _expect_dtype(dtypes):
"""
Factory for dtype-checking functions that work with the @preprocess
decorator.
"""
def error_message(func, argname, value):
# If the bad value has a dtype, but it's wrong, show the dtype
# name. Otherwise just show the value.
try:
value_to_show = value.dtype.name
except AttributeError:
value_to_show = value
return (
"{funcname}() expected a value with dtype {dtype_str} "
"for argument {argname!r}, but got {value!r} instead."
).format(
funcname=get_funcname(func),
dtype_str=' or '.join(repr(d.name) for d in dtypes),
argname=argname,
value=value_to_show,
)
def _actual_preprocessor(func, argname, argvalue):
if getattr(argvalue, 'dtype', object()) not in dtypes:
raise TypeError(error_message(func, argname, argvalue))
return argvalue
return _actual_preprocessor
return preprocess(**valmap(_expect_dtype, named))
def expect_kinds(**named):
"""
Preprocessing decorator that verifies inputs have expected dtype kinds.
Examples
--------
>>> from numpy import int64, int32, float32
>>> @expect_kinds(x='i')
... def foo(x):
... return x
...
>>> foo(int64(2))
2
>>> foo(int32(2))
2
>>> foo(float32(2)) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
TypeError: ...foo() expected a numpy object of kind 'i' for argument 'x',
but got 'f' instead.
"""
for name, kind in iteritems(named):
if not isinstance(kind, (str, tuple)):
raise TypeError(
"expect_dtype_kinds() expected a string or tuple of strings"
" for argument {name!r}, but got {kind} instead.".format(
name=name, kind=dtype,
)
)
@preprocess(kinds=call(lambda x: x if isinstance(x, tuple) else (x,)))
def _expect_kind(kinds):
"""
Factory for kind-checking functions that work the @preprocess
decorator.
"""
def error_message(func, argname, value):
# If the bad value has a dtype, but it's wrong, show the dtype
# kind. Otherwise just show the value.
try:
value_to_show = value.dtype.kind
except AttributeError:
value_to_show = value
return (
"{funcname}() expected a numpy object of kind {kinds} "
"for argument {argname!r}, but got {value!r} instead."
).format(
funcname=_qualified_name(func),
kinds=' or '.join(map(repr, kinds)),
argname=argname,
value=value_to_show,
)
def _actual_preprocessor(func, argname, argvalue):
if getattrs(argvalue, ('dtype', 'kind'), object()) not in kinds:
raise TypeError(error_message(func, argname, argvalue))
return argvalue
return _actual_preprocessor
return preprocess(**valmap(_expect_kind, named))
def expect_types(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs have expected types.
Examples
--------
>>> @expect_types(x=int, y=str)
... def foo(x, y):
... return x, y
...
>>> foo(2, '3')
(2, '3')
>>> foo(2.0, '3') # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
TypeError: ...foo() expected a value of type int for argument 'x',
but got float instead.
Notes
-----
A special argument, __funcname, can be provided as a string to override the
function name shown in error messages. This is most often used on __init__
or __new__ methods to make errors refer to the class name instead of the
function name.
"""
for name, type_ in iteritems(named):
if not isinstance(type_, (type, tuple)):
raise TypeError(
"expect_types() expected a type or tuple of types for "
"argument '{name}', but got {type_} instead.".format(
name=name, type_=type_,
)
)
def _expect_type(type_):
# Slightly different messages for type and tuple of types.
_template = (
"%(funcname)s() expected a value of type {type_or_types} "
"for argument '%(argname)s', but got %(actual)s instead."
)
if isinstance(type_, tuple):
template = _template.format(
type_or_types=' or '.join(map(_qualified_name, type_))
)
else:
template = _template.format(type_or_types=_qualified_name(type_))
return make_check(
exc_type=TypeError,
template=template,
pred=lambda v: not isinstance(v, type_),
actual=compose(_qualified_name, type),
funcname=__funcname,
)
return preprocess(**valmap(_expect_type, named))
def make_check(exc_type, template, pred, actual, funcname):
"""
Factory for making preprocessing functions that check a predicate on the
input value.
Parameters
----------
exc_type : Exception
The exception type to raise if the predicate fails.
template : str
A template string to use to create error messages.
Should have %-style named template parameters for 'funcname',
'argname', and 'actual'.
pred : function[object -> bool]
A function to call on the argument being preprocessed. If the
predicate returns `True`, we raise an instance of `exc_type`.
actual : function[object -> object]
A function to call on bad values to produce the value to display in the
error message.
funcname : str or callable
Name to use in error messages, or function to call on decorated
functions to produce a name. Passing an explicit name is useful when
creating checks for __init__ or __new__ methods when you want the error
to refer to the class name instead of the method name.
"""
if isinstance(funcname, str):
def get_funcname(_):
return funcname
else:
get_funcname = funcname
def _check(func, argname, argvalue):
if pred(argvalue):
raise exc_type(
template % {
'funcname': get_funcname(func),
'argname': argname,
'actual': actual(argvalue),
},
)
return argvalue
return _check
def optional(type_):
"""
Helper for use with `expect_types` when an input can be `type_` or `None`.
Returns an object such that both `None` and instances of `type_` pass
checks of the form `isinstance(obj, optional(type_))`.
Parameters
----------
type_ : type
Type for which to produce an option.
Examples
--------
>>> isinstance({}, optional(dict))
True
>>> isinstance(None, optional(dict))
True
>>> isinstance(1, optional(dict))
False
"""
return (type_, type(None))
def expect_element(__funcname=_qualified_name, **named):
"""
Preprocessing decorator that verifies inputs are elements of some
expected collection.
Examples
--------
>>> @expect_element(x=('a', 'b'))
... def foo(x):
... return x.upper()
...
>>> foo('a')
'A'
>>> foo('b')
'B'
>>> foo('c') # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
ValueError: ...foo() expected a value in ('a', 'b') for argument 'x',
but got 'c' instead.
Notes
-----
A special argument, __funcname, can be provided as a string to override the
function name shown in error messages. This is most often used on __init__
or __new__ methods to make errors refer to the class name instead of the
function name.
This uses the `in` operator (__contains__) to make the containment check.
This allows us to use any custom container as long as the object supports
the container protocol.
"""
def _expect_element(collection):
if isinstance(collection, (set, frozenset)):
# Special case the error message for set and frozen set to make it
# less verbose.
collection_for_error_message = tuple(sorted(collection))
else:
collection_for_error_message = collection
template = (
"%(funcname)s() expected a value in {collection} "
"for argument '%(argname)s', but got %(actual)s instead."
).format(collection=collection_for_error_message)
return make_check(
ValueError,
template,
complement(op.contains(collection)),
repr,
funcname=__funcname,
)
return preprocess(**valmap(_expect_element, named))
def expect_bounded(__funcname=_qualified_name, **named):
"""
Preprocessing decorator verifying that inputs fall INCLUSIVELY between
bounds.
Bounds should be passed as a pair of ``(min_value, max_value)``.
``None`` may be passed as ``min_value`` or ``max_value`` to signify that
the input is only bounded above or below.
Examples
--------
>>> @expect_bounded(x=(1, 5))
... def foo(x):
... return x + 1
...
>>> foo(1)
2
>>> foo(5)
6
>>> foo(6) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
ValueError: ...foo() expected a value inclusively between 1 and 5 for
argument 'x', but got 6 instead.
>>> @expect_bounded(x=(2, None))
... def foo(x):
... return x
...
>>> foo(100000)
100000
>>> foo(1) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
ValueError: ...foo() expected a value greater than or equal to 2 for
argument 'x', but got 1 instead.
>>> @expect_bounded(x=(None, 5))
... def foo(x):
... return x
...
>>> foo(6) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
ValueError: ...foo() expected a value less than or equal to 5 for
argument 'x', but got 6 instead.
"""
def _make_bounded_check(bounds):
(lower, upper) = bounds
if lower is None:
def should_fail(value):
return value > upper
predicate_descr = "less than or equal to " + str(upper)
elif upper is None:
def should_fail(value):
return value < lower
predicate_descr = "greater than or equal to " + str(lower)
else:
def should_fail(value):
return not (lower <= value <= upper)
predicate_descr = "inclusively between %s and %s" % bounds
template = (
"%(funcname)s() expected a value {predicate}"
" for argument '%(argname)s', but got %(actual)s instead."
).format(predicate=predicate_descr)
return make_check(
exc_type=ValueError,
template=template,
pred=should_fail,
actual=repr,
funcname=__funcname,
)
return _expect_bounded(_make_bounded_check, __funcname=__funcname, **named)
def expect_strictly_bounded(__funcname=_qualified_name, **named):
"""
Preprocessing decorator verifying that inputs fall EXCLUSIVELY between
bounds.
Bounds should be passed as a pair of ``(min_value, max_value)``.
``None`` may be passed as ``min_value`` or ``max_value`` to signify that
the input is only bounded above or below.
Examples
--------
>>> @expect_strictly_bounded(x=(1, 5))
... def foo(x):
... return x + 1
...
>>> foo(2)
3
>>> foo(4)
5
>>> foo(5) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
ValueError: ...foo() expected a value exclusively between 1 and 5 for
argument 'x', but got 5 instead.
>>> @expect_strictly_bounded(x=(2, None))
... def foo(x):
... return x
...
>>> foo(100000)
100000
>>> foo(2) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
ValueError: ...foo() expected a value strictly greater than 2 for
argument 'x', but got 2 instead.
>>> @expect_strictly_bounded(x=(None, 5))
... def foo(x):
... return x
...
>>> foo(5) # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
Traceback (most recent call last):
...
ValueError: ...foo() expected a value strictly less than 5 for
argument 'x', but got 5 instead.
"""
def _make_bounded_check(bounds):
(lower, upper) = bounds
if lower is None:
def should_fail(value):
return value >= upper
predicate_descr = "strictly less than " + str(upper)
elif upper is None:
def should_fail(value):
return value <= lower
predicate_descr = "strictly greater than " + str(lower)
else:
def should_fail(value):
return not (lower < value < upper)
predicate_descr = "exclusively between %s and %s" % bounds
template = (
"%(funcname)s() expected a value {predicate}"
" for argument '%(argname)s', but got %(actual)s instead."
).format(predicate=predicate_descr)
return make_check(
exc_type=ValueError,
template=template,
pred=should_fail,
actual=repr,
funcname=__funcname,
)
return _expect_bounded(_make_bounded_check, __funcname=__funcname, **named)
def _expect_bounded(make_bounded_check, __funcname, **named):
def valid_bounds(t):
return (
isinstance(t, tuple)
and len(t) == 2
and t != (None, None)
)
for name, bounds in iteritems(named):
if not valid_bounds(bounds):
raise TypeError(
"expect_bounded() expected a tuple of bounds for"
" argument '{name}', but got {bounds} instead.".format(
name=name,
bounds=bounds,
)
)
return preprocess(**valmap(make_bounded_check, named))
def expect_dimensions(__funcname=_qualified_name, **dimensions):
"""
Preprocessing decorator that verifies inputs are numpy arrays with a
specific dimensionality.
Examples
--------
>>> from numpy import array
>>> @expect_dimensions(x=1, y=2)
... def foo(x, y):
... return x[0] + y[0, 0]
...
>>> foo(array([1, 1]), array([[1, 1], [2, 2]]))
2
>>> foo(array([1, 1]), array([1, 1])) # doctest: +NORMALIZE_WHITESPACE
... # doctest: +ELLIPSIS
Traceback (most recent call last):
...
ValueError: ...foo() expected a 2-D array for argument 'y',
but got a 1-D array instead.
"""
if isinstance(__funcname, str):
def get_funcname(_):
return __funcname
else:
get_funcname = __funcname
def _expect_dimension(expected_ndim):
def _check(func, argname, argvalue):
actual_ndim = argvalue.ndim
if actual_ndim != expected_ndim:
if actual_ndim == 0:
actual_repr = 'scalar'
else:
actual_repr = "%d-D array" % actual_ndim
raise ValueError(
"{func}() expected a {expected:d}-D array"
" for argument {argname!r}, but got a {actual}"
" instead.".format(
func=get_funcname(func),
expected=expected_ndim,
argname=argname,
actual=actual_repr,
)
)
return argvalue
return _check
return preprocess(**valmap(_expect_dimension, dimensions))
def coerce(from_, to, **to_kwargs):
"""
A preprocessing decorator that coerces inputs of a given type by passing
them to a callable.
Parameters
----------
from : type or tuple or types
Inputs types on which to call ``to``.
to : function
Coercion function to call on inputs.
**to_kwargs
Additional keywords to forward to every call to ``to``.
Examples
--------
>>> @preprocess(x=coerce(float, int), y=coerce(float, int))
... def floordiff(x, y):
... return x - y
...
>>> floordiff(3.2, 2.5)
1
>>> @preprocess(x=coerce(str, int, base=2), y=coerce(str, int, base=2))
... def add_binary_strings(x, y):
... return bin(x + y)[2:]
...
>>> add_binary_strings('101', '001')
'110'
"""
def preprocessor(func, argname, arg):
if isinstance(arg, from_):
return to(arg, **to_kwargs)
return arg
return preprocessor
def coerce_types(**kwargs):
"""
Preprocessing decorator that applies type coercions.
Parameters
----------
**kwargs : dict[str -> (type, callable)]
Keyword arguments mapping function parameter names to pairs of
(from_type, to_type).
Examples
--------
>>> @coerce_types(x=(float, int), y=(int, str))
... def func(x, y):
... return (x, y)
...
>>> func(1.0, 3)
(1, '3')
"""
def _coerce(types):
return coerce(*types)
return preprocess(**valmap(_coerce, kwargs))
class error_keywords(object):
def __init__(self, *args, **kwargs):
self.messages = kwargs
def __call__(self, func):
@wraps(func)
def assert_keywords_and_call(*args, **kwargs):
for field, message in iteritems(self.messages):
if field in kwargs:
raise TypeError(message)
return func(*args, **kwargs)
return assert_keywords_and_call
coerce_string = partial(coerce, string_types)
def validate_keys(dict_, expected, funcname):
"""Validate that a dictionary has an expected set of keys.
"""
expected = set(expected)
received = set(dict_)
missing = expected - received
if missing:
raise ValueError(
"Missing keys in {}:\n"
"Expected Keys: {}\n"
"Received Keys: {}".format(
funcname,
sorted(expected),
sorted(received),
)
)
unexpected = received - expected
if unexpected:
raise ValueError(
"Unexpected keys in {}:\n"
"Expected Keys: {}\n"
"Received Keys: {}".format(
funcname,
sorted(expected),
sorted(received),
)
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/input_validation.py | input_validation.py |
from textwrap import dedent
from types import CodeType
from uuid import uuid4
from toolz.curried.operator import getitem
from six import viewkeys, exec_, PY3
from zipline.utils.compat import getargspec, wraps
_code_argorder = (
('co_argcount', 'co_kwonlyargcount') if PY3 else ('co_argcount',)
) + (
'co_nlocals',
'co_stacksize',
'co_flags',
'co_code',
'co_consts',
'co_names',
'co_varnames',
'co_filename',
'co_name',
'co_firstlineno',
'co_lnotab',
'co_freevars',
'co_cellvars',
)
NO_DEFAULT = object()
def preprocess(*_unused, **processors):
"""
Decorator that applies pre-processors to the arguments of a function before
calling the function.
Parameters
----------
**processors : dict
Map from argument name -> processor function.
A processor function takes three arguments: (func, argname, argvalue).
`func` is the the function for which we're processing args.
`argname` is the name of the argument we're processing.
`argvalue` is the value of the argument we're processing.
Examples
--------
>>> def _ensure_tuple(func, argname, arg):
... if isinstance(arg, tuple):
... return argvalue
... try:
... return tuple(arg)
... except TypeError:
... raise TypeError(
... "%s() expected argument '%s' to"
... " be iterable, but got %s instead." % (
... func.__name__, argname, arg,
... )
... )
...
>>> @preprocess(arg=_ensure_tuple)
... def foo(arg):
... return arg
...
>>> foo([1, 2, 3])
(1, 2, 3)
>>> foo("a")
('a',)
>>> foo(2)
Traceback (most recent call last):
...
TypeError: foo() expected argument 'arg' to be iterable, but got 2 instead.
"""
if _unused:
raise TypeError("preprocess() doesn't accept positional arguments")
def _decorator(f):
args, varargs, varkw, defaults = argspec = getargspec(f)
if defaults is None:
defaults = ()
no_defaults = (NO_DEFAULT,) * (len(args) - len(defaults))
args_defaults = list(zip(args, no_defaults + defaults))
if varargs:
args_defaults.append((varargs, NO_DEFAULT))
if varkw:
args_defaults.append((varkw, NO_DEFAULT))
argset = set(args) | {varargs, varkw} - {None}
# Arguments can be declared as tuples in Python 2.
if not all(isinstance(arg, str) for arg in args):
raise TypeError(
"Can't validate functions using tuple unpacking: %s" %
(argspec,)
)
# Ensure that all processors map to valid names.
bad_names = viewkeys(processors) - argset
if bad_names:
raise TypeError(
"Got processors for unknown arguments: %s." % bad_names
)
return _build_preprocessed_function(
f, processors, args_defaults, varargs, varkw,
)
return _decorator
def call(f):
"""
Wrap a function in a processor that calls `f` on the argument before
passing it along.
Useful for creating simple arguments to the `@preprocess` decorator.
Parameters
----------
f : function
Function accepting a single argument and returning a replacement.
Examples
--------
>>> @preprocess(x=call(lambda x: x + 1))
... def foo(x):
... return x
...
>>> foo(1)
2
"""
@wraps(f)
def processor(func, argname, arg):
return f(arg)
return processor
def _build_preprocessed_function(func,
processors,
args_defaults,
varargs,
varkw):
"""
Build a preprocessed function with the same signature as `func`.
Uses `exec` internally to build a function that actually has the same
signature as `func.
"""
format_kwargs = {'func_name': func.__name__}
def mangle(name):
return 'a' + uuid4().hex + name
format_kwargs['mangled_func'] = mangled_funcname = mangle(func.__name__)
def make_processor_assignment(arg, processor_name):
template = "{arg} = {processor}({func}, '{arg}', {arg})"
return template.format(
arg=arg,
processor=processor_name,
func=mangled_funcname,
)
exec_globals = {mangled_funcname: func, 'wraps': wraps}
defaults_seen = 0
default_name_template = 'a' + uuid4().hex + '_%d'
signature = []
call_args = []
assignments = []
star_map = {
varargs: '*',
varkw: '**',
}
def name_as_arg(arg):
return star_map.get(arg, '') + arg
for arg, default in args_defaults:
if default is NO_DEFAULT:
signature.append(name_as_arg(arg))
else:
default_name = default_name_template % defaults_seen
exec_globals[default_name] = default
signature.append('='.join([name_as_arg(arg), default_name]))
defaults_seen += 1
if arg in processors:
procname = mangle('_processor_' + arg)
exec_globals[procname] = processors[arg]
assignments.append(make_processor_assignment(arg, procname))
call_args.append(name_as_arg(arg))
exec_str = dedent(
"""\
@wraps({wrapped_funcname})
def {func_name}({signature}):
{assignments}
return {wrapped_funcname}({call_args})
"""
).format(
func_name=func.__name__,
signature=', '.join(signature),
assignments='\n '.join(assignments),
wrapped_funcname=mangled_funcname,
call_args=', '.join(call_args),
)
compiled = compile(
exec_str,
func.__code__.co_filename,
mode='exec',
)
exec_locals = {}
exec_(compiled, exec_globals, exec_locals)
new_func = exec_locals[func.__name__]
code = new_func.__code__
args = {
attr: getattr(code, attr)
for attr in dir(code)
if attr.startswith('co_')
}
# Copy the firstlineno out of the underlying function so that exceptions
# get raised with the correct traceback.
# This also makes dynamic source inspection (like IPython `??` operator)
# work as intended.
try:
# Try to get the pycode object from the underlying function.
original_code = func.__code__
except AttributeError:
try:
# The underlying callable was not a function, try to grab the
# `__func__.__code__` which exists on method objects.
original_code = func.__func__.__code__
except AttributeError:
# The underlying callable does not have a `__code__`. There is
# nothing for us to correct.
return new_func
args['co_firstlineno'] = original_code.co_firstlineno
new_func.__code__ = CodeType(*map(getitem(args), _code_argorder))
return new_func | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/preprocess.py | preprocess.py |
from abc import ABCMeta, abstractmethod
from collections import namedtuple
import six
import warnings
import datetime
import numpy as np
import pandas as pd
import pytz
from toolz import curry
from zipline.utils.input_validation import preprocess
from zipline.utils.memoize import lazyval
from zipline.utils.sentinel import sentinel
from .context_tricks import nop_context
__all__ = [
'EventManager',
'Event',
'EventRule',
'StatelessRule',
'ComposedRule',
'Always',
'Never',
'AfterOpen',
'BeforeClose',
'NotHalfDay',
'NthTradingDayOfWeek',
'NDaysBeforeLastTradingDayOfWeek',
'NthTradingDayOfMonth',
'NDaysBeforeLastTradingDayOfMonth',
'StatefulRule',
'OncePerDay',
# Factory API
'date_rules',
'time_rules',
'calendars',
'make_eventrule',
]
MAX_MONTH_RANGE = 23
MAX_WEEK_RANGE = 5
def naive_to_utc(ts):
"""
Converts a UTC tz-naive timestamp to a tz-aware timestamp.
"""
# Drop the nanoseconds field. warn=False suppresses the warning
# that we are losing the nanoseconds; however, this is intended.
return pd.Timestamp(ts.to_pydatetime(warn=False), tz='UTC')
def ensure_utc(time, tz='UTC'):
"""
Normalize a time. If the time is tz-naive, assume it is UTC.
"""
if not time.tzinfo:
time = time.replace(tzinfo=pytz.timezone(tz))
return time.replace(tzinfo=pytz.utc)
def _out_of_range_error(a, b=None, var='offset'):
start = 0
if b is None:
end = a - 1
else:
start = a
end = b - 1
return ValueError(
'{var} must be in between {start} and {end} inclusive'.format(
var=var,
start=start,
end=end,
)
)
def _td_check(td):
seconds = td.total_seconds()
# 43200 seconds = 12 hours
if 60 <= seconds <= 43200:
return td
else:
raise ValueError('offset must be in between 1 minute and 12 hours, '
'inclusive.')
def _build_offset(offset, kwargs, default):
"""
Builds the offset argument for event rules.
"""
# Filter down to just kwargs that were actually passed.
kwargs = {k: v for k, v in six.iteritems(kwargs) if v is not None}
if offset is None:
if not kwargs:
return default # use the default.
else:
return _td_check(datetime.timedelta(**kwargs))
elif kwargs:
raise ValueError('Cannot pass kwargs and an offset')
elif isinstance(offset, datetime.timedelta):
return _td_check(offset)
else:
raise TypeError("Must pass 'hours' and/or 'minutes' as keywords")
def _build_date(date, kwargs):
"""
Builds the date argument for event rules.
"""
if date is None:
if not kwargs:
raise ValueError('Must pass a date or kwargs')
else:
return datetime.date(**kwargs)
elif kwargs:
raise ValueError('Cannot pass kwargs and a date')
else:
return date
def _build_time(time, kwargs):
"""
Builds the time argument for event rules.
"""
tz = kwargs.pop('tz', 'UTC')
if time:
if kwargs:
raise ValueError('Cannot pass kwargs and a time')
else:
return ensure_utc(time, tz)
elif not kwargs:
raise ValueError('Must pass a time or kwargs')
else:
return datetime.time(**kwargs)
@curry
def lossless_float_to_int(funcname, func, argname, arg):
"""
A preprocessor that coerces integral floats to ints.
Receipt of non-integral floats raises a TypeError.
"""
if not isinstance(arg, float):
return arg
arg_as_int = int(arg)
if arg == arg_as_int:
warnings.warn(
"{f} expected an int for argument {name!r}, but got float {arg}."
" Coercing to int.".format(
f=funcname,
name=argname,
arg=arg,
),
)
return arg_as_int
raise TypeError(arg)
class EventManager(object):
"""Manages a list of Event objects.
This manages the logic for checking the rules and dispatching to the
handle_data function of the Events.
Parameters
----------
create_context : (BarData) -> context manager, optional
An optional callback to produce a context manager to wrap the calls
to handle_data. This will be passed the current BarData.
"""
def __init__(self, create_context=None):
self._events = []
self._create_context = (
create_context
if create_context is not None else
lambda *_: nop_context
)
def add_event(self, event, prepend=False):
"""
Adds an event to the manager.
"""
if prepend:
self._events.insert(0, event)
else:
self._events.append(event)
def handle_data(self, context, data, dt):
with self._create_context(data):
for event in self._events:
event.handle_data(
context,
data,
dt,
)
class Event(namedtuple('Event', ['rule', 'callback'])):
"""
An event is a pairing of an EventRule and a callable that will be invoked
with the current algorithm context, data, and datetime only when the rule
is triggered.
"""
def __new__(cls, rule, callback=None):
callback = callback or (lambda *args, **kwargs: None)
return super(cls, cls).__new__(cls, rule=rule, callback=callback)
def handle_data(self, context, data, dt):
"""
Calls the callable only when the rule is triggered.
"""
if self.rule.should_trigger(dt):
self.callback(context, data)
class EventRule(six.with_metaclass(ABCMeta)):
"""A rule defining when a scheduled function should execute.
"""
# Instances of EventRule are assigned a calendar instance when scheduling
# a function.
_cal = None
@property
def cal(self):
return self._cal
@cal.setter
def cal(self, value):
self._cal = value
@abstractmethod
def should_trigger(self, dt):
"""
Checks if the rule should trigger with its current state.
This method should be pure and NOT mutate any state on the object.
"""
raise NotImplementedError('should_trigger')
class StatelessRule(EventRule):
"""
A stateless rule has no observable side effects.
This is reentrant and will always give the same result for the
same datetime.
Because these are pure, they can be composed to create new rules.
"""
def and_(self, rule):
"""
Logical and of two rules, triggers only when both rules trigger.
This follows the short circuiting rules for normal and.
"""
return ComposedRule(self, rule, ComposedRule.lazy_and)
__and__ = and_
class ComposedRule(StatelessRule):
"""
A rule that composes the results of two rules with some composing function.
The composing function should be a binary function that accepts the results
first(dt) and second(dt) as positional arguments.
For example, operator.and_.
If lazy=True, then the lazy composer is used instead. The lazy composer
expects a function that takes the two should_trigger functions and the
datetime. This is useful of you don't always want to call should_trigger
for one of the rules. For example, this is used to implement the & and |
operators so that they will have the same short circuit logic that is
expected.
"""
def __init__(self, first, second, composer):
if not (isinstance(first, StatelessRule) and
isinstance(second, StatelessRule)):
raise ValueError('Only two StatelessRules can be composed')
self.first = first
self.second = second
self.composer = composer
def should_trigger(self, dt):
"""
Composes the two rules with a lazy composer.
"""
return self.composer(
self.first.should_trigger,
self.second.should_trigger,
dt
)
@staticmethod
def lazy_and(first_should_trigger, second_should_trigger, dt):
"""
Lazily ands the two rules. This will NOT call the should_trigger of the
second rule if the first one returns False.
"""
return first_should_trigger(dt) and second_should_trigger(dt)
@property
def cal(self):
return self.first.cal
@cal.setter
def cal(self, value):
# Thread the calendar through to the underlying rules.
self.first.cal = self.second.cal = value
class Always(StatelessRule):
"""
A rule that always triggers.
"""
@staticmethod
def always_trigger(dt):
"""
A should_trigger implementation that will always trigger.
"""
return True
should_trigger = always_trigger
class Never(StatelessRule):
"""
A rule that never triggers.
"""
@staticmethod
def never_trigger(dt):
"""
A should_trigger implementation that will never trigger.
"""
return False
should_trigger = never_trigger
class AfterOpen(StatelessRule):
"""
A rule that triggers for some offset after the market opens.
Example that triggers after 30 minutes of the market opening:
>>> AfterOpen(minutes=30) # doctest: +ELLIPSIS
<zipline.utils.events.AfterOpen object at ...>
"""
def __init__(self, offset=None, **kwargs):
self.offset = _build_offset(
offset,
kwargs,
datetime.timedelta(minutes=1), # Defaults to the first minute.
)
self._period_start = None
self._period_end = None
self._period_close = None
self._one_minute = datetime.timedelta(minutes=1)
def calculate_dates(self, dt):
"""
Given a date, find that day's open and period end (open + offset).
"""
period_start, period_close = self.cal.open_and_close_for_session(
self.cal.minute_to_session_label(dt),
)
# Align the market open and close times here with the execution times
# used by the simulation clock. This ensures that scheduled functions
# trigger at the correct times.
self._period_start = self.cal.execution_time_from_open(period_start)
self._period_close = self.cal.execution_time_from_close(period_close)
self._period_end = self._period_start + self.offset - self._one_minute
def should_trigger(self, dt):
# There are two reasons why we might want to recalculate the dates.
# One is the first time we ever call should_trigger, when
# self._period_start is none. The second is when we're on a new day,
# and need to recalculate the dates. For performance reasons, we rely
# on the fact that our clock only ever ticks forward, since it's
# cheaper to do dt1 <= dt2 than dt1.date() != dt2.date(). This means
# that we will NOT correctly recognize a new date if we go backwards
# in time(which should never happen in a simulation, or in live
# trading)
if (
self._period_start is None or
self._period_close <= dt
):
self.calculate_dates(dt)
return dt == self._period_end
class BeforeClose(StatelessRule):
"""
A rule that triggers for some offset time before the market closes.
Example that triggers for the last 30 minutes every day:
>>> BeforeClose(minutes=30) # doctest: +ELLIPSIS
<zipline.utils.events.BeforeClose object at ...>
"""
def __init__(self, offset=None, **kwargs):
self.offset = _build_offset(
offset,
kwargs,
datetime.timedelta(minutes=1), # Defaults to the last minute.
)
self._period_start = None
self._period_close = None
self._period_end = None
self._one_minute = datetime.timedelta(minutes=1)
def calculate_dates(self, dt):
"""
Given a dt, find that day's close and period start (close - offset).
"""
period_end = self.cal.open_and_close_for_session(
self.cal.minute_to_session_label(dt),
)[1]
# Align the market close time here with the execution time used by the
# simulation clock. This ensures that scheduled functions trigger at
# the correct times.
self._period_end = self.cal.execution_time_from_close(period_end)
self._period_start = self._period_end - self.offset
self._period_close = self._period_end
def should_trigger(self, dt):
# There are two reasons why we might want to recalculate the dates.
# One is the first time we ever call should_trigger, when
# self._period_start is none. The second is when we're on a new day,
# and need to recalculate the dates. For performance reasons, we rely
# on the fact that our clock only ever ticks forward, since it's
# cheaper to do dt1 <= dt2 than dt1.date() != dt2.date(). This means
# that we will NOT correctly recognize a new date if we go backwards
# in time(which should never happen in a simulation, or in live
# trading)
if self._period_start is None or self._period_close <= dt:
self.calculate_dates(dt)
return self._period_start == dt
class NotHalfDay(StatelessRule):
"""
A rule that only triggers when it is not a half day.
"""
def should_trigger(self, dt):
return self.cal.minute_to_session_label(dt) \
not in self.cal.early_closes
class TradingDayOfWeekRule(six.with_metaclass(ABCMeta, StatelessRule)):
@preprocess(n=lossless_float_to_int('TradingDayOfWeekRule'))
def __init__(self, n, invert):
if not 0 <= n < MAX_WEEK_RANGE:
raise _out_of_range_error(MAX_WEEK_RANGE)
self.td_delta = (-n - 1) if invert else n
def should_trigger(self, dt):
# is this market minute's period in the list of execution periods?
val = self.cal.minute_to_session_label(dt, direction="none").value
return val in self.execution_period_values
@lazyval
def execution_period_values(self):
# calculate the list of periods that match the given criteria
sessions = self.cal.all_sessions
return set(
pd.Series(data=sessions)
# Group by ISO year (0) and week (1)
.groupby(sessions.map(lambda x: x.isocalendar()[0:2]))
.nth(self.td_delta)
.astype(np.int64)
)
class NthTradingDayOfWeek(TradingDayOfWeekRule):
"""
A rule that triggers on the nth trading day of the week.
This is zero-indexed, n=0 is the first trading day of the week.
"""
def __init__(self, n):
super(NthTradingDayOfWeek, self).__init__(n, invert=False)
class NDaysBeforeLastTradingDayOfWeek(TradingDayOfWeekRule):
"""
A rule that triggers n days before the last trading day of the week.
"""
def __init__(self, n):
super(NDaysBeforeLastTradingDayOfWeek, self).__init__(n, invert=True)
class TradingDayOfMonthRule(six.with_metaclass(ABCMeta, StatelessRule)):
@preprocess(n=lossless_float_to_int('TradingDayOfMonthRule'))
def __init__(self, n, invert):
if not 0 <= n < MAX_MONTH_RANGE:
raise _out_of_range_error(MAX_MONTH_RANGE)
if invert:
self.td_delta = -n - 1
else:
self.td_delta = n
def should_trigger(self, dt):
# is this market minute's period in the list of execution periods?
value = self.cal.minute_to_session_label(dt, direction="none").value
return value in self.execution_period_values
@lazyval
def execution_period_values(self):
# calculate the list of periods that match the given criteria
sessions = self.cal.all_sessions
return set(
pd.Series(data=sessions)
.groupby([sessions.year, sessions.month])
.nth(self.td_delta)
.astype(np.int64)
)
class NthTradingDayOfMonth(TradingDayOfMonthRule):
"""
A rule that triggers on the nth trading day of the month.
This is zero-indexed, n=0 is the first trading day of the month.
"""
def __init__(self, n):
super(NthTradingDayOfMonth, self).__init__(n, invert=False)
class NDaysBeforeLastTradingDayOfMonth(TradingDayOfMonthRule):
"""
A rule that triggers n days before the last trading day of the month.
"""
def __init__(self, n):
super(NDaysBeforeLastTradingDayOfMonth, self).__init__(n, invert=True)
# Stateful rules
class StatefulRule(EventRule):
"""
A stateful rule has state.
This rule will give different results for the same datetimes depending
on the internal state that this holds.
StatefulRules wrap other rules as state transformers.
"""
def __init__(self, rule=None):
self.rule = rule or Always()
@property
def cal(self):
return self.rule.cal
@cal.setter
def cal(self, value):
# Thread the calendar through to the underlying rule.
self.rule.cal = value
class OncePerDay(StatefulRule):
def __init__(self, rule=None):
self.triggered = False
self.date = None
self.next_date = None
super(OncePerDay, self).__init__(rule)
def should_trigger(self, dt):
if self.date is None or dt >= self.next_date:
# initialize or reset for new date
self.triggered = False
self.date = dt
# record the timestamp for the next day, so that we can use it
# to know if we've moved to the next day
self.next_date = dt + pd.Timedelta(1, unit="d")
if not self.triggered and self.rule.should_trigger(dt):
self.triggered = True
return True
# Factory API
class date_rules(object):
"""
Factories for date-based :func:`~zipline.api.schedule_function` rules.
See Also
--------
:func:`~zipline.api.schedule_function`
"""
@staticmethod
def every_day():
"""Create a rule that triggers every day.
Returns
-------
rule : zipline.utils.events.EventRule
"""
return Always()
@staticmethod
def month_start(days_offset=0):
"""
Create a rule that triggers a fixed number of trading days after the
start of each month.
Parameters
----------
days_offset : int, optional
Number of trading days to wait before triggering each
month. Default is 0, i.e., trigger on the first trading day of the
month.
Returns
-------
rule : zipline.utils.events.EventRule
"""
return NthTradingDayOfMonth(n=days_offset)
@staticmethod
def month_end(days_offset=0):
"""
Create a rule that triggers a fixed number of trading days before the
end of each month.
Parameters
----------
days_offset : int, optional
Number of trading days prior to month end to trigger. Default is 0,
i.e., trigger on the last day of the month.
Returns
-------
rule : zipline.utils.events.EventRule
"""
return NDaysBeforeLastTradingDayOfMonth(n=days_offset)
@staticmethod
def week_start(days_offset=0):
"""
Create a rule that triggers a fixed number of trading days after the
start of each week.
Parameters
----------
days_offset : int, optional
Number of trading days to wait before triggering each week. Default
is 0, i.e., trigger on the first trading day of the week.
"""
return NthTradingDayOfWeek(n=days_offset)
@staticmethod
def week_end(days_offset=0):
"""
Create a rule that triggers a fixed number of trading days before the
end of each week.
Parameters
----------
days_offset : int, optional
Number of trading days prior to week end to trigger. Default is 0,
i.e., trigger on the last trading day of the week.
"""
return NDaysBeforeLastTradingDayOfWeek(n=days_offset)
class time_rules(object):
"""Factories for time-based :func:`~zipline.api.schedule_function` rules.
See Also
--------
:func:`~zipline.api.schedule_function`
"""
@staticmethod
def market_open(offset=None, hours=None, minutes=None):
"""
Create a rule that triggers at a fixed offset from market open.
The offset can be specified either as a :class:`datetime.timedelta`, or
as a number of hours and minutes.
Parameters
----------
offset : datetime.timedelta, optional
If passed, the offset from market open at which to trigger. Must be
at least 1 minute.
hours : int, optional
If passed, number of hours to wait after market open.
minutes : int, optional
If passed, number of minutes to wait after market open.
Returns
-------
rule : zipline.utils.events.EventRule
Notes
-----
If no arguments are passed, the default offset is one minute after
market open.
If ``offset`` is passed, ``hours`` and ``minutes`` must not be
passed. Conversely, if either ``hours`` or ``minutes`` are passed,
``offset`` must not be passed.
"""
return AfterOpen(offset=offset, hours=hours, minutes=minutes)
@staticmethod
def market_close(offset=None, hours=None, minutes=None):
"""
Create a rule that triggers at a fixed offset from market close.
The offset can be specified either as a :class:`datetime.timedelta`, or
as a number of hours and minutes.
Parameters
----------
offset : datetime.timedelta, optional
If passed, the offset from market close at which to trigger. Must
be at least 1 minute.
hours : int, optional
If passed, number of hours to wait before market close.
minutes : int, optional
If passed, number of minutes to wait before market close.
Returns
-------
rule : zipline.utils.events.EventRule
Notes
-----
If no arguments are passed, the default offset is one minute before
market close.
If ``offset`` is passed, ``hours`` and ``minutes`` must not be
passed. Conversely, if either ``hours`` or ``minutes`` are passed,
``offset`` must not be passed.
"""
return BeforeClose(offset=offset, hours=hours, minutes=minutes)
every_minute = Always
class calendars(object):
US_EQUITIES = sentinel('US_EQUITIES')
US_FUTURES = sentinel('US_FUTURES')
def _invert(d):
return dict(zip(d.values(), d.keys()))
_uncalled_rules = _invert(vars(date_rules))
_uncalled_rules.update(_invert(vars(time_rules)))
def _check_if_not_called(v):
try:
name = _uncalled_rules[v]
except KeyError:
if not issubclass(v, EventRule):
return
name = getattr(v, '__name__', None)
msg = 'invalid rule: %r' % (v,)
if name is not None:
msg += ' (hint: did you mean %s())' % name
raise TypeError(msg)
def make_eventrule(date_rule, time_rule, cal, half_days=True):
"""
Constructs an event rule from the factory api.
"""
_check_if_not_called(date_rule)
_check_if_not_called(time_rule)
if half_days:
inner_rule = date_rule & time_rule
else:
inner_rule = date_rule & time_rule & NotHalfDay()
opd = OncePerDay(rule=inner_rule)
# This is where a scheduled function's rule is associated with a calendar.
opd.cal = cal
return opd | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/events.py | events.py |
from collections import OrderedDict, Sequence
from itertools import compress
from weakref import WeakKeyDictionary, ref
from six.moves._thread import allocate_lock as Lock
from toolz.sandbox import unzip
from trading_calendars.utils.memoize import lazyval
from zipline.utils.compat import wraps
class classlazyval(lazyval):
""" Decorator that marks that an attribute of a class should not be
computed until needed, and that the value should be memoized.
Example
-------
>>> from zipline.utils.memoize import classlazyval
>>> class C(object):
... count = 0
... @classlazyval
... def val(cls):
... cls.count += 1
... return "val"
...
>>> C.count
0
>>> C.val, C.count
('val', 1)
>>> C.val, C.count
('val', 1)
"""
# We don't reassign the name on the class to implement the caching because
# then we would need to use a metaclass to track the name of the
# descriptor.
def __get__(self, instance, owner):
return super(classlazyval, self).__get__(owner, owner)
def _weak_lru_cache(maxsize=100):
"""
Users should only access the lru_cache through its public API:
cache_info, cache_clear
The internals of the lru_cache are encapsulated for thread safety and
to allow the implementation to change.
"""
def decorating_function(
user_function, tuple=tuple, sorted=sorted, len=len,
KeyError=KeyError):
hits, misses = [0], [0]
kwd_mark = (object(),) # separates positional and keyword args
lock = Lock() # needed because OrderedDict isn't threadsafe
if maxsize is None:
cache = _WeakArgsDict() # cache without ordering or size limit
@wraps(user_function)
def wrapper(*args, **kwds):
key = args
if kwds:
key += kwd_mark + tuple(sorted(kwds.items()))
try:
result = cache[key]
hits[0] += 1
return result
except KeyError:
pass
result = user_function(*args, **kwds)
cache[key] = result
misses[0] += 1
return result
else:
# ordered least recent to most recent
cache = _WeakArgsOrderedDict()
cache_popitem = cache.popitem
cache_renew = cache.move_to_end
@wraps(user_function)
def wrapper(*args, **kwds):
key = args
if kwds:
key += kwd_mark + tuple(sorted(kwds.items()))
with lock:
try:
result = cache[key]
cache_renew(key) # record recent use of this key
hits[0] += 1
return result
except KeyError:
pass
result = user_function(*args, **kwds)
with lock:
cache[key] = result # record recent use of this key
misses[0] += 1
if len(cache) > maxsize:
# purge least recently used cache entry
cache_popitem(False)
return result
def cache_info():
"""Report cache statistics"""
with lock:
return hits[0], misses[0], maxsize, len(cache)
def cache_clear():
"""Clear the cache and cache statistics"""
with lock:
cache.clear()
hits[0] = misses[0] = 0
wrapper.cache_info = cache_info
wrapper.cache_clear = cache_clear
return wrapper
return decorating_function
class _WeakArgs(Sequence):
"""
Works with _WeakArgsDict to provide a weak cache for function args.
When any of those args are gc'd, the pair is removed from the cache.
"""
def __init__(self, items, dict_remove=None):
def remove(k, selfref=ref(self), dict_remove=dict_remove):
self = selfref()
if self is not None and dict_remove is not None:
dict_remove(self)
self._items, self._selectors = unzip(self._try_ref(item, remove)
for item in items)
self._items = tuple(self._items)
self._selectors = tuple(self._selectors)
def __getitem__(self, index):
return self._items[index]
def __len__(self):
return len(self._items)
@staticmethod
def _try_ref(item, callback):
try:
return ref(item, callback), True
except TypeError:
return item, False
@property
def alive(self):
return all(item() is not None
for item in compress(self._items, self._selectors))
def __eq__(self, other):
return self._items == other._items
def __hash__(self):
try:
return self.__hash
except AttributeError:
h = self.__hash = hash(self._items)
return h
class _WeakArgsDict(WeakKeyDictionary, object):
def __delitem__(self, key):
del self.data[_WeakArgs(key)]
def __getitem__(self, key):
return self.data[_WeakArgs(key)]
def __repr__(self):
return '%s(%r)' % (type(self).__name__, self.data)
def __setitem__(self, key, value):
self.data[_WeakArgs(key, self._remove)] = value
def __contains__(self, key):
try:
wr = _WeakArgs(key)
except TypeError:
return False
return wr in self.data
def pop(self, key, *args):
return self.data.pop(_WeakArgs(key), *args)
class _WeakArgsOrderedDict(_WeakArgsDict, object):
def __init__(self):
super(_WeakArgsOrderedDict, self).__init__()
self.data = OrderedDict()
def popitem(self, last=True):
while True:
key, value = self.data.popitem(last)
if key.alive:
return tuple(key), value
def move_to_end(self, key):
"""Move an existing element to the end.
Raises KeyError if the element does not exist.
"""
self[key] = self.pop(key)
def weak_lru_cache(maxsize=100):
"""Weak least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and the cache
can grow without bound.
Arguments to the cached function must be hashable. Any that are weak-
referenceable will be stored by weak reference. Once any of the args have
been garbage collected, the entry will be removed from the cache.
View the cache statistics named tuple (hits, misses, maxsize, currsize)
with f.cache_info(). Clear the cache and statistics with f.cache_clear().
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
class desc(lazyval):
def __get__(self, instance, owner):
if instance is None:
return self
try:
return self._cache[instance]
except KeyError:
inst = ref(instance)
@_weak_lru_cache(maxsize)
@wraps(self._get)
def wrapper(*args, **kwargs):
return self._get(inst(), *args, **kwargs)
self._cache[instance] = wrapper
return wrapper
@_weak_lru_cache(maxsize)
def __call__(self, *args, **kwargs):
return self._get(*args, **kwargs)
return desc
remember_last = weak_lru_cache(1) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/memoize.py | memoize.py |
import zipline.api
from zipline.utils.compat import wraps
from zipline.utils.algo_instance import get_algo_instance, set_algo_instance
class ZiplineAPI(object):
"""
Context manager for making an algorithm instance available to zipline API
functions within a scoped block.
"""
def __init__(self, algo_instance):
self.algo_instance = algo_instance
def __enter__(self):
"""
Set the given algo instance, storing any previously-existing instance.
"""
self.old_algo_instance = get_algo_instance()
set_algo_instance(self.algo_instance)
def __exit__(self, _type, _value, _tb):
"""
Restore the algo instance stored in __enter__.
"""
set_algo_instance(self.old_algo_instance)
def api_method(f):
# Decorator that adds the decorated class method as a callable
# function (wrapped) to zipline.api
@wraps(f)
def wrapped(*args, **kwargs):
# Get the instance and call the method
algo_instance = get_algo_instance()
if algo_instance is None:
raise RuntimeError(
'zipline api method %s must be called during a simulation.'
% f.__name__
)
return getattr(algo_instance, f.__name__)(*args, **kwargs)
# Add functor to zipline.api
setattr(zipline.api, f.__name__, wrapped)
zipline.api.__all__.append(f.__name__)
f.is_api_method = True
return f
def require_not_initialized(exception):
"""
Decorator for API methods that should only be called during or before
TradingAlgorithm.initialize. `exception` will be raised if the method is
called after initialize.
Examples
--------
@require_not_initialized(SomeException("Don't do that!"))
def method(self):
# Do stuff that should only be allowed during initialize.
"""
def decorator(method):
@wraps(method)
def wrapped_method(self, *args, **kwargs):
if self.initialized:
raise exception
return method(self, *args, **kwargs)
return wrapped_method
return decorator
def require_initialized(exception):
"""
Decorator for API methods that should only be called after
TradingAlgorithm.initialize. `exception` will be raised if the method is
called before initialize has completed.
Examples
--------
@require_initialized(SomeException("Don't do that!"))
def method(self):
# Do stuff that should only be allowed after initialize.
"""
def decorator(method):
@wraps(method)
def wrapped_method(self, *args, **kwargs):
if not self.initialized:
raise exception
return method(self, *args, **kwargs)
return wrapped_method
return decorator
def disallowed_in_before_trading_start(exception):
"""
Decorator for API methods that cannot be called from within
TradingAlgorithm.before_trading_start. `exception` will be raised if the
method is called inside `before_trading_start`.
Examples
--------
@disallowed_in_before_trading_start(SomeException("Don't do that!"))
def method(self):
# Do stuff that is not allowed inside before_trading_start.
"""
def decorator(method):
@wraps(method)
def wrapped_method(self, *args, **kwargs):
if self._in_before_trading_start:
raise exception
return method(self, *args, **kwargs)
return wrapped_method
return decorator
def allowed_only_in_before_trading_start(exception):
"""
Decorator for API methods that can be called only from within
TradingAlgorithm.before_trading_start. `exception` will be raised if the
method is called outside `before_trading_start`.
Usage
-----
@allowed_only_in_before_trading_start(SomeException("Don't do that!"))
def method(self):
# Do stuff that is only allowed inside before_trading_start.
"""
def decorator(method):
@wraps(method)
def wrapped_method(self, *args, **kwargs):
if not self._in_before_trading_start:
raise exception
return method(self, *args, **kwargs)
return wrapped_method
return decorator | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/api_support.py | api_support.py |
import pandas as pd
import numpy as np
from datetime import timedelta, datetime
from trading_calendars import get_calendar
from zipline.sources import SpecificEquityTrades
from zipline.finance.trading import SimulationParameters
from zipline.sources.test_source import create_trade
def create_simulation_parameters(year=2006,
start=None,
end=None,
capital_base=float("1.0e5"),
num_days=None,
data_frequency='daily',
emission_rate='daily',
trading_calendar=None):
if not trading_calendar:
trading_calendar = get_calendar("NYSE")
if start is None:
start = pd.Timestamp("{0}-01-01".format(year), tz='UTC')
elif type(start) == datetime:
start = pd.Timestamp(start)
if end is None:
if num_days:
start_index = trading_calendar.all_sessions.searchsorted(start)
end = trading_calendar.all_sessions[start_index + num_days - 1]
else:
end = pd.Timestamp("{0}-12-31".format(year), tz='UTC')
elif type(end) == datetime:
end = pd.Timestamp(end)
sim_params = SimulationParameters(
start_session=start,
end_session=end,
capital_base=capital_base,
data_frequency=data_frequency,
emission_rate=emission_rate,
trading_calendar=trading_calendar,
)
return sim_params
def get_next_trading_dt(current, interval, trading_calendar):
next_dt = pd.Timestamp(current).tz_convert(trading_calendar.tz)
while True:
# Convert timestamp to naive before adding day, otherwise the when
# stepping over EDT an hour is added.
next_dt = pd.Timestamp(next_dt.replace(tzinfo=None))
next_dt = next_dt + interval
next_dt = pd.Timestamp(next_dt, tz=trading_calendar.tz)
next_dt_utc = next_dt.tz_convert('UTC')
if trading_calendar.is_open_on_minute(next_dt_utc):
break
next_dt = next_dt_utc.tz_convert(trading_calendar.tz)
return next_dt_utc
def create_trade_history(sid, prices, amounts, interval, sim_params,
trading_calendar, source_id="test_factory"):
trades = []
current = sim_params.first_open
oneday = timedelta(days=1)
use_midnight = interval >= oneday
for price, amount in zip(prices, amounts):
if use_midnight:
trade_dt = current.replace(hour=0, minute=0)
else:
trade_dt = current
trade = create_trade(sid, price, amount, trade_dt, source_id)
trades.append(trade)
current = get_next_trading_dt(current, interval, trading_calendar)
assert len(trades) == len(prices)
return trades
def create_returns_from_range(sim_params):
return pd.Series(index=sim_params.sessions,
data=np.random.rand(len(sim_params.sessions)))
def create_returns_from_list(returns, sim_params):
return pd.Series(index=sim_params.sessions[:len(returns)],
data=returns)
def create_daily_trade_source(sids,
sim_params,
asset_finder,
trading_calendar):
"""
creates trade_count trades for each sid in sids list.
first trade will be on sim_params.start_session, and daily
thereafter for each sid. Thus, two sids should result in two trades per
day.
"""
return create_trade_source(
sids,
timedelta(days=1),
sim_params,
asset_finder,
trading_calendar=trading_calendar,
)
def create_trade_source(sids,
trade_time_increment,
sim_params,
asset_finder,
trading_calendar):
# If the sim_params define an end that is during market hours, that will be
# used as the end of the data source
if trading_calendar.is_open_on_minute(sim_params.end_session):
end = sim_params.end_session
# Otherwise, the last_close after the end_session is used as the end of the
# data source
else:
end = sim_params.last_close
args = tuple()
kwargs = {
'sids': sids,
'start': sim_params.first_open,
'end': end,
'delta': trade_time_increment,
'trading_calendar': trading_calendar,
'asset_finder': asset_finder,
}
source = SpecificEquityTrades(*args, **kwargs)
return source | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/factory.py | factory.py |
import click
import pandas as pd
from .context_tricks import CallbackManager
def maybe_show_progress(it, show_progress, **kwargs):
"""Optionally show a progress bar for the given iterator.
Parameters
----------
it : iterable
The underlying iterator.
show_progress : bool
Should progress be shown.
**kwargs
Forwarded to the click progress bar.
Returns
-------
itercontext : context manager
A context manager whose enter is the actual iterator to use.
Examples
--------
.. code-block:: python
with maybe_show_progress([1, 2, 3], True) as ns:
for n in ns:
...
"""
if show_progress:
return click.progressbar(it, **kwargs)
# context manager that just return `it` when we enter it
return CallbackManager(lambda it=it: it)
class _DatetimeParam(click.ParamType):
def __init__(self, tz=None):
self.tz = tz
def parser(self, value):
return pd.Timestamp(value, tz=self.tz)
@property
def name(self):
return type(self).__name__.upper()
def convert(self, value, param, ctx):
try:
return self.parser(value)
except ValueError:
self.fail(
'%s is not a valid %s' % (value, self.name.lower()),
param,
ctx,
)
class Timestamp(_DatetimeParam):
"""A click parameter that parses the value into pandas.Timestamp objects.
Parameters
----------
tz : timezone-coercable, optional
The timezone to parse the string as.
By default the timezone will be infered from the string or naiive.
"""
class Date(_DatetimeParam):
"""A click parameter that parses the value into datetime.date objects.
Parameters
----------
tz : timezone-coercable, optional
The timezone to parse the string as.
By default the timezone will be infered from the string or naiive.
as_timestamp : bool, optional
If True, return the value as a pd.Timestamp object normalized to
midnight.
"""
def __init__(self, tz=None, as_timestamp=False):
super(Date, self).__init__(tz=tz)
self.as_timestamp = as_timestamp
def parser(self, value):
ts = super(Date, self).parser(value)
return ts.normalize() if self.as_timestamp else ts.date()
class Time(_DatetimeParam):
"""A click parameter that parses the value into timetime.time objects.
Parameters
----------
tz : timezone-coercable, optional
The timezone to parse the string as.
By default the timezone will be infered from the string or naiive.
"""
def parser(self, value):
return super(Time, self).parser(value).time()
class Timedelta(_DatetimeParam):
"""A click parameter that parses values into pd.Timedelta objects.
Parameters
----------
unit : {'D', 'h', 'm', 's', 'ms', 'us', 'ns'}, optional
Denotes the unit of the input if the input is an integer.
"""
def __init__(self, unit='ns'):
self.unit = unit
def parser(self, value):
return pd.Timedelta(value, unit=self.unit) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/utils/cli.py | cli.py |
import abc
import logbook
from datetime import datetime
import pandas as pd
from six import with_metaclass
from zipline.errors import (
AccountControlViolation,
TradingControlViolation,
)
from zipline.utils.input_validation import (
expect_bounded,
expect_types,
)
log = logbook.Logger('TradingControl')
class TradingControl(with_metaclass(abc.ABCMeta)):
"""
Abstract base class representing a fail-safe control on the behavior of any
algorithm.
"""
def __init__(self, on_error, **kwargs):
"""
Track any arguments that should be printed in the error message
generated by self.fail.
"""
self.on_error = on_error
self.__fail_args = kwargs
@abc.abstractmethod
def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Before any order is executed by TradingAlgorithm, this method should be
called *exactly once* on each registered TradingControl object.
If the specified asset and amount do not violate this TradingControl's
restraint given the information in `portfolio`, this method should
return None and have no externally-visible side-effects.
If the desired order violates this TradingControl's contraint, this
method should call self.fail(asset, amount).
"""
raise NotImplementedError
def _constraint_msg(self, metadata):
constraint = repr(self)
if metadata:
constraint = "{constraint} (Metadata: {metadata})".format(
constraint=constraint,
metadata=metadata
)
return constraint
def handle_violation(self, asset, amount, datetime, metadata=None):
"""
Handle a TradingControlViolation, either by raising or logging and
error with information about the failure.
If dynamic information should be displayed as well, pass it in via
`metadata`.
"""
constraint = self._constraint_msg(metadata)
if self.on_error == 'fail':
raise TradingControlViolation(
asset=asset,
amount=amount,
datetime=datetime,
constraint=constraint)
elif self.on_error == 'log':
log.error("Order for {amount} shares of {asset} at {dt} "
"violates trading constraint {constraint}",
amount=amount, asset=asset, dt=datetime,
constraint=constraint)
def __repr__(self):
return "{name}({attrs})".format(name=self.__class__.__name__,
attrs=self.__fail_args)
class MaxOrderCount(TradingControl):
"""
TradingControl representing a limit on the number of orders that can be
placed in a given trading day.
"""
def __init__(self, on_error, max_count):
super(MaxOrderCount, self).__init__(on_error, max_count=max_count)
self.orders_placed = 0
self.max_count = max_count
self.current_date = None
def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if we've already placed self.max_count orders today.
"""
algo_date = algo_datetime.date()
# Reset order count if it's a new day.
if self.current_date and self.current_date != algo_date:
self.orders_placed = 0
self.current_date = algo_date
if self.orders_placed >= self.max_count:
self.handle_violation(asset, amount, algo_datetime)
self.orders_placed += 1
class RestrictedListOrder(TradingControl):
"""TradingControl representing a restricted list of assets that
cannot be ordered by the algorithm.
Parameters
----------
restrictions : zipline.finance.asset_restrictions.Restrictions
Object representing restrictions of a group of assets.
"""
def __init__(self, on_error, restrictions):
super(RestrictedListOrder, self).__init__(on_error)
self.restrictions = restrictions
def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the asset is in the restricted_list.
"""
if self.restrictions.is_restricted(asset, algo_datetime):
self.handle_violation(asset, amount, algo_datetime)
class MaxOrderSize(TradingControl):
"""
TradingControl representing a limit on the magnitude of any single order
placed with the given asset. Can be specified by share or by dollar
value.
"""
def __init__(self, on_error, asset=None, max_shares=None,
max_notional=None):
super(MaxOrderSize, self).__init__(on_error,
asset=asset,
max_shares=max_shares,
max_notional=max_notional)
self.asset = asset
self.max_shares = max_shares
self.max_notional = max_notional
if max_shares is None and max_notional is None:
raise ValueError(
"Must supply at least one of max_shares and max_notional"
)
if max_shares and max_shares < 0:
raise ValueError(
"max_shares cannot be negative."
)
if max_notional and max_notional < 0:
raise ValueError(
"max_notional must be positive."
)
def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the magnitude of the given order exceeds either self.max_shares
or self.max_notional.
"""
if self.asset is not None and self.asset != asset:
return
if self.max_shares is not None and abs(amount) > self.max_shares:
self.handle_violation(asset, amount, algo_datetime)
current_asset_price = algo_current_data.current(asset, "price")
order_value = amount * current_asset_price
too_much_value = (self.max_notional is not None and
abs(order_value) > self.max_notional)
if too_much_value:
self.handle_violation(asset, amount, algo_datetime)
class MaxPositionSize(TradingControl):
"""
TradingControl representing a limit on the maximum position size that can
be held by an algo for a given asset.
"""
def __init__(self, on_error, asset=None, max_shares=None,
max_notional=None):
super(MaxPositionSize, self).__init__(on_error,
asset=asset,
max_shares=max_shares,
max_notional=max_notional)
self.asset = asset
self.max_shares = max_shares
self.max_notional = max_notional
if max_shares is None and max_notional is None:
raise ValueError(
"Must supply at least one of max_shares and max_notional"
)
if max_shares and max_shares < 0:
raise ValueError(
"max_shares cannot be negative."
)
if max_notional and max_notional < 0:
raise ValueError(
"max_notional must be positive."
)
def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the given order would cause the magnitude of our position to be
greater in shares than self.max_shares or greater in dollar value than
self.max_notional.
"""
if self.asset is not None and self.asset != asset:
return
current_share_count = portfolio.positions[asset].amount
shares_post_order = current_share_count + amount
too_many_shares = (self.max_shares is not None and
abs(shares_post_order) > self.max_shares)
if too_many_shares:
self.handle_violation(asset, amount, algo_datetime)
current_price = algo_current_data.current(asset, "price")
value_post_order = shares_post_order * current_price
too_much_value = (self.max_notional is not None and
abs(value_post_order) > self.max_notional)
if too_much_value:
self.handle_violation(asset, amount, algo_datetime)
class LongOnly(TradingControl):
"""
TradingControl representing a prohibition against holding short positions.
"""
def __init__(self, on_error):
super(LongOnly, self).__init__(on_error)
def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if we would hold negative shares of asset after completing this
order.
"""
if portfolio.positions[asset].amount + amount < 0:
self.handle_violation(asset, amount, algo_datetime)
class AssetDateBounds(TradingControl):
"""
TradingControl representing a prohibition against ordering an asset before
its start_date, or after its end_date.
"""
def __init__(self, on_error):
super(AssetDateBounds, self).__init__(on_error)
def validate(self,
asset,
amount,
portfolio,
algo_datetime,
algo_current_data):
"""
Fail if the algo has passed this Asset's end_date, or before the
Asset's start date.
"""
# If the order is for 0 shares, then silently pass through.
if amount == 0:
return
normalized_algo_dt = pd.Timestamp(algo_datetime).normalize()
# Fail if the algo is before this Asset's start_date
if asset.start_date:
normalized_start = pd.Timestamp(asset.start_date).normalize()
if normalized_algo_dt < normalized_start:
metadata = {
'asset_start_date': normalized_start
}
self.handle_violation(
asset, amount, algo_datetime, metadata=metadata)
# Fail if the algo has passed this Asset's end_date
if asset.end_date:
normalized_end = pd.Timestamp(asset.end_date).normalize()
if normalized_algo_dt > normalized_end:
metadata = {
'asset_end_date': normalized_end
}
self.handle_violation(
asset, amount, algo_datetime, metadata=metadata)
class AccountControl(with_metaclass(abc.ABCMeta)):
"""
Abstract base class representing a fail-safe control on the behavior of any
algorithm.
"""
def __init__(self, **kwargs):
"""
Track any arguments that should be printed in the error message
generated by self.fail.
"""
self.__fail_args = kwargs
@abc.abstractmethod
def validate(self,
_portfolio,
_account,
_algo_datetime,
_algo_current_data):
"""
On each call to handle data by TradingAlgorithm, this method should be
called *exactly once* on each registered AccountControl object.
If the check does not violate this AccountControl's restraint given
the information in `portfolio` and `account`, this method should
return None and have no externally-visible side-effects.
If the desired order violates this AccountControl's contraint, this
method should call self.fail().
"""
raise NotImplementedError
def fail(self):
"""
Raise an AccountControlViolation with information about the failure.
"""
raise AccountControlViolation(constraint=repr(self))
def __repr__(self):
return "{name}({attrs})".format(name=self.__class__.__name__,
attrs=self.__fail_args)
class MaxLeverage(AccountControl):
"""
AccountControl representing a limit on the maximum leverage allowed
by the algorithm.
"""
def __init__(self, max_leverage):
"""
max_leverage is the gross leverage in decimal form. For example,
2, limits an algorithm to trading at most double the account value.
"""
super(MaxLeverage, self).__init__(max_leverage=max_leverage)
self.max_leverage = max_leverage
if max_leverage is None:
raise ValueError(
"Must supply max_leverage"
)
if max_leverage < 0:
raise ValueError(
"max_leverage must be positive"
)
def validate(self,
_portfolio,
_account,
_algo_datetime,
_algo_current_data):
"""
Fail if the leverage is greater than the allowed leverage.
"""
if _account.leverage > self.max_leverage:
self.fail()
class MinLeverage(AccountControl):
"""AccountControl representing a limit on the minimum leverage allowed
by the algorithm after a threshold period of time.
Parameters
----------
min_leverage : float
The gross leverage in decimal form.
deadline : datetime
The date the min leverage must be achieved by.
For example, min_leverage=2 limits an algorithm to trading at minimum
double the account value by the deadline date.
"""
@expect_types(
__funcname='MinLeverage',
min_leverage=(int, float),
deadline=datetime
)
@expect_bounded(__funcname='MinLeverage', min_leverage=(0, None))
def __init__(self, min_leverage, deadline):
super(MinLeverage, self).__init__(min_leverage=min_leverage,
deadline=deadline)
self.min_leverage = min_leverage
self.deadline = deadline
def validate(self,
_portfolio,
account,
algo_datetime,
_algo_current_data):
"""
Make validation checks if we are after the deadline.
Fail if the leverage is less than the min leverage.
"""
if (algo_datetime > self.deadline and
account.leverage < self.min_leverage):
self.fail() | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/controls.py | controls.py |
TRADING_DAYS_IN_YEAR = 250
TRADING_HOURS_IN_DAY = 6.5
MINUTES_IN_HOUR = 60
ANNUALIZER = {'daily': TRADING_DAYS_IN_YEAR,
'hourly': TRADING_DAYS_IN_YEAR * TRADING_HOURS_IN_DAY,
'minute': TRADING_DAYS_IN_YEAR * TRADING_HOURS_IN_DAY *
MINUTES_IN_HOUR}
# NOTE: It may be worth revisiting how the keys for this dictionary are
# specified, for instance making them ContinuousFuture objects instead of
# static strings.
FUTURE_EXCHANGE_FEES_BY_SYMBOL = {
'AD': 1.60, # AUD
'AI': 0.96, # Bloomberg Commodity Index
'BD': 1.50, # Big Dow
'BO': 1.95, # Soybean Oil
'BP': 1.60, # GBP
'CD': 1.60, # CAD
'CL': 1.50, # Crude Oil
'CM': 1.03, # Corn e-mini
'CN': 1.95, # Corn
'DJ': 1.50, # Dow Jones
'EC': 1.60, # Euro FX
'ED': 1.25, # Eurodollar
'EE': 1.50, # Euro FX e-mini
'EI': 1.50, # MSCI Emerging Markets mini
'EL': 1.50, # Eurodollar NYSE LIFFE
'ER': 0.65, # Russell2000 e-mini
'ES': 1.18, # SP500 e-mini
'ET': 1.50, # Ethanol
'EU': 1.50, # Eurodollar e-micro
'FC': 2.03, # Feeder Cattle
'FF': 0.96, # 3-Day Federal Funds
'FI': 0.56, # Deliverable Interest Rate Swap 5y
'FS': 1.50, # Interest Rate Swap 5y
'FV': 0.65, # US 5y
'GC': 1.50, # Gold
'HG': 1.50, # Copper
'HO': 1.50, # Heating Oil
'HU': 1.50, # Unleaded Gasoline
'JE': 0.16, # JPY e-mini
'JY': 1.60, # JPY
'LB': 2.03, # Lumber
'LC': 2.03, # Live Cattle
'LH': 2.03, # Lean Hogs
'MB': 1.50, # Municipal Bonds
'MD': 1.50, # SP400 Midcap
'ME': 1.60, # MXN
'MG': 1.50, # MSCI EAFE mini
'MI': 1.18, # SP400 Midcap e-mini
'MS': 1.03, # Soybean e-mini
'MW': 1.03, # Wheat e-mini
'ND': 1.50, # Nasdaq100
'NG': 1.50, # Natural Gas
'NK': 2.15, # Nikkei225
'NQ': 1.18, # Nasdaq100 e-mini
'NZ': 1.60, # NZD
'OA': 1.95, # Oats
'PA': 1.50, # Palladium
'PB': 1.50, # Pork Bellies
'PL': 1.50, # Platinum
'QG': 0.50, # Natural Gas e-mini
'QM': 1.20, # Crude Oil e-mini
'RM': 1.50, # Russell1000 e-mini
'RR': 1.95, # Rough Rice
'SB': 2.10, # Sugar
'SF': 1.60, # CHF
'SM': 1.95, # Soybean Meal
'SP': 2.40, # SP500
'SV': 1.50, # Silver
'SY': 1.95, # Soybean
'TB': 1.50, # Treasury Bills
'TN': 0.56, # Deliverable Interest Rate Swap 10y
'TS': 1.50, # Interest Rate Swap 10y
'TU': 1.50, # US 2y
'TY': 0.75, # US 10y
'UB': 0.85, # Ultra Tbond
'US': 0.80, # US 30y
'VX': 1.50, # VIX
'WC': 1.95, # Wheat
'XB': 1.50, # RBOB Gasoline
'XG': 0.75, # Gold e-mini
'YM': 1.50, # Dow Jones e-mini
'YS': 0.75, # Silver e-mini
}
# See `zipline.finance.slippage.VolatilityVolumeShare` for more information on
# how these constants are used.
DEFAULT_ETA = 0.049018143225019836
ROOT_SYMBOL_TO_ETA = {
'AD': DEFAULT_ETA, # AUD
'AI': DEFAULT_ETA, # Bloomberg Commodity Index
'BD': 0.050346811117733474, # Big Dow
'BO': 0.054930995070046298, # Soybean Oil
'BP': 0.047841544238716338, # GBP
'CD': 0.051124420640250717, # CAD
'CL': 0.04852544628414196, # Crude Oil
'CM': 0.052683478163348625, # Corn e-mini
'CN': 0.053499718390037809, # Corn
'DJ': 0.02313009072076987, # Dow Jones
'EC': 0.04885131067661861, # Euro FX
'ED': 0.094184297090245755, # Eurodollar
'EE': 0.048713151357687556, # Euro FX e-mini
'EI': 0.031712708439692663, # MSCI Emerging Markets mini
'EL': 0.044207422018209361, # Eurodollar NYSE LIFFE
'ER': 0.045930567737711307, # Russell2000 e-mini
'ES': 0.047304418321993502, # SP500 e-mini
'ET': DEFAULT_ETA, # Ethanol
'EU': 0.049750396084029064, # Eurodollar e-micro
'FC': 0.058728734202178494, # Feeder Cattle
'FF': 0.048970591527624042, # 3-Day Federal Funds
'FI': 0.033477176738170772, # Deliverable Interest Rate Swap 5y
'FS': 0.034557788010453824, # Interest Rate Swap 5y
'FV': 0.046544427716056963, # US 5y
'GC': 0.048933313546125207, # Gold
'HG': 0.052238417524987799, # Copper
'HO': 0.045061318412156062, # Heating Oil
'HU': 0.017154313062463938, # Unleaded Gasoline
'JE': 0.013948949613401812, # JPY e-mini
'JY': DEFAULT_ETA, # JPY
'LB': 0.06146586386903994, # Lumber
'LC': 0.055853801862858619, # Live Cattle
'LH': 0.057557004630219781, # Lean Hogs
'MB': DEFAULT_ETA, # Municipal Bonds
'MD': DEFAULT_ETA, # SP400 Midcap
'ME': 0.030383767727818548, # MXN
'MG': 0.029579261656151684, # MSCI EAFE mini
'MI': 0.041026288873007355, # SP400 Midcap e-mini
'MS': DEFAULT_ETA, # Soybean e-mini
'MW': 0.052579919663880245, # Wheat e-mini
'ND': DEFAULT_ETA, # Nasdaq100
'NG': 0.047897809233755716, # Natural Gas
'NK': 0.044555435054791433, # Nikkei225
'NQ': 0.044772425085977945, # Nasdaq100 e-mini
'NZ': 0.049170418073872041, # NZD
'OA': 0.056973267232775522, # Oats
'PA': DEFAULT_ETA, # Palladium
'PB': DEFAULT_ETA, # Pork Bellies
'PL': 0.054579379665647493, # Platinum
'QG': DEFAULT_ETA, # Natural Gas e-mini
'QM': DEFAULT_ETA, # Crude Oil e-mini
'RM': 0.037425041244579654, # Russell1000 e-mini
'RR': DEFAULT_ETA, # Rough Rice
'SB': 0.057388160345668134, # Sugar
'SF': 0.047784825569615726, # CHF
'SM': 0.048552860559844223, # Soybean Meal
'SP': DEFAULT_ETA, # SP500
'SV': 0.052691435039931109, # Silver
'SY': 0.052041703657281613, # Soybean
'TB': DEFAULT_ETA, # Treasury Bills
'TN': 0.033363465365262503, # Deliverable Interest Rate Swap 10y
'TS': 0.032908878455069152, # Interest Rate Swap 10y
'TU': 0.063867646063840794, # US 2y
'TY': 0.050586988554700826, # US 10y
'UB': DEFAULT_ETA, # Ultra Tbond
'US': 0.047984179873590722, # US 30y
'VX': DEFAULT_ETA, # VIX
'WC': 0.052636542119329242, # Wheat
'XB': 0.044444916388854484, # RBOB Gasoline
'XG': DEFAULT_ETA, # Gold e-mini
'YM': DEFAULT_ETA, # Dow Jones e-mini
'YS': DEFAULT_ETA, # Silver e-mini
} | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/constants.py | constants.py |
import abc
from numpy import vectorize
from functools import partial, reduce
import operator
import pandas as pd
from six import with_metaclass, iteritems
from collections import namedtuple
from toolz import groupby
from zipline.utils.enum import enum
from zipline.utils.numpy_utils import vectorized_is_element
from zipline.assets import Asset
Restriction = namedtuple(
'Restriction', ['asset', 'effective_date', 'state']
)
RESTRICTION_STATES = enum(
'ALLOWED',
'FROZEN',
)
class Restrictions(with_metaclass(abc.ABCMeta)):
"""
Abstract restricted list interface, representing a set of assets that an
algorithm is restricted from trading.
"""
@abc.abstractmethod
def is_restricted(self, assets, dt):
"""
Is the asset restricted (RestrictionStates.FROZEN) on the given dt?
Parameters
----------
asset : Asset of iterable of Assets
The asset(s) for which we are querying a restriction
dt : pd.Timestamp
The timestamp of the restriction query
Returns
-------
is_restricted : bool or pd.Series[bool] indexed by asset
Is the asset or assets restricted on this dt?
"""
raise NotImplementedError('is_restricted')
def __or__(self, other_restriction):
"""Base implementation for combining two restrictions.
"""
# If the right side is a _UnionRestrictions, defers to the
# _UnionRestrictions implementation of `|`, which intelligently
# flattens restricted lists
if isinstance(other_restriction, _UnionRestrictions):
return other_restriction | self
return _UnionRestrictions([self, other_restriction])
class _UnionRestrictions(Restrictions):
"""
A union of a number of sub restrictions.
Parameters
----------
sub_restrictions : iterable of Restrictions (but not _UnionRestrictions)
The Restrictions to be added together
Notes
-----
- Consumers should not construct instances of this class directly, but
instead use the `|` operator to combine restrictions
"""
def __new__(cls, sub_restrictions):
# Filter out NoRestrictions and deal with resulting cases involving
# one or zero sub_restrictions
sub_restrictions = [
r for r in sub_restrictions if not isinstance(r, NoRestrictions)
]
if len(sub_restrictions) == 0:
return NoRestrictions()
elif len(sub_restrictions) == 1:
return sub_restrictions[0]
new_instance = super(_UnionRestrictions, cls).__new__(cls)
new_instance.sub_restrictions = sub_restrictions
return new_instance
def __or__(self, other_restriction):
"""
Overrides the base implementation for combining two restrictions, of
which the left side is a _UnionRestrictions.
"""
# Flatten the underlying sub restrictions of _UnionRestrictions
if isinstance(other_restriction, _UnionRestrictions):
new_sub_restrictions = \
self.sub_restrictions + other_restriction.sub_restrictions
else:
new_sub_restrictions = self.sub_restrictions + [other_restriction]
return _UnionRestrictions(new_sub_restrictions)
def is_restricted(self, assets, dt):
if isinstance(assets, Asset):
return any(
r.is_restricted(assets, dt) for r in self.sub_restrictions
)
return reduce(
operator.or_,
(r.is_restricted(assets, dt) for r in self.sub_restrictions)
)
class NoRestrictions(Restrictions):
"""
A no-op restrictions that contains no restrictions.
"""
def is_restricted(self, assets, dt):
if isinstance(assets, Asset):
return False
return pd.Series(index=pd.Index(assets), data=False)
class StaticRestrictions(Restrictions):
"""
Static restrictions stored in memory that are constant regardless of dt
for each asset.
Parameters
----------
restricted_list : iterable of assets
The assets to be restricted
"""
def __init__(self, restricted_list):
self._restricted_set = frozenset(restricted_list)
def is_restricted(self, assets, dt):
"""
An asset is restricted for all dts if it is in the static list.
"""
if isinstance(assets, Asset):
return assets in self._restricted_set
return pd.Series(
index=pd.Index(assets),
data=vectorized_is_element(assets, self._restricted_set)
)
class HistoricalRestrictions(Restrictions):
"""
Historical restrictions stored in memory with effective dates for each
asset.
Parameters
----------
restrictions : iterable of namedtuple Restriction
The restrictions, each defined by an asset, effective date and state
"""
def __init__(self, restrictions):
# A dict mapping each asset to its restrictions, which are sorted by
# ascending order of effective_date
self._restrictions_by_asset = {
asset: sorted(
restrictions_for_asset, key=lambda x: x.effective_date
)
for asset, restrictions_for_asset
in iteritems(groupby(lambda x: x.asset, restrictions))
}
def is_restricted(self, assets, dt):
"""
Returns whether or not an asset or iterable of assets is restricted
on a dt.
"""
if isinstance(assets, Asset):
return self._is_restricted_for_asset(assets, dt)
is_restricted = partial(self._is_restricted_for_asset, dt=dt)
return pd.Series(
index=pd.Index(assets),
data=vectorize(is_restricted, otypes=[bool])(assets)
)
def _is_restricted_for_asset(self, asset, dt):
state = RESTRICTION_STATES.ALLOWED
for r in self._restrictions_by_asset.get(asset, ()):
if r.effective_date > dt:
break
state = r.state
return state == RESTRICTION_STATES.FROZEN
class SecurityListRestrictions(Restrictions):
"""
Restrictions based on a security list.
Parameters
----------
restrictions : zipline.utils.security_list.SecurityList
The restrictions defined by a SecurityList
"""
def __init__(self, security_list_by_dt):
self.current_securities = security_list_by_dt.current_securities
def is_restricted(self, assets, dt):
securities_in_list = self.current_securities(dt)
if isinstance(assets, Asset):
return assets in securities_in_list
return pd.Series(
index=pd.Index(assets),
data=vectorized_is_element(assets, securities_in_list)
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/asset_restrictions.py | asset_restrictions.py |
from abc import abstractmethod
from collections import defaultdict
from six import with_metaclass
from toolz import merge
from zipline.assets import Equity, Future
from zipline.finance.constants import FUTURE_EXCHANGE_FEES_BY_SYMBOL
from zipline.finance.shared import AllowedAssetMarker, FinancialModelMeta
from zipline.utils.dummy import DummyMapping
DEFAULT_PER_SHARE_COST = 0.001 # 0.1 cents per share
DEFAULT_PER_CONTRACT_COST = 0.85 # $0.85 per future contract
DEFAULT_PER_DOLLAR_COST = 0.0015 # 0.15 cents per dollar
DEFAULT_MINIMUM_COST_PER_EQUITY_TRADE = 0.0 # $0 per trade
DEFAULT_MINIMUM_COST_PER_FUTURE_TRADE = 0.0 # $0 per trade
class CommissionModel(with_metaclass(FinancialModelMeta)):
"""Abstract base class for commission models.
Commission models are responsible for accepting order/transaction pairs and
calculating how much commission should be charged to an algorithm's account
on each transaction.
To implement a new commission model, create a subclass of
:class:`~zipline.finance.commission.CommissionModel` and implement
:meth:`calculate`.
"""
# Asset types that are compatible with the given model.
allowed_asset_types = (Equity, Future)
@abstractmethod
def calculate(self, order, transaction):
"""
Calculate the amount of commission to charge on ``order`` as a result
of ``transaction``.
Parameters
----------
order : zipline.finance.order.Order
The order being processed.
The ``commission`` field of ``order`` is a float indicating the
amount of commission already charged on this order.
transaction : zipline.finance.transaction.Transaction
The transaction being processed. A single order may generate
multiple transactions if there isn't enough volume in a given bar
to fill the full amount requested in the order.
Returns
-------
amount_charged : float
The additional commission, in dollars, that we should attribute to
this order.
"""
raise NotImplementedError('calculate')
class NoCommission(CommissionModel):
"""Model commissions as free.
Notes
-----
This is primarily used for testing.
"""
@staticmethod
def calculate(order, transaction):
return 0.0
class EquityCommissionModel(with_metaclass(AllowedAssetMarker,
CommissionModel)):
"""
Base class for commission models which only support equities.
"""
allowed_asset_types = (Equity,)
class FutureCommissionModel(with_metaclass(AllowedAssetMarker,
CommissionModel)):
"""
Base class for commission models which only support futures.
"""
allowed_asset_types = (Future,)
def calculate_per_unit_commission(order,
transaction,
cost_per_unit,
initial_commission,
min_trade_cost):
"""
If there is a minimum commission:
If the order hasn't had a commission paid yet, pay the minimum
commission.
If the order has paid a commission, start paying additional
commission once the minimum commission has been reached.
If there is no minimum commission:
Pay commission based on number of units in the transaction.
"""
additional_commission = abs(transaction.amount * cost_per_unit)
if order.commission == 0:
# no commission paid yet, pay at least the minimum plus a one-time
# exchange fee.
return max(min_trade_cost, additional_commission + initial_commission)
else:
# we've already paid some commission, so figure out how much we
# would be paying if we only counted per unit.
per_unit_total = \
abs(order.filled * cost_per_unit) + \
additional_commission + \
initial_commission
if per_unit_total < min_trade_cost:
# if we haven't hit the minimum threshold yet, don't pay
# additional commission
return 0
else:
# we've exceeded the threshold, so pay more commission.
return per_unit_total - order.commission
class PerShare(EquityCommissionModel):
"""
Calculates a commission for a transaction based on a per share cost with
an optional minimum cost per trade.
Parameters
----------
cost : float, optional
The amount of commissions paid per share traded. Default is one tenth
of a cent per share.
min_trade_cost : float, optional
The minimum amount of commissions paid per trade. Default is no
minimum.
Notes
-----
This is zipline's default commission model for equities.
"""
def __init__(self,
cost=DEFAULT_PER_SHARE_COST,
min_trade_cost=DEFAULT_MINIMUM_COST_PER_EQUITY_TRADE):
self.cost_per_share = float(cost)
self.min_trade_cost = min_trade_cost or 0
def __repr__(self):
return (
'{class_name}(cost_per_share={cost_per_share}, '
'min_trade_cost={min_trade_cost})'
.format(
class_name=self.__class__.__name__,
cost_per_share=self.cost_per_share,
min_trade_cost=self.min_trade_cost,
)
)
def calculate(self, order, transaction):
return calculate_per_unit_commission(
order=order,
transaction=transaction,
cost_per_unit=self.cost_per_share,
initial_commission=0,
min_trade_cost=self.min_trade_cost,
)
class PerContract(FutureCommissionModel):
"""
Calculates a commission for a transaction based on a per contract cost with
an optional minimum cost per trade.
Parameters
----------
cost : float or dict
The amount of commissions paid per contract traded. If given a float,
the commission for all futures contracts is the same. If given a
dictionary, it must map root symbols to the commission cost for
contracts of that symbol.
exchange_fee : float or dict
A flat-rate fee charged by the exchange per trade. This value is a
constant, one-time charge no matter how many contracts are being
traded. If given a float, the fee for all contracts is the same. If
given a dictionary, it must map root symbols to the fee for contracts
of that symbol.
min_trade_cost : float, optional
The minimum amount of commissions paid per trade.
"""
def __init__(self,
cost,
exchange_fee,
min_trade_cost=DEFAULT_MINIMUM_COST_PER_FUTURE_TRADE):
# If 'cost' or 'exchange fee' are constants, use a dummy mapping to
# treat them as a dictionary that always returns the same value.
# NOTE: These dictionary does not handle unknown root symbols, so it
# may be worth revisiting this behavior.
if isinstance(cost, (int, float)):
self._cost_per_contract = DummyMapping(float(cost))
else:
# Cost per contract is a dictionary. If the user's dictionary does
# not provide a commission cost for a certain contract, fall back
# on the pre-defined cost values per root symbol.
self._cost_per_contract = defaultdict(
lambda: DEFAULT_PER_CONTRACT_COST, **cost
)
if isinstance(exchange_fee, (int, float)):
self._exchange_fee = DummyMapping(float(exchange_fee))
else:
# Exchange fee is a dictionary. If the user's dictionary does not
# provide an exchange fee for a certain contract, fall back on the
# pre-defined exchange fees per root symbol.
self._exchange_fee = merge(
FUTURE_EXCHANGE_FEES_BY_SYMBOL, exchange_fee,
)
self.min_trade_cost = min_trade_cost or 0
def __repr__(self):
if isinstance(self._cost_per_contract, DummyMapping):
# Cost per contract is a constant, so extract it.
cost_per_contract = self._cost_per_contract['dummy key']
else:
cost_per_contract = '<varies>'
if isinstance(self._exchange_fee, DummyMapping):
# Exchange fee is a constant, so extract it.
exchange_fee = self._exchange_fee['dummy key']
else:
exchange_fee = '<varies>'
return (
'{class_name}(cost_per_contract={cost_per_contract}, '
'exchange_fee={exchange_fee}, min_trade_cost={min_trade_cost})'
.format(
class_name=self.__class__.__name__,
cost_per_contract=cost_per_contract,
exchange_fee=exchange_fee,
min_trade_cost=self.min_trade_cost,
)
)
def calculate(self, order, transaction):
root_symbol = order.asset.root_symbol
cost_per_contract = self._cost_per_contract[root_symbol]
exchange_fee = self._exchange_fee[root_symbol]
return calculate_per_unit_commission(
order=order,
transaction=transaction,
cost_per_unit=cost_per_contract,
initial_commission=exchange_fee,
min_trade_cost=self.min_trade_cost,
)
class PerTrade(CommissionModel):
"""
Calculates a commission for a transaction based on a per trade cost.
For orders that require multiple fills, the full commission is charged to
the first fill.
Parameters
----------
cost : float, optional
The flat amount of commissions paid per equity trade.
"""
def __init__(self, cost=DEFAULT_MINIMUM_COST_PER_EQUITY_TRADE):
"""
Cost parameter is the cost of a trade, regardless of share count.
$5.00 per trade is fairly typical of discount brokers.
"""
# Cost needs to be floating point so that calculation using division
# logic does not floor to an integer.
self.cost = float(cost)
def __repr__(self):
return '{class_name}(cost_per_trade={cost})'.format(
class_name=self.__class__.__name__, cost=self.cost,
)
def calculate(self, order, transaction):
"""
If the order hasn't had a commission paid yet, pay the fixed
commission.
"""
if order.commission == 0:
# if the order hasn't had a commission attributed to it yet,
# that's what we need to pay.
return self.cost
else:
# order has already had commission attributed, so no more
# commission.
return 0.0
class PerFutureTrade(PerContract):
"""
Calculates a commission for a transaction based on a per trade cost.
Parameters
----------
cost : float or dict
The flat amount of commissions paid per trade, regardless of the number
of contracts being traded. If given a float, the commission for all
futures contracts is the same. If given a dictionary, it must map root
symbols to the commission cost for trading contracts of that symbol.
"""
def __init__(self, cost=DEFAULT_MINIMUM_COST_PER_FUTURE_TRADE):
# The per-trade cost can be represented as the exchange fee in a
# per-contract model because the exchange fee is just a one time cost
# incurred on the first fill.
super(PerFutureTrade, self).__init__(
cost=0, exchange_fee=cost, min_trade_cost=0,
)
self._cost_per_trade = self._exchange_fee
def __repr__(self):
if isinstance(self._cost_per_trade, DummyMapping):
# Cost per trade is a constant, so extract it.
cost_per_trade = self._cost_per_trade['dummy key']
else:
cost_per_trade = '<varies>'
return '{class_name}(cost_per_trade={cost_per_trade})'.format(
class_name=self.__class__.__name__, cost_per_trade=cost_per_trade,
)
class PerDollar(EquityCommissionModel):
"""
Model commissions by applying a fixed cost per dollar transacted.
Parameters
----------
cost : float, optional
The flat amount of commissions paid per dollar of equities
traded. Default is a commission of $0.0015 per dollar transacted.
"""
def __init__(self, cost=DEFAULT_PER_DOLLAR_COST):
"""
Cost parameter is the cost of a trade per-dollar. 0.0015
on $1 million means $1,500 commission (=1M * 0.0015)
"""
self.cost_per_dollar = float(cost)
def __repr__(self):
return "{class_name}(cost_per_dollar={cost})".format(
class_name=self.__class__.__name__,
cost=self.cost_per_dollar)
def calculate(self, order, transaction):
"""
Pay commission based on dollar value of shares.
"""
cost_per_share = transaction.price * self.cost_per_dollar
return abs(transaction.amount) * cost_per_share | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/commission.py | commission.py |
import abc
from sys import float_info
from six import with_metaclass
from numpy import isfinite
import zipline.utils.math_utils as zp_math
from zipline.errors import BadOrderParameters
from zipline.utils.compat import consistent_round
class ExecutionStyle(with_metaclass(abc.ABCMeta)):
"""Base class for order execution styles.
"""
_exchange = None
@abc.abstractmethod
def get_limit_price(self, is_buy):
"""
Get the limit price for this order.
Returns either None or a numerical value >= 0.
"""
raise NotImplementedError
@abc.abstractmethod
def get_stop_price(self, is_buy):
"""
Get the stop price for this order.
Returns either None or a numerical value >= 0.
"""
raise NotImplementedError
@property
def exchange(self):
"""
The exchange to which this order should be routed.
"""
return self._exchange
class MarketOrder(ExecutionStyle):
"""
Execution style for orders to be filled at current market price.
This is the default for orders placed with :func:`~zipline.api.order`.
"""
def __init__(self, exchange=None):
self._exchange = exchange
def get_limit_price(self, _is_buy):
return None
def get_stop_price(self, _is_buy):
return None
class LimitOrder(ExecutionStyle):
"""
Execution style for orders to be filled at a price equal to or better than
a specified limit price.
Parameters
----------
limit_price : float
Maximum price for buys, or minimum price for sells, at which the order
should be filled.
"""
def __init__(self, limit_price, asset=None, exchange=None):
check_stoplimit_prices(limit_price, 'limit')
self.limit_price = limit_price
self._exchange = exchange
self.asset = asset
def get_limit_price(self, is_buy):
return asymmetric_round_price(
self.limit_price,
is_buy,
tick_size=(0.01 if self.asset is None else self.asset.tick_size)
)
def get_stop_price(self, _is_buy):
return None
class StopOrder(ExecutionStyle):
"""
Execution style representing a market order to be placed if market price
reaches a threshold.
Parameters
----------
stop_price : float
Price threshold at which the order should be placed. For sells, the
order will be placed if market price falls below this value. For buys,
the order will be placed if market price rises above this value.
"""
def __init__(self, stop_price, asset=None, exchange=None):
check_stoplimit_prices(stop_price, 'stop')
self.stop_price = stop_price
self._exchange = exchange
self.asset = asset
def get_limit_price(self, _is_buy):
return None
def get_stop_price(self, is_buy):
return asymmetric_round_price(
self.stop_price,
not is_buy,
tick_size=(0.01 if self.asset is None else self.asset.tick_size)
)
class StopLimitOrder(ExecutionStyle):
"""
Execution style representing a limit order to be placed if market price
reaches a threshold.
Parameters
----------
limit_price : float
Maximum price for buys, or minimum price for sells, at which the order
should be filled, if placed.
stop_price : float
Price threshold at which the order should be placed. For sells, the
order will be placed if market price falls below this value. For buys,
the order will be placed if market price rises above this value.
"""
def __init__(self, limit_price, stop_price, asset=None, exchange=None):
check_stoplimit_prices(limit_price, 'limit')
check_stoplimit_prices(stop_price, 'stop')
self.limit_price = limit_price
self.stop_price = stop_price
self._exchange = exchange
self.asset = asset
def get_limit_price(self, is_buy):
return asymmetric_round_price(
self.limit_price,
is_buy,
tick_size=(0.01 if self.asset is None else self.asset.tick_size)
)
def get_stop_price(self, is_buy):
return asymmetric_round_price(
self.stop_price,
not is_buy,
tick_size=(0.01 if self.asset is None else self.asset.tick_size)
)
def asymmetric_round_price(price, prefer_round_down, tick_size, diff=0.95):
"""
Asymmetric rounding function for adjusting prices to the specified number
of places in a way that "improves" the price. For limit prices, this means
preferring to round down on buys and preferring to round up on sells.
For stop prices, it means the reverse.
If prefer_round_down == True:
When .05 below to .95 above a specified decimal place, use it.
If prefer_round_down == False:
When .95 below to .05 above a specified decimal place, use it.
In math-speak:
If prefer_round_down: [<X-1>.0095, X.0195) -> round to X.01.
If not prefer_round_down: (<X-1>.0005, X.0105] -> round to X.01.
"""
precision = zp_math.number_of_decimal_places(tick_size)
multiplier = int(tick_size * (10 ** precision))
diff -= 0.5 # shift the difference down
diff *= (10 ** -precision) # adjust diff to precision of tick size
diff *= multiplier # adjust diff to value of tick_size
# Subtracting an epsilon from diff to enforce the open-ness of the upper
# bound on buys and the lower bound on sells. Using the actual system
# epsilon doesn't quite get there, so use a slightly less epsilon-ey value.
epsilon = float_info.epsilon * 10
diff = diff - epsilon
# relies on rounding half away from zero, unlike numpy's bankers' rounding
rounded = tick_size * consistent_round(
(price - (diff if prefer_round_down else -diff)) / tick_size
)
if zp_math.tolerant_equals(rounded, 0.0):
return 0.0
return rounded
def check_stoplimit_prices(price, label):
"""
Check to make sure the stop/limit prices are reasonable and raise
a BadOrderParameters exception if not.
"""
try:
if not isfinite(price):
raise BadOrderParameters(
msg="Attempted to place an order with a {} price "
"of {}.".format(label, price)
)
# This catches arbitrary objects
except TypeError:
raise BadOrderParameters(
msg="Attempted to place an order with a {} price "
"of {}.".format(label, type(price))
)
if price < 0:
raise BadOrderParameters(
msg="Can't place a {} order with a negative price.".format(label)
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/execution.py | execution.py |
import logbook
import pandas as pd
from zipline.utils.memoize import remember_last
from zipline.utils.pandas_utils import normalize_date
log = logbook.Logger('Trading')
DEFAULT_CAPITAL_BASE = 1e5
class SimulationParameters(object):
def __init__(self,
start_session,
end_session,
trading_calendar,
capital_base=DEFAULT_CAPITAL_BASE,
emission_rate='daily',
data_frequency='daily',
arena='backtest',
execution_id=None):
# assert type(start_session) == pd.Timestamp
# assert type(end_session) == pd.Timestamp
assert trading_calendar is not None, \
"Must pass in trading calendar!"
assert start_session <= end_session, \
"Period start falls after period end."
assert start_session <= trading_calendar.last_trading_session, \
"Period start falls after the last known trading day."
assert end_session >= trading_calendar.first_trading_session, \
"Period end falls before the first known trading day."
# chop off any minutes or hours on the given start and end dates,
# as we only support session labels here (and we represent session
# labels as midnight UTC).
# self._start_session = normalize_date(start_session)
# self._end_session = normalize_date(end_session)
self._start_session = start_session
self._end_session = end_session
self._capital_base = capital_base
if execution_id:
self._execution_id = execution_id
self._emission_rate = emission_rate
self._data_frequency = data_frequency
# copied to algorithm's environment for runtime access
self._arena = arena
self._trading_calendar = trading_calendar
if not trading_calendar.is_session(self._start_session):
# if the start date is not a valid session in this calendar,
# push it forward to the first valid session
self._start_session = trading_calendar.minute_to_session_label(
pd.Timestamp(self._start_session)
)
if not trading_calendar.is_session(self._end_session):
# if the end date is not a valid session in this calendar,
# pull it backward to the last valid session before the given
# end date.
self._end_session = trading_calendar.minute_to_session_label(
pd.Timestamp(self._end_session), direction="previous"
)
self._first_open = trading_calendar.open_and_close_for_session(
self._start_session
)[0]
self._last_close = trading_calendar.open_and_close_for_session(
self._end_session
)[1]
@property
def capital_base(self):
return self._capital_base
@property
def emission_rate(self):
return self._emission_rate
@property
def data_frequency(self):
return self._data_frequency
@data_frequency.setter
def data_frequency(self, val):
self._data_frequency = val
@property
def arena(self):
return self._arena
@arena.setter
def arena(self, val):
self._arena = val
@property
def start_session(self):
return self._start_session
@property
def end_session(self):
return self._end_session
@property
def first_open(self):
return self._first_open
@property
def last_close(self):
return self._last_close
@property
def trading_calendar(self):
return self._trading_calendar
@property
@remember_last
def sessions(self):
return self._trading_calendar.sessions_in_range(
self.start_session,
self.end_session
)
def create_new(self, start_session, end_session, data_frequency=None):
if data_frequency is None:
data_frequency = self.data_frequency
return SimulationParameters(
start_session,
end_session,
self._trading_calendar,
capital_base=self.capital_base,
emission_rate=self.emission_rate,
data_frequency=data_frequency,
arena=self.arena
)
def __repr__(self):
return """
{class_name}(
start_session={start_session},
end_session={end_session},
capital_base={capital_base},
data_frequency={data_frequency},
emission_rate={emission_rate},
first_open={first_open},
last_close={last_close},
trading_calendar={trading_calendar}
)\
""".format(class_name=self.__class__.__name__,
start_session=self.start_session,
end_session=self.end_session,
capital_base=self.capital_base,
data_frequency=self.data_frequency,
emission_rate=self.emission_rate,
first_open=self.first_open,
last_close=self.last_close,
trading_calendar=self._trading_calendar) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/trading.py | trading.py |
from __future__ import division
from abc import abstractmethod
import math
import numpy as np
from pandas import isnull
from six import with_metaclass
from toolz import merge
from zipline.assets import Equity, Future
from zipline.errors import HistoryWindowStartsBeforeData
from zipline.finance.constants import ROOT_SYMBOL_TO_ETA
from zipline.finance.shared import AllowedAssetMarker, FinancialModelMeta
from zipline.finance.transaction import create_transaction
from zipline.utils.cache import ExpiringCache
from zipline.utils.dummy import DummyMapping
from zipline.utils.input_validation import (expect_bounded,
expect_strictly_bounded)
SELL = 1 << 0
BUY = 1 << 1
STOP = 1 << 2
LIMIT = 1 << 3
SQRT_252 = math.sqrt(252)
DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT = 0.025
DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT = 0.05
class LiquidityExceeded(Exception):
pass
def fill_price_worse_than_limit_price(fill_price, order):
"""
Checks whether the fill price is worse than the order's limit price.
Parameters
----------
fill_price: float
The price to check.
order: zipline.finance.order.Order
The order whose limit price to check.
Returns
-------
bool: Whether the fill price is above the limit price (for a buy) or below
the limit price (for a sell).
"""
if order.limit:
# this is tricky! if an order with a limit price has reached
# the limit price, we will try to fill the order. do not fill
# these shares if the impacted price is worse than the limit
# price. return early to avoid creating the transaction.
# buy order is worse if the impacted price is greater than
# the limit price. sell order is worse if the impacted price
# is less than the limit price
if (order.direction > 0 and fill_price > order.limit) or \
(order.direction < 0 and fill_price < order.limit):
return True
return False
class SlippageModel(with_metaclass(FinancialModelMeta)):
"""
Abstract base class for slippage models.
Slippage models are responsible for the rates and prices at which orders
fill during a simulation.
To implement a new slippage model, create a subclass of
:class:`~zipline.finance.slippage.SlippageModel` and implement
:meth:`process_order`.
Methods
-------
process_order(data, order)
Attributes
----------
volume_for_bar : int
Number of shares that have already been filled for the
currently-filling asset in the current minute. This attribute is
maintained automatically by the base class. It can be used by
subclasses to keep track of the total amount filled if there are
multiple open orders for a single asset.
Notes
-----
Subclasses that define their own constructors should call
``super(<subclass name>, self).__init__()`` before performing other
initialization.
"""
# Asset types that are compatible with the given model.
allowed_asset_types = (Equity, Future)
def __init__(self):
self._volume_for_bar = 0
@property
def volume_for_bar(self):
return self._volume_for_bar
@abstractmethod
def process_order(self, data, order):
"""
Compute the number of shares and price to fill for ``order`` in the
current minute.
Parameters
----------
data : zipline.protocol.BarData
The data for the given bar.
order : zipline.finance.order.Order
The order to simulate.
Returns
-------
execution_price : float
The price of the fill.
execution_volume : int
The number of shares that should be filled. Must be between ``0``
and ``order.amount - order.filled``. If the amount filled is less
than the amount remaining, ``order`` will remain open and will be
passed again to this method in the next minute.
Raises
------
zipline.finance.slippage.LiquidityExceeded
May be raised if no more orders should be processed for the current
asset during the current bar.
Notes
-----
Before this method is called, :attr:`volume_for_bar` will be set to the
number of shares that have already been filled for ``order.asset`` in
the current minute.
:meth:`process_order` is not called by the base class on bars for which
there was no historical volume.
"""
raise NotImplementedError('process_order')
def simulate(self, data, asset, orders_for_asset):
self._volume_for_bar = 0
volume = data.current(asset, "volume")
if volume == 0:
return
# can use the close price, since we verified there's volume in this
# bar.
price = data.current(asset, "close")
# BEGIN
#
# Remove this block after fixing data to ensure volume always has
# corresponding price.
if isnull(price):
return
# END
dt = data.current_dt
for order in orders_for_asset:
if order.open_amount == 0:
continue
order.check_triggers(price, dt)
if not order.triggered:
continue
txn = None
try:
execution_price, execution_volume = \
self.process_order(data, order)
if execution_price is not None:
txn = create_transaction(
order,
data.current_dt,
execution_price,
execution_volume
)
except LiquidityExceeded:
break
if txn:
self._volume_for_bar += abs(txn.amount)
yield order, txn
def asdict(self):
return self.__dict__
class NoSlippage(SlippageModel):
"""A slippage model where all orders fill immediately and completely at the
current close price.
Notes
-----
This is primarily used for testing.
"""
@staticmethod
def process_order(data, order):
return (
data.current(order.asset, 'close'),
order.amount,
)
class EquitySlippageModel(with_metaclass(AllowedAssetMarker, SlippageModel)):
"""
Base class for slippage models which only support equities.
"""
allowed_asset_types = (Equity,)
class FutureSlippageModel(with_metaclass(AllowedAssetMarker, SlippageModel)):
"""
Base class for slippage models which only support futures.
"""
allowed_asset_types = (Future,)
class VolumeShareSlippage(SlippageModel):
"""
Model slippage as a quadratic function of percentage of historical volume.
Orders to buy will be filled at::
price * (1 + price_impact * (volume_share ** 2))
Orders to sell will be filled at::
price * (1 - price_impact * (volume_share ** 2))
where ``price`` is the close price for the bar, and ``volume_share`` is the
percentage of minutely volume filled, up to a max of ``volume_limit``.
Parameters
----------
volume_limit : float, optional
Maximum percent of historical volume that can fill in each bar. 0.5
means 50% of historical volume. 1.0 means 100%. Default is 0.025 (i.e.,
2.5%).
price_impact : float, optional
Scaling coefficient for price impact. Larger values will result in more
simulated price impact. Smaller values will result in less simulated
price impact. Default is 0.1.
"""
def __init__(self,
volume_limit=DEFAULT_EQUITY_VOLUME_SLIPPAGE_BAR_LIMIT,
price_impact=0.1):
super(VolumeShareSlippage, self).__init__()
self.volume_limit = volume_limit
self.price_impact = price_impact
def __repr__(self):
return """
{class_name}(
volume_limit={volume_limit},
price_impact={price_impact})
""".strip().format(class_name=self.__class__.__name__,
volume_limit=self.volume_limit,
price_impact=self.price_impact)
def process_order(self, data, order):
volume = data.current(order.asset, "volume")
max_volume = self.volume_limit * volume
# price impact accounts for the total volume of transactions
# created against the current minute bar
remaining_volume = max_volume - self.volume_for_bar
if remaining_volume < 1:
# we can't fill any more transactions
raise LiquidityExceeded()
# the current order amount will be the min of the
# volume available in the bar or the open amount.
cur_volume = int(min(remaining_volume, abs(order.open_amount)))
if cur_volume < 1:
return None, None
# tally the current amount into our total amount ordered.
# total amount will be used to calculate price impact
total_volume = self.volume_for_bar + cur_volume
volume_share = min(total_volume / volume,
self.volume_limit)
price = data.current(order.asset, "close")
# BEGIN
#
# Remove this block after fixing data to ensure volume always has
# corresponding price.
if isnull(price):
return
# END
simulated_impact = volume_share ** 2 \
* math.copysign(self.price_impact, order.direction) \
* price
impacted_price = price + simulated_impact
if fill_price_worse_than_limit_price(impacted_price, order):
return None, None
return (
impacted_price,
math.copysign(cur_volume, order.direction)
)
class FixedSlippage(SlippageModel):
"""
Simple model assuming a fixed-size spread for all assets.
Parameters
----------
spread : float, optional
Size of the assumed spread for all assets.
Orders to buy will be filled at ``close + (spread / 2)``.
Orders to sell will be filled at ``close - (spread / 2)``.
Notes
-----
This model does not impose limits on the size of fills. An order for an
asset will always be filled as soon as any trading activity occurs in the
order's asset, even if the size of the order is greater than the historical
volume.
"""
def __init__(self, spread=0.0):
super(FixedSlippage, self).__init__()
self.spread = spread
def __repr__(self):
return '{class_name}(spread={spread})'.format(
class_name=self.__class__.__name__, spread=self.spread,
)
def process_order(self, data, order):
price = data.current(order.asset, "close")
return (
price + (self.spread / 2.0 * order.direction),
order.amount
)
class MarketImpactBase(SlippageModel):
"""
Base class for slippage models which compute a simulated price impact
according to a history lookback.
"""
NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = 10.0 / 10000
def __init__(self):
super(MarketImpactBase, self).__init__()
self._window_data_cache = ExpiringCache()
@abstractmethod
def get_txn_volume(self, data, order):
"""
Return the number of shares we would like to order in this minute.
Parameters
----------
data : BarData
order : Order
Return
------
int : the number of shares
"""
raise NotImplementedError('get_txn_volume')
@abstractmethod
def get_simulated_impact(self,
order,
current_price,
current_volume,
txn_volume,
mean_volume,
volatility):
"""
Calculate simulated price impact.
Parameters
----------
order : The order being processed.
current_price : Current price of the asset being ordered.
current_volume : Volume of the asset being ordered for the current bar.
txn_volume : Number of shares/contracts being ordered.
mean_volume : Trailing ADV of the asset.
volatility : Annualized daily volatility of returns.
Return
------
int : impact on the current price.
"""
raise NotImplementedError('get_simulated_impact')
def process_order(self, data, order):
if order.open_amount == 0:
return None, None
minute_data = data.current(order.asset, ['volume', 'high', 'low'])
mean_volume, volatility = self._get_window_data(data, order.asset, 20)
# Price to use is the average of the minute bar's open and close.
price = np.mean([minute_data['high'], minute_data['low']])
volume = minute_data['volume']
if not volume:
return None, None
txn_volume = int(
min(self.get_txn_volume(data, order), abs(order.open_amount))
)
# If the computed transaction volume is zero or a decimal value, 'int'
# will round it down to zero. In that case just bail.
if txn_volume == 0:
return None, None
if mean_volume == 0 or np.isnan(volatility):
# If this is the first day the contract exists or there is no
# volume history, default to a conservative estimate of impact.
simulated_impact = price * self.NO_DATA_VOLATILITY_SLIPPAGE_IMPACT
else:
simulated_impact = self.get_simulated_impact(
order=order,
current_price=price,
current_volume=volume,
txn_volume=txn_volume,
mean_volume=mean_volume,
volatility=volatility,
)
impacted_price = \
price + math.copysign(simulated_impact, order.direction)
if fill_price_worse_than_limit_price(impacted_price, order):
return None, None
return impacted_price, math.copysign(txn_volume, order.direction)
def _get_window_data(self, data, asset, window_length):
"""
Internal utility method to return the trailing mean volume over the
past 'window_length' days, and volatility of close prices for a
specific asset.
Parameters
----------
data : The BarData from which to fetch the daily windows.
asset : The Asset whose data we are fetching.
window_length : Number of days of history used to calculate the mean
volume and close price volatility.
Returns
-------
(mean volume, volatility)
"""
try:
values = self._window_data_cache.get(asset, data.current_session)
except KeyError:
try:
# Add a day because we want 'window_length' complete days,
# excluding the current day.
volume_history = data.history(
asset, 'volume', window_length + 1, '1d',
)
close_history = data.history(
asset, 'close', window_length + 1, '1d',
)
except HistoryWindowStartsBeforeData:
# If there is not enough data to do a full history call, return
# values as if there was no data.
return 0, np.NaN
# Exclude the first value of the percent change array because it is
# always just NaN.
close_volatility = close_history[:-1].pct_change()[1:].std(
skipna=False,
)
values = {
'volume': volume_history[:-1].mean(),
'close': close_volatility * SQRT_252,
}
self._window_data_cache.set(asset, values, data.current_session)
return values['volume'], values['close']
class VolatilityVolumeShare(MarketImpactBase):
"""
Model slippage for futures contracts according to the following formula:
new_price = price + (price * MI / 10000),
where 'MI' is market impact, which is defined as:
MI = eta * sigma * sqrt(psi)
- ``eta`` is a constant which varies by root symbol.
- ``sigma`` is 20-day annualized volatility.
- ``psi`` is the volume traded in the given bar divided by 20-day ADV.
Parameters
----------
volume_limit : float
Maximum percentage (as a decimal) of a bar's total volume that can be
traded.
eta : float or dict
Constant used in the market impact formula. If given a float, the eta
for all futures contracts is the same. If given a dictionary, it must
map root symbols to the eta for contracts of that symbol.
"""
NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = 7.5 / 10000
allowed_asset_types = (Future,)
def __init__(self, volume_limit, eta=ROOT_SYMBOL_TO_ETA):
super(VolatilityVolumeShare, self).__init__()
self.volume_limit = volume_limit
# If 'eta' is a constant, use a dummy mapping to treat it as a
# dictionary that always returns the same value.
# NOTE: This dictionary does not handle unknown root symbols, so it may
# be worth revisiting this behavior.
if isinstance(eta, (int, float)):
self._eta = DummyMapping(float(eta))
else:
# Eta is a dictionary. If the user's dictionary does not provide a
# value for a certain contract, fall back on the pre-defined eta
# values per root symbol.
self._eta = merge(ROOT_SYMBOL_TO_ETA, eta)
def __repr__(self):
if isinstance(self._eta, DummyMapping):
# Eta is a constant, so extract it.
eta = self._eta['dummy key']
else:
eta = '<varies>'
return '{class_name}(volume_limit={volume_limit}, eta={eta})'.format(
class_name=self.__class__.__name__,
volume_limit=self.volume_limit,
eta=eta,
)
def get_simulated_impact(self,
order,
current_price,
current_volume,
txn_volume,
mean_volume,
volatility):
eta = self._eta[order.asset.root_symbol]
psi = txn_volume / mean_volume
market_impact = eta * volatility * math.sqrt(psi)
# We divide by 10,000 because this model computes to basis points.
# To convert from bps to % we need to divide by 100, then again to
# convert from % to fraction.
return (current_price * market_impact) / 10000
def get_txn_volume(self, data, order):
volume = data.current(order.asset, 'volume')
return volume * self.volume_limit
class FixedBasisPointsSlippage(SlippageModel):
"""
Model slippage as a fixed percentage difference from historical minutely
close price, limiting the size of fills to a fixed percentage of historical
minutely volume.
Orders to buy are filled at::
historical_price * (1 + (basis_points * 0.0001))
Orders to sell are filled at::
historical_price * (1 - (basis_points * 0.0001))
Fill sizes are capped at::
historical_volume * volume_limit
Parameters
----------
basis_points : float, optional
Number of basis points of slippage to apply for each fill. Default
is 5 basis points.
volume_limit : float, optional
Fraction of trading volume that can be filled each minute. Default is
10% of trading volume.
Notes
-----
- A basis point is one one-hundredth of a percent.
- This class, default-constructed, is zipline's default slippage model for
equities.
"""
@expect_bounded(
basis_points=(0, None),
__funcname='FixedBasisPointsSlippage',
)
@expect_strictly_bounded(
volume_limit=(0, None),
__funcname='FixedBasisPointsSlippage',
)
def __init__(self, basis_points=5.0, volume_limit=0.1):
super(FixedBasisPointsSlippage, self).__init__()
self.basis_points = basis_points
self.percentage = self.basis_points / 10000.0
self.volume_limit = volume_limit
def __repr__(self):
return """
{class_name}(
basis_points={basis_points},
volume_limit={volume_limit},
)
""".strip().format(
class_name=self.__class__.__name__,
basis_points=self.basis_points,
volume_limit=self.volume_limit,
)
def process_order(self, data, order):
volume = data.current(order.asset, "volume")
max_volume = int(self.volume_limit * volume)
price = data.current(order.asset, "close")
shares_to_fill = min(abs(order.open_amount),
max_volume - self.volume_for_bar)
if shares_to_fill == 0:
raise LiquidityExceeded()
return (
price + price * (self.percentage * order.direction),
shares_to_fill * order.direction
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/slippage.py | slippage.py |
import math
import uuid
from six import text_type
import zipline.protocol as zp
from zipline.assets import Asset
from zipline.utils.enum import enum
from zipline.utils.input_validation import expect_types
ORDER_STATUS = enum(
'OPEN',
'FILLED',
'CANCELLED',
'REJECTED',
'HELD',
)
SELL = 1 << 0
BUY = 1 << 1
STOP = 1 << 2
LIMIT = 1 << 3
ORDER_FIELDS_TO_IGNORE = {'type', 'direction', '_status', 'asset'}
class Order(object):
# using __slots__ to save on memory usage. Simulations can create many
# Order objects and we keep them all in memory, so it's worthwhile trying
# to cut down on the memory footprint of this object.
__slots__ = ["id", "dt", "reason", "created", "asset", "amount", "filled",
"commission", "_status", "stop", "limit", "stop_reached",
"limit_reached", "direction", "type", "broker_order_id"]
@expect_types(asset=Asset)
def __init__(self, dt, asset, amount, stop=None, limit=None, filled=0,
commission=0, id=None):
"""
@dt - datetime.datetime that the order was placed
@asset - asset for the order.
@amount - the number of shares to buy/sell
a positive sign indicates a buy
a negative sign indicates a sell
@filled - how many shares of the order have been filled so far
"""
# get a string representation of the uuid.
self.id = self.make_id() if id is None else id
self.dt = dt
self.reason = None
self.created = dt
self.asset = asset
self.amount = amount
self.filled = filled
self.commission = commission
self._status = ORDER_STATUS.OPEN
self.stop = stop
self.limit = limit
self.stop_reached = False
self.limit_reached = False
self.direction = math.copysign(1, self.amount)
self.type = zp.DATASOURCE_TYPE.ORDER
self.broker_order_id = None
@staticmethod
def make_id():
return uuid.uuid4().hex
def to_dict(self):
dct = {name: getattr(self, name)
for name in self.__slots__
if name not in ORDER_FIELDS_TO_IGNORE}
if self.broker_order_id is None:
del dct['broker_order_id']
# Adding 'sid' for backwards compatibility with downstream consumers.
dct['sid'] = self.asset
dct['status'] = self.status
return dct
@property
def sid(self):
# For backwards compatibility because we pass this object to
# custom slippage models.
return self.asset
def to_api_obj(self):
pydict = self.to_dict()
obj = zp.Order(initial_values=pydict)
return obj
def check_triggers(self, price, dt):
"""
Update internal state based on price triggers and the
trade event's price.
"""
stop_reached, limit_reached, sl_stop_reached = \
self.check_order_triggers(price)
if (stop_reached, limit_reached) \
!= (self.stop_reached, self.limit_reached):
self.dt = dt
self.stop_reached = stop_reached
self.limit_reached = limit_reached
if sl_stop_reached:
# Change the STOP LIMIT order into a LIMIT order
self.stop = None
def check_order_triggers(self, current_price):
"""
Given an order and a trade event, return a tuple of
(stop_reached, limit_reached).
For market orders, will return (False, False).
For stop orders, limit_reached will always be False.
For limit orders, stop_reached will always be False.
For stop limit orders a Boolean is returned to flag
that the stop has been reached.
Orders that have been triggered already (price targets reached),
the order's current values are returned.
"""
if self.triggered:
return (self.stop_reached, self.limit_reached, False)
stop_reached = False
limit_reached = False
sl_stop_reached = False
order_type = 0
if self.amount > 0:
order_type |= BUY
else:
order_type |= SELL
if self.stop is not None:
order_type |= STOP
if self.limit is not None:
order_type |= LIMIT
if order_type == BUY | STOP | LIMIT:
if current_price >= self.stop:
sl_stop_reached = True
if current_price <= self.limit:
limit_reached = True
elif order_type == SELL | STOP | LIMIT:
if current_price <= self.stop:
sl_stop_reached = True
if current_price >= self.limit:
limit_reached = True
elif order_type == BUY | STOP:
if current_price >= self.stop:
stop_reached = True
elif order_type == SELL | STOP:
if current_price <= self.stop:
stop_reached = True
elif order_type == BUY | LIMIT:
if current_price <= self.limit:
limit_reached = True
elif order_type == SELL | LIMIT:
# This is a SELL LIMIT order
if current_price >= self.limit:
limit_reached = True
return (stop_reached, limit_reached, sl_stop_reached)
def handle_split(self, ratio):
# update the amount, limit_price, and stop_price
# by the split's ratio
# info here: http://finra.complinet.com/en/display/display_plain.html?
# rbid=2403&element_id=8950&record_id=12208&print=1
# new_share_amount = old_share_amount / ratio
# new_price = old_price * ratio
self.amount = int(self.amount / ratio)
if self.limit is not None:
self.limit = round(self.limit * ratio, 2)
if self.stop is not None:
self.stop = round(self.stop * ratio, 2)
@property
def status(self):
if not self.open_amount:
return ORDER_STATUS.FILLED
elif self._status == ORDER_STATUS.HELD and self.filled:
return ORDER_STATUS.OPEN
else:
return self._status
@status.setter
def status(self, status):
self._status = status
def cancel(self):
self.status = ORDER_STATUS.CANCELLED
def reject(self, reason=''):
self.status = ORDER_STATUS.REJECTED
self.reason = reason
def hold(self, reason=''):
self.status = ORDER_STATUS.HELD
self.reason = reason
@property
def open(self):
return self.status in [ORDER_STATUS.OPEN, ORDER_STATUS.HELD]
@property
def triggered(self):
"""
For a market order, True.
For a stop order, True IFF stop_reached.
For a limit order, True IFF limit_reached.
"""
if self.stop is not None and not self.stop_reached:
return False
if self.limit is not None and not self.limit_reached:
return False
return True
@property
def open_amount(self):
return self.amount - self.filled
def __repr__(self):
"""
String representation for this object.
"""
return "Order(%s)" % self.to_dict().__repr__()
def __unicode__(self):
"""
Unicode representation for this object.
"""
return text_type(repr(self)) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/order.py | order.py |
from __future__ import division
from math import copysign
import numpy as np
import logbook
from zipline.assets import Future
import zipline.protocol as zp
log = logbook.Logger('Performance')
class Position(object):
__slots__ = 'inner_position', 'protocol_position'
def __init__(self,
asset,
amount=0,
cost_basis=0.0,
last_sale_price=0.0,
last_sale_date=None):
inner = zp.InnerPosition(
asset=asset,
amount=amount,
cost_basis=cost_basis,
last_sale_price=last_sale_price,
last_sale_date=last_sale_date,
)
object.__setattr__(self, 'inner_position', inner)
object.__setattr__(self, 'protocol_position', zp.Position(inner))
def __getattr__(self, attr):
return getattr(self.inner_position, attr)
def __setattr__(self, attr, value):
setattr(self.inner_position, attr, value)
def earn_dividend(self, dividend):
"""
Register the number of shares we held at this dividend's ex date so
that we can pay out the correct amount on the dividend's pay date.
"""
return {
'amount': self.amount * dividend.amount
}
def earn_stock_dividend(self, stock_dividend):
"""
Register the number of shares we held at this dividend's ex date so
that we can pay out the correct amount on the dividend's pay date.
"""
return {
'payment_asset': stock_dividend.payment_asset,
'share_count': np.floor(
self.amount * float(stock_dividend.ratio)
)
}
def handle_split(self, asset, ratio):
"""
Update the position by the split ratio, and return the resulting
fractional share that will be converted into cash.
Returns the unused cash.
"""
if self.asset != asset:
raise Exception("updating split with the wrong asset!")
# adjust the # of shares by the ratio
# (if we had 100 shares, and the ratio is 3,
# we now have 33 shares)
# (old_share_count / ratio = new_share_count)
# (old_price * ratio = new_price)
# e.g., 33.333
raw_share_count = self.amount / float(ratio)
# e.g., 33
full_share_count = np.floor(raw_share_count)
# e.g., 0.333
fractional_share_count = raw_share_count - full_share_count
# adjust the cost basis to the nearest cent, e.g., 60.0
new_cost_basis = round(self.cost_basis * ratio, 2)
self.cost_basis = new_cost_basis
self.amount = full_share_count
return_cash = round(float(fractional_share_count * new_cost_basis), 2)
log.info("after split: " + str(self))
log.info("returning cash: " + str(return_cash))
# return the leftover cash, which will be converted into cash
# (rounded to the nearest cent)
return return_cash
def update(self, txn):
if self.asset != txn.asset:
raise Exception('updating position with txn for a '
'different asset')
total_shares = self.amount + txn.amount
if total_shares == 0:
self.cost_basis = 0.0
else:
prev_direction = copysign(1, self.amount)
txn_direction = copysign(1, txn.amount)
if prev_direction != txn_direction:
# we're covering a short or closing a position
if abs(txn.amount) > abs(self.amount):
# we've closed the position and gone short
# or covered the short position and gone long
self.cost_basis = txn.price
else:
prev_cost = self.cost_basis * self.amount
txn_cost = txn.amount * txn.price
total_cost = prev_cost + txn_cost
self.cost_basis = total_cost / total_shares
# Update the last sale price if txn is
# best data we have so far
if self.last_sale_date is None or txn.dt > self.last_sale_date:
self.last_sale_price = txn.price
self.last_sale_date = txn.dt
self.amount = total_shares
def adjust_commission_cost_basis(self, asset, cost):
"""
A note about cost-basis in zipline: all positions are considered
to share a cost basis, even if they were executed in different
transactions with different commission costs, different prices, etc.
Due to limitations about how zipline handles positions, zipline will
currently spread an externally-delivered commission charge across
all shares in a position.
"""
if asset != self.asset:
raise Exception('Updating a commission for a different asset?')
if cost == 0.0:
return
# If we no longer hold this position, there is no cost basis to
# adjust.
if self.amount == 0:
return
# We treat cost basis as the share price where we have broken even.
# For longs, commissions cause a relatively straight forward increase
# in the cost basis.
#
# For shorts, you actually want to decrease the cost basis because you
# break even and earn a profit when the share price decreases.
#
# Shorts are represented as having a negative `amount`.
#
# The multiplication and division by `amount` cancel out leaving the
# cost_basis positive, while subtracting the commission.
prev_cost = self.cost_basis * self.amount
if isinstance(asset, Future):
cost_to_use = cost / asset.price_multiplier
else:
cost_to_use = cost
new_cost = prev_cost + cost_to_use
self.cost_basis = new_cost / self.amount
def __repr__(self):
template = "asset: {asset}, amount: {amount}, cost_basis: {cost_basis}, \
last_sale_price: {last_sale_price}"
return template.format(
asset=self.asset,
amount=self.amount,
cost_basis=self.cost_basis,
last_sale_price=self.last_sale_price
)
def to_dict(self):
"""
Creates a dictionary representing the state of this position.
Returns a dict object of the form:
"""
return {
'sid': self.asset,
'amount': self.amount,
'cost_basis': self.cost_basis,
'last_sale_price': self.last_sale_price
} | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/position.py | position.py |
from __future__ import division
from collections import namedtuple, OrderedDict
from functools import partial
from math import isnan
import logbook
import numpy as np
import pandas as pd
from six import iteritems, itervalues, PY2
from zipline.assets import Future
from zipline.finance.transaction import Transaction
import zipline.protocol as zp
from zipline.utils.sentinel import sentinel
from .position import Position
from ._finance_ext import (
PositionStats,
calculate_position_tracker_stats,
update_position_last_sale_prices,
)
log = logbook.Logger('Performance')
class PositionTracker(object):
"""The current state of the positions held.
Parameters
----------
data_frequency : {'daily', 'minute'}
The data frequency of the simulation.
"""
def __init__(self, data_frequency):
self.positions = OrderedDict()
self._unpaid_dividends = {}
self._unpaid_stock_dividends = {}
self._positions_store = zp.Positions()
self.data_frequency = data_frequency
# cache the stats until something alters our positions
self._dirty_stats = True
self._stats = PositionStats.new()
def update_position(self,
asset,
amount=None,
last_sale_price=None,
last_sale_date=None,
cost_basis=None):
self._dirty_stats = True
if asset not in self.positions:
position = Position(asset)
self.positions[asset] = position
else:
position = self.positions[asset]
if amount is not None:
position.amount = amount
if last_sale_price is not None:
position.last_sale_price = last_sale_price
if last_sale_date is not None:
position.last_sale_date = last_sale_date
if cost_basis is not None:
position.cost_basis = cost_basis
if position.amount == 0:
del self.positions[asset]
try:
# if this position exists in our user-facing dictionary,
# remove it as well.
del self._positions_store[asset]
except KeyError:
pass
def execute_transaction(self, txn):
self._dirty_stats = True
asset = txn.asset
if asset not in self.positions:
position = Position(asset)
self.positions[asset] = position
else:
position = self.positions[asset]
position.update(txn)
if position.amount == 0:
del self.positions[asset]
try:
# if this position exists in our user-facing dictionary,
# remove it as well.
del self._positions_store[asset]
except KeyError:
pass
def handle_commission(self, asset, cost):
# Adjust the cost basis of the stock if we own it
if asset in self.positions:
self._dirty_stats = True
self.positions[asset].adjust_commission_cost_basis(asset, cost)
def handle_splits(self, splits):
"""Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list
A list of splits. Each split is a tuple of (asset, ratio).
Returns
-------
int: The leftover cash from fractional shares after modifying each
position.
"""
total_leftover_cash = 0
for asset, ratio in splits:
if asset in self.positions:
self._dirty_stats = True
# Make the position object handle the split. It returns the
# leftover cash from a fractional share, if there is any.
position = self.positions[asset]
leftover_cash = position.handle_split(asset, ratio)
total_leftover_cash += leftover_cash
return total_leftover_cash
def earn_dividends(self, cash_dividends, stock_dividends):
"""Given a list of dividends whose ex_dates are all the next trading
day, calculate and store the cash and/or stock payments to be paid on
each dividend's pay date.
Parameters
----------
cash_dividends : iterable of (asset, amount, pay_date) namedtuples
stock_dividends: iterable of (asset, payment_asset, ratio, pay_date)
namedtuples.
"""
for cash_dividend in cash_dividends:
self._dirty_stats = True # only mark dirty if we pay a dividend
# Store the earned dividends so that they can be paid on the
# dividends' pay_dates.
div_owed = self.positions[cash_dividend.asset].earn_dividend(
cash_dividend,
)
try:
self._unpaid_dividends[cash_dividend.pay_date].append(div_owed)
except KeyError:
self._unpaid_dividends[cash_dividend.pay_date] = [div_owed]
for stock_dividend in stock_dividends:
self._dirty_stats = True # only mark dirty if we pay a dividend
div_owed = self.positions[
stock_dividend.asset
].earn_stock_dividend(stock_dividend)
try:
self._unpaid_stock_dividends[stock_dividend.pay_date].append(
div_owed,
)
except KeyError:
self._unpaid_stock_dividends[stock_dividend.pay_date] = [
div_owed,
]
def pay_dividends(self, next_trading_day):
"""
Returns a cash payment based on the dividends that should be paid out
according to the accumulated bookkeeping of earned, unpaid, and stock
dividends.
"""
net_cash_payment = 0.0
try:
payments = self._unpaid_dividends[next_trading_day]
# Mark these dividends as paid by dropping them from our unpaid
del self._unpaid_dividends[next_trading_day]
except KeyError:
payments = []
# representing the fact that we're required to reimburse the owner of
# the stock for any dividends paid while borrowing.
for payment in payments:
net_cash_payment += payment['amount']
# Add stock for any stock dividends paid. Again, the values here may
# be negative in the case of short positions.
try:
stock_payments = self._unpaid_stock_dividends[next_trading_day]
except KeyError:
stock_payments = []
for stock_payment in stock_payments:
payment_asset = stock_payment['payment_asset']
share_count = stock_payment['share_count']
# note we create a Position for stock dividend if we don't
# already own the asset
if payment_asset in self.positions:
position = self.positions[payment_asset]
else:
position = self.positions[payment_asset] = Position(
payment_asset,
)
position.amount += share_count
return net_cash_payment
def maybe_create_close_position_transaction(self, asset, dt, data_portal):
if not self.positions.get(asset):
return None
amount = self.positions.get(asset).amount
price = data_portal.get_spot_value(
asset, 'price', dt, self.data_frequency)
# Get the last traded price if price is no longer available
if isnan(price):
price = self.positions.get(asset).last_sale_price
return Transaction(
asset=asset,
amount=-amount,
dt=dt,
price=price,
order_id=None,
)
def get_positions(self):
positions = self._positions_store
for asset, pos in iteritems(self.positions):
# Adds the new position if we didn't have one before, or overwrite
# one we have currently
positions[asset] = pos.protocol_position
return positions
def get_position_list(self):
return [
pos.to_dict()
for asset, pos in iteritems(self.positions)
if pos.amount != 0
]
def sync_last_sale_prices(self,
dt,
data_portal,
handle_non_market_minutes=False):
self._dirty_stats = True
if handle_non_market_minutes:
previous_minute = data_portal.trading_calendar.previous_minute(dt)
get_price = partial(
data_portal.get_adjusted_value,
field='price',
dt=previous_minute,
perspective_dt=dt,
data_frequency=self.data_frequency,
)
else:
get_price = partial(
data_portal.get_scalar_asset_spot_value,
field='price',
dt=dt,
data_frequency=self.data_frequency,
)
update_position_last_sale_prices(self.positions, get_price, dt)
@property
def stats(self):
"""The current status of the positions.
Returns
-------
stats : PositionStats
The current stats position stats.
Notes
-----
This is cached, repeated access will not recompute the stats until
the stats may have changed.
"""
if self._dirty_stats:
calculate_position_tracker_stats(self.positions, self._stats)
self._dirty_stats = False
return self._stats
if PY2:
def move_to_end(ordered_dict, key, last=False):
if last:
ordered_dict[key] = ordered_dict.pop(key)
else:
# please don't do this in python 2 ;_;
new_first_element = ordered_dict.pop(key)
# the items (without the given key) in the order they were inserted
items = ordered_dict.items()
# reset the ordered_dict to re-insert in the new order
ordered_dict.clear()
ordered_dict[key] = new_first_element
# add the items back in their original order
ordered_dict.update(items)
else:
move_to_end = OrderedDict.move_to_end
PeriodStats = namedtuple(
'PeriodStats',
'net_liquidation gross_leverage net_leverage',
)
not_overridden = sentinel(
'not_overridden',
'Mark that an account field has not been overridden',
)
class Ledger(object):
"""The ledger tracks all orders and transactions as well as the current
state of the portfolio and positions.
Attributes
----------
portfolio : zipline.protocol.Portfolio
The updated portfolio being managed.
account : zipline.protocol.Account
The updated account being managed.
position_tracker : PositionTracker
The current set of positions.
todays_returns : float
The current day's returns. In minute emission mode, this is the partial
day's returns. In daily emission mode, this is
``daily_returns[session]``.
daily_returns_series : pd.Series
The daily returns series. Days that have not yet finished will hold
a value of ``np.nan``.
daily_returns_array : np.ndarray
The daily returns as an ndarray. Days that have not yet finished will
hold a value of ``np.nan``.
"""
def __init__(self, trading_sessions, capital_base, data_frequency):
if len(trading_sessions):
start = trading_sessions[0]
else:
start = None
# Have some fields of the portfolio changed? This should be accessed
# through ``self._dirty_portfolio``
self.__dirty_portfolio = False
self._immutable_portfolio = zp.Portfolio(start, capital_base)
self._portfolio = zp.MutableView(self._immutable_portfolio)
self.daily_returns_series = pd.Series(
np.nan,
index=trading_sessions,
)
# Get a view into the storage of the returns series. Metrics
# can access this directly in minute mode for performance reasons.
self.daily_returns_array = self.daily_returns_series.values
self._previous_total_returns = 0
# this is a component of the cache key for the account
self._position_stats = None
# Have some fields of the account changed?
self._dirty_account = True
self._immutable_account = zp.Account()
self._account = zp.MutableView(self._immutable_account)
# The broker blotter can override some fields on the account. This is
# way to tangled up at the moment but we aren't fixing it today.
self._account_overrides = {}
self.position_tracker = PositionTracker(data_frequency)
self._processed_transactions = {}
self._orders_by_modified = {}
self._orders_by_id = OrderedDict()
# Keyed by asset, the previous last sale price of positions with
# payouts on price differences, e.g. Futures.
#
# This dt is not the previous minute to the minute for which the
# calculation is done, but the last sale price either before the period
# start, or when the price at execution.
self._payout_last_sale_prices = {}
@property
def todays_returns(self):
# compute today's returns in returns space instead of portfolio-value
# space to work even when we have capital changes
return (
(self.portfolio.returns + 1) /
(self._previous_total_returns + 1) -
1
)
@property
def _dirty_portfolio(self):
return self.__dirty_portfolio
@_dirty_portfolio.setter
def _dirty_portfolio(self, value):
if value:
# marking the portfolio as dirty also marks the account as dirty
self.__dirty_portfolio = self._dirty_account = value
else:
self.__dirty_portfolio = value
def start_of_session(self, session_label):
self._processed_transactions.clear()
self._orders_by_modified.clear()
self._orders_by_id.clear()
# Save the previous day's total returns so that ``todays_returns``
# produces returns since yesterday. This does not happen in
# ``end_of_session`` because we want ``todays_returns`` to produce the
# correct value in metric ``end_of_session`` handlers.
self._previous_total_returns = self.portfolio.returns
def end_of_bar(self, session_ix):
# make daily_returns hold the partial returns, this saves many
# metrics from doing a concat and copying all of the previous
# returns
self.daily_returns_array[session_ix] = self.todays_returns
def end_of_session(self, session_ix):
# save the daily returns time-series
self.daily_returns_series[session_ix] = self.todays_returns
def sync_last_sale_prices(self,
dt,
data_portal,
handle_non_market_minutes=False):
self.position_tracker.sync_last_sale_prices(
dt,
data_portal,
handle_non_market_minutes=handle_non_market_minutes,
)
self._dirty_portfolio = True
@staticmethod
def _calculate_payout(multiplier, amount, old_price, price):
return (price - old_price) * multiplier * amount
def _cash_flow(self, amount):
self._dirty_portfolio = True
p = self._portfolio
p.cash_flow += amount
p.cash += amount
def process_transaction(self, transaction):
"""Add a transaction to ledger, updating the current state as needed.
Parameters
----------
transaction : zp.Transaction
The transaction to execute.
"""
asset = transaction.asset
if isinstance(asset, Future):
try:
old_price = self._payout_last_sale_prices[asset]
except KeyError:
self._payout_last_sale_prices[asset] = transaction.price
else:
position = self.position_tracker.positions[asset]
amount = position.amount
price = transaction.price
self._cash_flow(
self._calculate_payout(
asset.price_multiplier,
amount,
old_price,
price,
),
)
if amount + transaction.amount == 0:
del self._payout_last_sale_prices[asset]
else:
self._payout_last_sale_prices[asset] = price
else:
self._cash_flow(-(transaction.price * transaction.amount))
self.position_tracker.execute_transaction(transaction)
# we only ever want the dict form from now on
transaction_dict = transaction.to_dict()
try:
self._processed_transactions[transaction.dt].append(
transaction_dict,
)
except KeyError:
self._processed_transactions[transaction.dt] = [transaction_dict]
def process_splits(self, splits):
"""Processes a list of splits by modifying any positions as needed.
Parameters
----------
splits: list[(Asset, float)]
A list of splits. Each split is a tuple of (asset, ratio).
"""
leftover_cash = self.position_tracker.handle_splits(splits)
if leftover_cash > 0:
self._cash_flow(leftover_cash)
def process_order(self, order):
"""Keep track of an order that was placed.
Parameters
----------
order : zp.Order
The order to record.
"""
try:
dt_orders = self._orders_by_modified[order.dt]
except KeyError:
self._orders_by_modified[order.dt] = OrderedDict([
(order.id, order),
])
self._orders_by_id[order.id] = order
else:
self._orders_by_id[order.id] = dt_orders[order.id] = order
# to preserve the order of the orders by modified date
move_to_end(dt_orders, order.id, last=True)
move_to_end(self._orders_by_id, order.id, last=True)
def process_commission(self, commission):
"""Process the commission.
Parameters
----------
commission : zp.Event
The commission being paid.
"""
asset = commission['asset']
cost = commission['cost']
self.position_tracker.handle_commission(asset, cost)
self._cash_flow(-cost)
def close_position(self, asset, dt, data_portal):
txn = self.position_tracker.maybe_create_close_position_transaction(
asset,
dt,
data_portal,
)
if txn is not None:
self.process_transaction(txn)
def process_dividends(self, next_session, asset_finder, adjustment_reader):
"""Process dividends for the next session.
This will earn us any dividends whose ex-date is the next session as
well as paying out any dividends whose pay-date is the next session
"""
position_tracker = self.position_tracker
# Earn dividends whose ex_date is the next trading day. We need to
# check if we own any of these stocks so we know to pay them out when
# the pay date comes.
held_sids = set(position_tracker.positions)
if held_sids:
cash_dividends = adjustment_reader.get_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
stock_dividends = (
adjustment_reader.get_stock_dividends_with_ex_date(
held_sids,
next_session,
asset_finder
)
)
# Earning a dividend just marks that we need to get paid out on
# the dividend's pay-date. This does not affect our cash yet.
position_tracker.earn_dividends(
cash_dividends,
stock_dividends,
)
# Pay out the dividends whose pay-date is the next session. This does
# affect out cash.
self._cash_flow(
position_tracker.pay_dividends(
next_session,
),
)
def capital_change(self, change_amount):
self.update_portfolio()
portfolio = self._portfolio
# we update the cash and total value so this is not dirty
portfolio.portfolio_value += change_amount
portfolio.cash += change_amount
def transactions(self, dt=None):
"""Retrieve the dict-form of all of the transactions in a given bar or
for the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up transactions for. If not passed,
or None is explicitly passed, all of the transactions will be
returned.
Returns
-------
transactions : list[dict]
The transaction information.
"""
if dt is None:
# flatten the by-day transactions
return [
txn
for by_day in itervalues(self._processed_transactions)
for txn in by_day
]
return self._processed_transactions.get(dt, [])
def orders(self, dt=None):
"""Retrieve the dict-form of all of the orders in a given bar or for
the whole simulation.
Parameters
----------
dt : pd.Timestamp or None, optional
The particular datetime to look up order for. If not passed, or
None is explicitly passed, all of the orders will be returned.
Returns
-------
orders : list[dict]
The order information.
"""
if dt is None:
# orders by id is already flattened
return [o.to_dict() for o in itervalues(self._orders_by_id)]
return [
o.to_dict()
for o in itervalues(self._orders_by_modified.get(dt, {}))
]
@property
def positions(self):
return self.position_tracker.get_position_list()
def _get_payout_total(self, positions):
calculate_payout = self._calculate_payout
payout_last_sale_prices = self._payout_last_sale_prices
total = 0
for asset, old_price in iteritems(payout_last_sale_prices):
position = positions[asset]
payout_last_sale_prices[asset] = price = position.last_sale_price
amount = position.amount
total += calculate_payout(
asset.price_multiplier,
amount,
old_price,
price,
)
return total
def update_portfolio(self):
"""Force a computation of the current portfolio state.
"""
if not self._dirty_portfolio:
return
portfolio = self._portfolio
pt = self.position_tracker
portfolio.positions = pt.get_positions()
position_stats = pt.stats
portfolio.positions_value = position_value = (
position_stats.net_value
)
portfolio.positions_exposure = position_stats.net_exposure
self._cash_flow(self._get_payout_total(pt.positions))
start_value = portfolio.portfolio_value
# update the new starting value
portfolio.portfolio_value = end_value = portfolio.cash + position_value
pnl = end_value - start_value
if start_value != 0:
returns = pnl / start_value
else:
returns = 0.0
portfolio.pnl += pnl
portfolio.returns = (
(1 + portfolio.returns) *
(1 + returns) -
1
)
# the portfolio has been fully synced
self._dirty_portfolio = False
@property
def portfolio(self):
"""Compute the current portfolio.
Notes
-----
This is cached, repeated access will not recompute the portfolio until
the portfolio may have changed.
"""
self.update_portfolio()
return self._immutable_portfolio
def calculate_period_stats(self):
position_stats = self.position_tracker.stats
portfolio_value = self.portfolio.portfolio_value
if portfolio_value == 0:
gross_leverage = net_leverage = np.inf
else:
gross_leverage = position_stats.gross_exposure / portfolio_value
net_leverage = position_stats.net_exposure / portfolio_value
return portfolio_value, gross_leverage, net_leverage
def override_account_fields(self,
settled_cash=not_overridden,
accrued_interest=not_overridden,
buying_power=not_overridden,
equity_with_loan=not_overridden,
total_positions_value=not_overridden,
total_positions_exposure=not_overridden,
regt_equity=not_overridden,
regt_margin=not_overridden,
initial_margin_requirement=not_overridden,
maintenance_margin_requirement=not_overridden,
available_funds=not_overridden,
excess_liquidity=not_overridden,
cushion=not_overridden,
day_trades_remaining=not_overridden,
leverage=not_overridden,
net_leverage=not_overridden,
net_liquidation=not_overridden):
"""Override fields on ``self.account``.
"""
# mark that the portfolio is dirty to override the fields again
self._dirty_account = True
self._account_overrides = kwargs = {
k: v for k, v in locals().items() if v is not not_overridden
}
del kwargs['self']
@property
def account(self):
if self._dirty_account:
portfolio = self.portfolio
account = self._account
# If no attribute is found in the ``_account_overrides`` resort to
# the following default values. If an attribute is found use the
# existing value. For instance, a broker may provide updates to
# these attributes. In this case we do not want to over write the
# broker values with the default values.
account.settled_cash = portfolio.cash
account.accrued_interest = 0.0
account.buying_power = np.inf
account.equity_with_loan = portfolio.portfolio_value
account.total_positions_value = (
portfolio.portfolio_value - portfolio.cash
)
account.total_positions_exposure = (
portfolio.positions_exposure
)
account.regt_equity = portfolio.cash
account.regt_margin = np.inf
account.initial_margin_requirement = 0.0
account.maintenance_margin_requirement = 0.0
account.available_funds = portfolio.cash
account.excess_liquidity = portfolio.cash
account.cushion = (
(portfolio.cash / portfolio.portfolio_value)
if portfolio.portfolio_value else
np.nan
)
account.day_trades_remaining = np.inf
(account.net_liquidation,
account.gross_leverage,
account.net_leverage) = self.calculate_period_stats()
account.leverage = account.gross_leverage
# apply the overrides
for k, v in iteritems(self._account_overrides):
setattr(account, k, v)
# the account has been fully synced
self._dirty_account = False
return self._immutable_account | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/ledger.py | ledger.py |
from abc import ABCMeta, abstractmethod
from six import with_metaclass
from zipline.extensions import extensible
from zipline.finance.cancel_policy import NeverCancel
@extensible
class Blotter(with_metaclass(ABCMeta)):
def __init__(self, cancel_policy=None):
self.cancel_policy = cancel_policy if cancel_policy else NeverCancel()
self.current_dt = None
def set_date(self, dt):
self.current_dt = dt
@abstractmethod
def order(self, asset, amount, style, order_id=None):
"""Place an order.
Parameters
----------
asset : zipline.assets.Asset
The asset that this order is for.
amount : int
The amount of shares to order. If ``amount`` is positive, this is
the number of shares to buy or cover. If ``amount`` is negative,
this is the number of shares to sell or short.
style : zipline.finance.execution.ExecutionStyle
The execution style for the order.
order_id : str, optional
The unique identifier for this order.
Returns
-------
order_id : str or None
The unique identifier for this order, or None if no order was
placed.
Notes
-----
amount > 0 :: Buy/Cover
amount < 0 :: Sell/Short
Market order: order(asset, amount)
Limit order: order(asset, amount, style=LimitOrder(limit_price))
Stop order: order(asset, amount, style=StopOrder(stop_price))
StopLimit order: order(asset, amount, style=StopLimitOrder(limit_price,
stop_price))
"""
raise NotImplementedError('order')
def batch_order(self, order_arg_lists):
"""Place a batch of orders.
Parameters
----------
order_arg_lists : iterable[tuple]
Tuples of args that `order` expects.
Returns
-------
order_ids : list[str or None]
The unique identifier (or None) for each of the orders placed
(or not placed).
Notes
-----
This is required for `Blotter` subclasses to be able to place a batch
of orders, instead of being passed the order requests one at a time.
"""
return [self.order(*order_args) for order_args in order_arg_lists]
@abstractmethod
def cancel(self, order_id, relay_status=True):
"""Cancel a single order
Parameters
----------
order_id : int
The id of the order
relay_status : bool
Whether or not to record the status of the order
"""
raise NotImplementedError('cancel')
@abstractmethod
def cancel_all_orders_for_asset(self, asset, warn=False,
relay_status=True):
"""
Cancel all open orders for a given asset.
"""
raise NotImplementedError('cancel_all_orders_for_asset')
@abstractmethod
def execute_cancel_policy(self, event):
raise NotImplementedError('execute_cancel_policy')
@abstractmethod
def reject(self, order_id, reason=''):
"""
Mark the given order as 'rejected', which is functionally similar to
cancelled. The distinction is that rejections are involuntary (and
usually include a message from a broker indicating why the order was
rejected) while cancels are typically user-driven.
"""
raise NotImplementedError('reject')
@abstractmethod
def hold(self, order_id, reason=''):
"""
Mark the order with order_id as 'held'. Held is functionally similar
to 'open'. When a fill (full or partial) arrives, the status
will automatically change back to open/filled as necessary.
"""
raise NotImplementedError('hold')
@abstractmethod
def process_splits(self, splits):
"""
Processes a list of splits by modifying any open orders as needed.
Parameters
----------
splits: list
A list of splits. Each split is a tuple of (asset, ratio).
Returns
-------
None
"""
raise NotImplementedError('process_splits')
@abstractmethod
def get_transactions(self, bar_data):
"""
Creates a list of transactions based on the current open orders,
slippage model, and commission model.
Parameters
----------
bar_data: zipline._protocol.BarData
Notes
-----
This method book-keeps the blotter's open_orders dictionary, so that
it is accurate by the time we're done processing open orders.
Returns
-------
transactions_list: List
transactions_list: list of transactions resulting from the current
open orders. If there were no open orders, an empty list is
returned.
commissions_list: List
commissions_list: list of commissions resulting from filling the
open orders. A commission is an object with "asset" and "cost"
parameters.
closed_orders: List
closed_orders: list of all the orders that have filled.
"""
raise NotImplementedError('get_transactions')
@abstractmethod
def prune_orders(self, closed_orders):
"""
Removes all given orders from the blotter's open_orders list.
Parameters
----------
closed_orders: iterable of orders that are closed.
Returns
-------
None
"""
raise NotImplementedError('prune_orders') | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/blotter/blotter.py | blotter.py |
from logbook import Logger
from collections import defaultdict
from copy import copy
from six import iteritems
from zipline.assets import Equity, Future, Asset
from .blotter import Blotter
from zipline.extensions import register
from zipline.finance.order import Order
from zipline.finance.slippage import (
DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT,
VolatilityVolumeShare,
FixedBasisPointsSlippage,
)
from zipline.finance.commission import (
DEFAULT_PER_CONTRACT_COST,
FUTURE_EXCHANGE_FEES_BY_SYMBOL,
PerContract,
PerShare,
)
from zipline.utils.input_validation import expect_types
log = Logger('Blotter')
warning_logger = Logger('AlgoWarning')
@register(Blotter, 'default')
class SimulationBlotter(Blotter):
def __init__(self,
equity_slippage=None,
future_slippage=None,
equity_commission=None,
future_commission=None,
cancel_policy=None):
super(SimulationBlotter, self).__init__(cancel_policy=cancel_policy)
# these orders are aggregated by asset
self.open_orders = defaultdict(list)
# keep a dict of orders by their own id
self.orders = {}
# holding orders that have come in since the last event.
self.new_orders = []
self.max_shares = int(1e+11)
self.slippage_models = {
Equity: equity_slippage or FixedBasisPointsSlippage(),
Future: future_slippage or VolatilityVolumeShare(
volume_limit=DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT,
),
}
self.commission_models = {
Equity: equity_commission or PerShare(),
Future: future_commission or PerContract(
cost=DEFAULT_PER_CONTRACT_COST,
exchange_fee=FUTURE_EXCHANGE_FEES_BY_SYMBOL,
),
}
def __repr__(self):
return """
{class_name}(
slippage_models={slippage_models},
commission_models={commission_models},
open_orders={open_orders},
orders={orders},
new_orders={new_orders},
current_dt={current_dt})
""".strip().format(class_name=self.__class__.__name__,
slippage_models=self.slippage_models,
commission_models=self.commission_models,
open_orders=self.open_orders,
orders=self.orders,
new_orders=self.new_orders,
current_dt=self.current_dt)
@expect_types(asset=Asset)
def order(self, asset, amount, style, order_id=None):
"""Place an order.
Parameters
----------
asset : zipline.assets.Asset
The asset that this order is for.
amount : int
The amount of shares to order. If ``amount`` is positive, this is
the number of shares to buy or cover. If ``amount`` is negative,
this is the number of shares to sell or short.
style : zipline.finance.execution.ExecutionStyle
The execution style for the order.
order_id : str, optional
The unique identifier for this order.
Returns
-------
order_id : str or None
The unique identifier for this order, or None if no order was
placed.
Notes
-----
amount > 0 :: Buy/Cover
amount < 0 :: Sell/Short
Market order: order(asset, amount)
Limit order: order(asset, amount, style=LimitOrder(limit_price))
Stop order: order(asset, amount, style=StopOrder(stop_price))
StopLimit order: order(asset, amount, style=StopLimitOrder(limit_price,
stop_price))
"""
# something could be done with amount to further divide
# between buy by share count OR buy shares up to a dollar amount
# numeric == share count AND "$dollar.cents" == cost amount
if amount == 0:
# Don't bother placing orders for 0 shares.
return None
elif amount > self.max_shares:
# Arbitrary limit of 100 billion (US) shares will never be
# exceeded except by a buggy algorithm.
raise OverflowError("Can't order more than %d shares" %
self.max_shares)
is_buy = (amount > 0)
order = Order(
dt=self.current_dt,
asset=asset,
amount=amount,
stop=style.get_stop_price(is_buy),
limit=style.get_limit_price(is_buy),
id=order_id
)
self.open_orders[order.asset].append(order)
self.orders[order.id] = order
self.new_orders.append(order)
return order.id
def cancel(self, order_id, relay_status=True):
if order_id not in self.orders:
return
cur_order = self.orders[order_id]
if cur_order.open:
order_list = self.open_orders[cur_order.asset]
if cur_order in order_list:
order_list.remove(cur_order)
if cur_order in self.new_orders:
self.new_orders.remove(cur_order)
cur_order.cancel()
cur_order.dt = self.current_dt
if relay_status:
# we want this order's new status to be relayed out
# along with newly placed orders.
self.new_orders.append(cur_order)
def cancel_all_orders_for_asset(self, asset, warn=False,
relay_status=True):
"""
Cancel all open orders for a given asset.
"""
# (sadly) open_orders is a defaultdict, so this will always succeed.
orders = self.open_orders[asset]
# We're making a copy here because `cancel` mutates the list of open
# orders in place. The right thing to do here would be to make
# self.open_orders no longer a defaultdict. If we do that, then we
# should just remove the orders once here and be done with the matter.
for order in orders[:]:
self.cancel(order.id, relay_status)
if warn:
# Message appropriately depending on whether there's
# been a partial fill or not.
if order.filled > 0:
warning_logger.warn(
'Your order for {order_amt} shares of '
'{order_sym} has been partially filled. '
'{order_filled} shares were successfully '
'purchased. {order_failed} shares were not '
'filled by the end of day and '
'were canceled.'.format(
order_amt=order.amount,
order_sym=order.asset.symbol,
order_filled=order.filled,
order_failed=order.amount - order.filled,
)
)
elif order.filled < 0:
warning_logger.warn(
'Your order for {order_amt} shares of '
'{order_sym} has been partially filled. '
'{order_filled} shares were successfully '
'sold. {order_failed} shares were not '
'filled by the end of day and '
'were canceled.'.format(
order_amt=order.amount,
order_sym=order.asset.symbol,
order_filled=-1 * order.filled,
order_failed=-1 * (order.amount - order.filled),
)
)
else:
warning_logger.warn(
'Your order for {order_amt} shares of '
'{order_sym} failed to fill by the end of day '
'and was canceled.'.format(
order_amt=order.amount,
order_sym=order.asset.symbol,
)
)
assert not orders
del self.open_orders[asset]
def execute_cancel_policy(self, event):
if self.cancel_policy.should_cancel(event):
warn = self.cancel_policy.warn_on_cancel
for asset in copy(self.open_orders):
self.cancel_all_orders_for_asset(asset, warn,
relay_status=False)
def reject(self, order_id, reason=''):
"""
Mark the given order as 'rejected', which is functionally similar to
cancelled. The distinction is that rejections are involuntary (and
usually include a message from a broker indicating why the order was
rejected) while cancels are typically user-driven.
"""
if order_id not in self.orders:
return
cur_order = self.orders[order_id]
order_list = self.open_orders[cur_order.asset]
if cur_order in order_list:
order_list.remove(cur_order)
if cur_order in self.new_orders:
self.new_orders.remove(cur_order)
cur_order.reject(reason=reason)
cur_order.dt = self.current_dt
# we want this order's new status to be relayed out
# along with newly placed orders.
self.new_orders.append(cur_order)
def hold(self, order_id, reason=''):
"""
Mark the order with order_id as 'held'. Held is functionally similar
to 'open'. When a fill (full or partial) arrives, the status
will automatically change back to open/filled as necessary.
"""
if order_id not in self.orders:
return
cur_order = self.orders[order_id]
if cur_order.open:
if cur_order in self.new_orders:
self.new_orders.remove(cur_order)
cur_order.hold(reason=reason)
cur_order.dt = self.current_dt
# we want this order's new status to be relayed out
# along with newly placed orders.
self.new_orders.append(cur_order)
def process_splits(self, splits):
"""
Processes a list of splits by modifying any open orders as needed.
Parameters
----------
splits: list
A list of splits. Each split is a tuple of (asset, ratio).
Returns
-------
None
"""
for asset, ratio in splits:
if asset not in self.open_orders:
continue
orders_to_modify = self.open_orders[asset]
for order in orders_to_modify:
order.handle_split(ratio)
def get_transactions(self, bar_data):
"""
Creates a list of transactions based on the current open orders,
slippage model, and commission model.
Parameters
----------
bar_data: zipline._protocol.BarData
Notes
-----
This method book-keeps the blotter's open_orders dictionary, so that
it is accurate by the time we're done processing open orders.
Returns
-------
transactions_list: List
transactions_list: list of transactions resulting from the current
open orders. If there were no open orders, an empty list is
returned.
commissions_list: List
commissions_list: list of commissions resulting from filling the
open orders. A commission is an object with "asset" and "cost"
parameters.
closed_orders: List
closed_orders: list of all the orders that have filled.
"""
closed_orders = []
transactions = []
commissions = []
if self.open_orders:
for asset, asset_orders in iteritems(self.open_orders):
slippage = self.slippage_models[type(asset)]
for order, txn in \
slippage.simulate(bar_data, asset, asset_orders):
commission = self.commission_models[type(asset)]
additional_commission = commission.calculate(order, txn)
if additional_commission > 0:
commissions.append({
"asset": order.asset,
"order": order,
"cost": additional_commission
})
order.filled += txn.amount
order.commission += additional_commission
order.dt = txn.dt
transactions.append(txn)
if not order.open:
closed_orders.append(order)
return transactions, commissions, closed_orders
def prune_orders(self, closed_orders):
"""
Removes all given orders from the blotter's open_orders list.
Parameters
----------
closed_orders: iterable of orders that are closed.
Returns
-------
None
"""
# remove all closed orders from our open_orders dict
for order in closed_orders:
asset = order.asset
asset_orders = self.open_orders[asset]
try:
asset_orders.remove(order)
except ValueError:
continue
# now clear out the assets from our open_orders dict that have
# zero open orders
for asset in list(self.open_orders.keys()):
if len(self.open_orders[asset]) == 0:
del self.open_orders[asset] | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/blotter/simulation_blotter.py | simulation_blotter.py |
from logbook import Logger
from collections import defaultdict
from copy import copy
from six import itervalues, iteritems
from zipline.assets import Equity, Future, Asset
from zipline.finance.blotter.blotter import Blotter
from zipline.extensions import register
from zipline.finance.order import Order
from zipline.finance.slippage import (
DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT,
VolatilityVolumeShare,
FixedBasisPointsSlippage,
)
from zipline.finance.commission import (
DEFAULT_PER_CONTRACT_COST,
FUTURE_EXCHANGE_FEES_BY_SYMBOL,
PerContract,
PerShare,
)
from zipline.utils.input_validation import expect_types
import pandas as pd
log = Logger('Blotter Live')
warning_logger = Logger('AlgoWarning')
class BlotterLive(Blotter):
def __init__(self, data_frequency, broker):
self.broker = broker
self._processed_closed_orders = []
self._processed_transactions = []
self.data_frequency = data_frequency
self.new_orders = []
self.max_shares = int(1e+11)
self.slippage_models = {
Equity: FixedBasisPointsSlippage(),
Future: VolatilityVolumeShare(
volume_limit=DEFAULT_FUTURE_VOLUME_SLIPPAGE_BAR_LIMIT,
),
}
self.commission_models = {
Equity: PerShare(),
Future: PerContract(
cost=DEFAULT_PER_CONTRACT_COST,
exchange_fee=FUTURE_EXCHANGE_FEES_BY_SYMBOL,
),
}
log.info('Initialized blotter_live')
def __repr__(self):
return """
{class_name}(
open_orders={open_orders},
orders={orders},
new_orders={new_orders},
""".strip().format(class_name=self.__class__.__name__,
open_orders=self.open_orders,
orders=self.orders,
new_orders=self.new_orders)
@property
def orders(self):
# IB returns orders from previous days too.
# Need to filter for today to be in sync with zipline's behavior
# TODO: This logic needs to be extended once GTC orders are supported
today = pd.to_datetime('now', utc=True).date()
return {order_id: order
for order_id, order in iteritems(self.broker.orders)
if order.dt.date() == today}
@property
def open_orders(self):
assets = set([order.asset for order in itervalues(self.orders)
if order.open])
return {
asset: [order for order in itervalues(self.orders)
if order.asset == asset and order.open]
for asset in assets
}
@expect_types(asset=Asset)
def order(self, asset, amount, style, order_id=None):
assert order_id is None
if amount == 0:
# it's a zipline fuck up.. we shouldn't get orders with amount 0. ignoring this order
return ''
order = self.broker.order(asset, amount, style)
self.new_orders.append(order)
return order.id
def cancel(self, order_id, relay_status=True):
return self.broker.cancel_order(order_id)
def execute_cancel_policy(self, event):
# Cancellation is handled at the broker
pass
def cancel_all_orders_for_asset(self, asset, warn=False, relay_status=True):
"""
Cancel all open orders for a given asset.
"""
# (sadly) open_orders is a defaultdict, so this will always succeed.
orders = self.open_orders[asset]
# We're making a copy here because `cancel` mutates the list of open
# orders in place. The right thing to do here would be to make
# self.open_orders no longer a defaultdict. If we do that, then we
# should just remove the orders once here and be done with the matter.
for order in orders[:]:
self.cancel(order.id, relay_status)
if warn:
# Message appropriately depending on whether there's
# been a partial fill or not.
if order.filled > 0:
warning_logger.warn(
'Your order for {order_amt} shares of '
'{order_sym} has been partially filled. '
'{order_filled} shares were successfully '
'purchased. {order_failed} shares were not '
'filled by the end of day and '
'were canceled.'.format(
order_amt=order.amount,
order_sym=order.asset.symbol,
order_filled=order.filled,
order_failed=order.amount - order.filled,
)
)
elif order.filled < 0:
warning_logger.warn(
'Your order for {order_amt} shares of '
'{order_sym} has been partially filled. '
'{order_filled} shares were successfully '
'sold. {order_failed} shares were not '
'filled by the end of day and '
'were canceled.'.format(
order_amt=order.amount,
order_sym=order.asset.symbol,
order_filled=-1 * order.filled,
order_failed=-1 * (order.amount - order.filled),
)
)
else:
warning_logger.warn(
'Your order for {order_amt} shares of '
'{order_sym} failed to fill by the end of day '
'and was canceled.'.format(
order_amt=order.amount,
order_sym=order.asset.symbol,
)
)
assert not orders
del self.open_orders[asset]
def reject(self, order_id, reason=''):
log.warning("Unexpected reject request for {}: '{}'".format(
order_id, reason))
def hold(self, order_id, reason=''):
log.warning("Unexpected hold request for {}: '{}'".format(
order_id, reason))
def get_transactions(self, bar_data):
# All returned values from this function are delta between
# the previous and actual call.
def _list_delta(lst_a, lst_b):
return [elem for elem in lst_a if elem not in set(lst_b)]
today = pd.to_datetime('now', utc=True).date()
all_transactions = [tx
for tx in itervalues(self.broker.transactions)
if tx.dt.date() == today]
new_transactions = _list_delta(all_transactions,
self._processed_transactions)
self._processed_transactions = all_transactions
new_commissions = [{'asset': tx.asset,
'cost': self.broker.orders[tx.order_id].commission,
'order': self.orders[tx.order_id]}
for tx in new_transactions]
all_closed_orders = [order
for order in itervalues(self.orders)
if not order.open]
new_closed_orders = _list_delta(all_closed_orders,
self._processed_closed_orders)
self._processed_closed_orders = all_closed_orders
return new_transactions, new_commissions, new_closed_orders
def prune_orders(self, closed_orders):
# Orders are handled at the broker
pass
def process_splits(self, splits):
# Splits are handled at the broker
pass | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/blotter/blotter_live.py | blotter_live.py |
from __future__ import division
import logbook
from ..ledger import Ledger
from zipline.utils.exploding_object import NamedExplodingObject
log = logbook.Logger(__name__)
class MetricsTracker(object):
"""The algorithm's interface to the registered risk and performance
metrics.
Parameters
----------
trading_calendar : TrandingCalendar
The trading calendar used in the simulation.
first_session : pd.Timestamp
The label of the first trading session in the simulation.
last_session : pd.Timestamp
The label of the last trading session in the simulation.
capital_base : float
The starting capital for the simulation.
emission_rate : {'daily', 'minute'}
How frequently should a performance packet be generated?
data_frequency : {'daily', 'minute'}
The data frequency of the data portal.
asset_finder : AssetFinder
The asset finder used in the simulation.
metrics : list[Metric]
The metrics to track.
"""
_hooks = (
'start_of_simulation',
'end_of_simulation',
'start_of_session',
'end_of_session',
'end_of_bar',
)
@staticmethod
def _execution_open_and_close(calendar, session):
open_, close = calendar.open_and_close_for_session(session)
execution_open = calendar.execution_time_from_open(open_)
execution_close = calendar.execution_time_from_close(close)
return execution_open, execution_close
def __init__(self,
trading_calendar,
first_session,
last_session,
capital_base,
emission_rate,
data_frequency,
asset_finder,
metrics):
self.emission_rate = emission_rate
self._trading_calendar = trading_calendar
self._first_session = first_session
self._last_session = last_session
self._capital_base = capital_base
self._asset_finder = asset_finder
self._current_session = first_session
self._market_open, self._market_close = self._execution_open_and_close(
trading_calendar,
first_session,
)
self._session_count = 0
self._sessions = sessions = trading_calendar.sessions_in_range(
first_session,
last_session,
)
self._total_session_count = len(sessions)
self._ledger = Ledger(sessions, capital_base, data_frequency)
self._benchmark_source = NamedExplodingObject(
'self._benchmark_source',
'_benchmark_source is not set until ``handle_start_of_simulation``'
' is called',
)
if emission_rate == 'minute':
def progress(self):
return 1.0 # a fake value
else:
def progress(self):
return self._session_count / self._total_session_count
# don't compare these strings over and over again!
self._progress = progress
# bind all of the hooks from the passed metric objects.
for hook in self._hooks:
registered = []
for metric in metrics:
try:
registered.append(getattr(metric, hook))
except AttributeError:
pass
def closing_over_loop_variables_is_hard(registered=registered):
def hook_implementation(*args, **kwargs):
for impl in registered:
impl(*args, **kwargs)
return hook_implementation
hook_implementation = closing_over_loop_variables_is_hard()
hook_implementation.__name__ = hook
setattr(self, hook, hook_implementation)
def handle_start_of_simulation(self, benchmark_source):
self._benchmark_source = benchmark_source
self.start_of_simulation(
self._ledger,
self.emission_rate,
self._trading_calendar,
self._sessions,
benchmark_source,
)
@property
def portfolio(self):
return self._ledger.portfolio
@property
def account(self):
return self._ledger.account
@property
def positions(self):
return self._ledger.position_tracker.positions
def update_position(self,
asset,
amount=None,
last_sale_price=None,
last_sale_date=None,
cost_basis=None):
self._ledger.position_tracker.update_position(
asset,
amount,
last_sale_price,
last_sale_date,
cost_basis,
)
def override_account_fields(self, **kwargs):
self._ledger.override_account_fields(**kwargs)
def process_transaction(self, transaction):
self._ledger.process_transaction(transaction)
def handle_splits(self, splits):
self._ledger.process_splits(splits)
def process_order(self, event):
self._ledger.process_order(event)
def process_commission(self, commission):
self._ledger.process_commission(commission)
def process_close_position(self, asset, dt, data_portal):
self._ledger.close_position(asset, dt, data_portal)
def capital_change(self, amount):
self._ledger.capital_change(amount)
def sync_last_sale_prices(self,
dt,
data_portal,
handle_non_market_minutes=False):
self._ledger.sync_last_sale_prices(
dt,
data_portal,
handle_non_market_minutes=handle_non_market_minutes,
)
def handle_minute_close(self, dt, data_portal):
"""
Handles the close of the given minute in minute emission.
Parameters
----------
dt : Timestamp
The minute that is ending
Returns
-------
A minute perf packet.
"""
self.sync_last_sale_prices(dt, data_portal)
packet = {
'period_start': self._first_session,
'period_end': self._last_session,
'capital_base': self._capital_base,
'minute_perf': {
'period_open': self._market_open,
'period_close': dt,
},
'cumulative_perf': {
'period_open': self._first_session,
'period_close': self._last_session,
},
'progress': self._progress(self),
'cumulative_risk_metrics': {},
}
ledger = self._ledger
ledger.end_of_bar(self._session_count)
self.end_of_bar(
packet,
ledger,
dt,
self._session_count,
data_portal,
)
return packet
def handle_market_open(self, session_label, data_portal):
"""Handles the start of each session.
Parameters
----------
session_label : Timestamp
The label of the session that is about to begin.
data_portal : DataPortal
The current data portal.
"""
ledger = self._ledger
ledger.start_of_session(session_label)
adjustment_reader = data_portal.adjustment_reader
if adjustment_reader is not None:
# this is None when running with a dataframe source
ledger.process_dividends(
session_label,
self._asset_finder,
adjustment_reader,
)
self._current_session = session_label
cal = self._trading_calendar
self._market_open, self._market_close = self._execution_open_and_close(
cal,
session_label,
)
self.start_of_session(ledger, session_label, data_portal)
def handle_market_close(self, dt, data_portal):
"""Handles the close of the given day.
Parameters
----------
dt : Timestamp
The most recently completed simulation datetime.
data_portal : DataPortal
The current data portal.
Returns
-------
A daily perf packet.
"""
completed_session = self._current_session
if self.emission_rate == 'daily':
# this method is called for both minutely and daily emissions, but
# this chunk of code here only applies for daily emissions. (since
# it's done every minute, elsewhere, for minutely emission).
self.sync_last_sale_prices(dt, data_portal)
session_ix = self._session_count
# increment the day counter before we move markers forward.
self._session_count += 1
packet = {
'period_start': self._first_session,
'period_end': self._last_session,
'capital_base': self._capital_base,
'daily_perf': {
'period_open': self._market_open,
'period_close': dt,
},
'cumulative_perf': {
'period_open': self._first_session,
'period_close': self._last_session,
},
'progress': self._progress(self),
'cumulative_risk_metrics': {},
}
ledger = self._ledger
ledger.end_of_session(session_ix)
self.end_of_session(
packet,
ledger,
completed_session,
session_ix,
data_portal,
)
return packet
def handle_simulation_end(self, data_portal):
"""
When the simulation is complete, run the full period risk report
and send it out on the results socket.
"""
log.info(
'Simulated {} trading days\n'
'first open: {}\n'
'last close: {}',
self._session_count,
self._trading_calendar.session_open(self._first_session),
self._trading_calendar.session_close(self._last_session),
)
packet = {}
self.end_of_simulation(
packet,
self._ledger,
self._trading_calendar,
self._sessions,
data_portal,
self._benchmark_source,
)
return packet | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/metrics/tracker.py | tracker.py |
from functools import partial
from zipline.utils.compat import mappingproxy
def _make_metrics_set_core():
"""Create a family of metrics sets functions that read from the same
metrics set mapping.
Returns
-------
metrics_sets : mappingproxy
The mapping of metrics sets to load functions.
register : callable
The function which registers new metrics sets in the ``metrics_sets``
mapping.
unregister : callable
The function which deregisters metrics sets from the ``metrics_sets``
mapping.
load : callable
The function which loads the ingested metrics sets back into memory.
"""
_metrics_sets = {}
# Expose _metrics_sets through a proxy so that users cannot mutate this
# accidentally. Users may go through `register` to update this which will
# warn when trampling another metrics set.
metrics_sets = mappingproxy(_metrics_sets)
def register(name, function=None):
"""Register a new metrics set.
Parameters
----------
name : str
The name of the metrics set
function : callable
The callable which produces the metrics set.
Notes
-----
This may be used as a decorator if only ``name`` is passed.
See Also
--------
zipline.finance.metrics.get_metrics_set
zipline.finance.metrics.unregister_metrics_set
"""
if function is None:
# allow as decorator with just name.
return partial(register, name)
if name in _metrics_sets:
raise ValueError('metrics set %r is already registered' % name)
_metrics_sets[name] = function
return function
def unregister(name):
"""Unregister an existing metrics set.
Parameters
----------
name : str
The name of the metrics set
See Also
--------
zipline.finance.metrics.register_metrics_set
"""
try:
del _metrics_sets[name]
except KeyError:
raise ValueError(
'metrics set %r was not already registered' % name,
)
def load(name):
"""Return an instance of the metrics set registered with the given name.
Returns
-------
metrics : set[Metric]
A new instance of the metrics set.
Raises
------
ValueError
Raised when no metrics set is registered to ``name``
"""
try:
function = _metrics_sets[name]
except KeyError:
raise ValueError(
'no metrics set registered as %r, options are: %r' % (
name,
sorted(_metrics_sets),
),
)
return function()
return metrics_sets, register, unregister, load
metrics_sets, register, unregister, load = _make_metrics_set_core() | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/metrics/core.py | core.py |
import empyrical
from zipline.utils.deprecate import deprecated
from .core import (
metrics_sets,
register,
unregister,
load,
)
from .metric import (
AlphaBeta,
BenchmarkReturnsAndVolatility,
CashFlow,
DailyLedgerField,
MaxLeverage,
NumTradingDays,
Orders,
PeriodLabel,
PNL,
Returns,
ReturnsStatistic,
SimpleLedgerField,
StartOfPeriodLedgerField,
Transactions,
_ConstantCumulativeRiskMetric,
_ClassicRiskMetrics,
)
from .tracker import MetricsTracker
__all__ = ['MetricsTracker', 'unregister', 'metrics_sets', 'load']
register('none', set)
@register('default')
def default_metrics():
return {
Returns(),
ReturnsStatistic(empyrical.annual_volatility, 'algo_volatility'),
BenchmarkReturnsAndVolatility(),
PNL(),
CashFlow(),
Orders(),
Transactions(),
SimpleLedgerField('positions'),
StartOfPeriodLedgerField(
'portfolio.positions_exposure',
'starting_exposure',
),
DailyLedgerField(
'portfolio.positions_exposure',
'ending_exposure',
),
StartOfPeriodLedgerField(
'portfolio.positions_value',
'starting_value'
),
DailyLedgerField('portfolio.positions_value', 'ending_value'),
StartOfPeriodLedgerField('portfolio.cash', 'starting_cash'),
DailyLedgerField('portfolio.cash', 'ending_cash'),
DailyLedgerField('portfolio.portfolio_value'),
DailyLedgerField('position_tracker.stats.longs_count'),
DailyLedgerField('position_tracker.stats.shorts_count'),
DailyLedgerField('position_tracker.stats.long_value'),
DailyLedgerField('position_tracker.stats.short_value'),
DailyLedgerField('position_tracker.stats.long_exposure'),
DailyLedgerField('position_tracker.stats.short_exposure'),
DailyLedgerField('account.gross_leverage'),
DailyLedgerField('account.net_leverage'),
AlphaBeta(),
ReturnsStatistic(empyrical.sharpe_ratio, 'sharpe'),
ReturnsStatistic(empyrical.sortino_ratio, 'sortino'),
ReturnsStatistic(empyrical.max_drawdown),
MaxLeverage(),
# Please kill these!
_ConstantCumulativeRiskMetric('excess_return', 0.0),
_ConstantCumulativeRiskMetric('treasury_period_return', 0.0),
NumTradingDays(),
PeriodLabel(),
}
@register('classic')
@deprecated(
'The original risk packet has been deprecated and will be removed in a '
'future release. Please use "default" metrics instead.'
)
def classic_metrics():
metrics = default_metrics()
metrics.add(_ClassicRiskMetrics())
return metrics | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/metrics/__init__.py | __init__.py |
import datetime
from functools import partial
import operator as op
from dateutil.relativedelta import relativedelta
import empyrical as ep
import numpy as np
import pandas as pd
from six import iteritems
from zipline.utils.exploding_object import NamedExplodingObject
from zipline.finance._finance_ext import minute_annual_volatility
class SimpleLedgerField(object):
"""Emit the current value of a ledger field every bar or every session.
Parameters
----------
ledger_field : str
The ledger field to read.
packet_field : str, optional
The name of the field to populate in the packet. If not provided,
``ledger_field`` will be used.
"""
def __init__(self, ledger_field, packet_field=None):
self._get_ledger_field = op.attrgetter(ledger_field)
if packet_field is None:
self._packet_field = ledger_field.rsplit('.', 1)[-1]
else:
self._packet_field = packet_field
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['minute_perf'][self._packet_field] = self._get_ledger_field(
ledger,
)
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
packet['daily_perf'][self._packet_field] = self._get_ledger_field(
ledger,
)
class DailyLedgerField(object):
"""Like :class:`~zipline.finance.metrics.metric.SimpleLedgerField` but
also puts the current value in the ``cumulative_perf`` section.
Parameters
----------
ledger_field : str
The ledger field to read.
packet_field : str, optional
The name of the field to populate in the packet. If not provided,
``ledger_field`` will be used.
"""
def __init__(self, ledger_field, packet_field=None):
self._get_ledger_field = op.attrgetter(ledger_field)
if packet_field is None:
self._packet_field = ledger_field.rsplit('.', 1)[-1]
else:
self._packet_field = packet_field
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
field = self._packet_field
packet['cumulative_perf'][field] = packet['minute_perf'][field] = (
self._get_ledger_field(ledger)
)
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
field = self._packet_field
packet['cumulative_perf'][field] = packet['daily_perf'][field] = (
self._get_ledger_field(ledger)
)
class StartOfPeriodLedgerField(object):
"""Keep track of the value of a ledger field at the start of the period.
Parameters
----------
ledger_field : str
The ledger field to read.
packet_field : str, optional
The name of the field to populate in the packet. If not provided,
``ledger_field`` will be used.
"""
def __init__(self, ledger_field, packet_field=None):
self._get_ledger_field = op.attrgetter(ledger_field)
if packet_field is None:
self._packet_field = ledger_field.rsplit('.', 1)[-1]
else:
self._packet_field = packet_field
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._start_of_simulation = self._get_ledger_field(ledger)
def start_of_session(self, ledger, session, data_portal):
self._previous_day = self._get_ledger_field(ledger)
def _end_of_period(self, sub_field, packet, ledger):
packet_field = self._packet_field
packet['cumulative_perf'][packet_field] = self._start_of_simulation
packet[sub_field][packet_field] = self._previous_day
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
self._end_of_period('minute_perf', packet, ledger)
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
self._end_of_period('daily_perf', packet, ledger)
class Returns(object):
"""Tracks the daily and cumulative returns of the algorithm.
"""
def _end_of_period(field,
packet,
ledger,
dt,
session_ix,
data_portal):
packet[field]['returns'] = ledger.todays_returns
packet['cumulative_perf']['returns'] = ledger.portfolio.returns
packet['cumulative_risk_metrics']['algorithm_period_return'] = (
ledger.portfolio.returns
)
end_of_bar = partial(_end_of_period, 'minute_perf')
end_of_session = partial(_end_of_period, 'daily_perf')
class BenchmarkReturnsAndVolatility(object):
"""Tracks daily and cumulative returns for the benchmark as well as the
volatility of the benchmark returns.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
daily_returns_series = benchmark_source.daily_returns(
sessions[0],
sessions[-1],
)
self._daily_returns = daily_returns_array = daily_returns_series.values
self._daily_cumulative_returns = (
np.cumprod(1 + daily_returns_array) - 1
)
self._daily_annual_volatility = (
daily_returns_series.expanding(2).std(ddof=1) * np.sqrt(252)
).values
if emission_rate == 'daily':
self._minute_cumulative_returns = NamedExplodingObject(
'self._minute_cumulative_returns',
'does not exist in daily emission rate',
)
self._minute_annual_volatility = NamedExplodingObject(
'self._minute_annual_volatility',
'does not exist in daily emission rate',
)
else:
open_ = trading_calendar.session_open(sessions[0])
close = trading_calendar.session_close(sessions[-1])
returns = benchmark_source.get_range(open_, close)
self._minute_cumulative_returns = (
(1 + returns).cumprod() - 1
)
self._minute_annual_volatility = pd.Series(
minute_annual_volatility(
returns.index.normalize().view('int64'),
returns.values,
daily_returns_array,
),
index=returns.index,
)
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
r = self._minute_cumulative_returns[dt]
if np.isnan(r):
r = None
packet['cumulative_risk_metrics']['benchmark_period_return'] = r
v = self._minute_annual_volatility[dt]
if np.isnan(v):
v = None
packet['cumulative_risk_metrics']['benchmark_volatility'] = v
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
r = self._daily_cumulative_returns[session_ix]
if np.isnan(r):
r = None
packet['cumulative_risk_metrics']['benchmark_period_return'] = r
v = self._daily_annual_volatility[session_ix]
if np.isnan(v):
v = None
packet['cumulative_risk_metrics']['benchmark_volatility'] = v
class PNL(object):
"""Tracks daily and cumulative PNL.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._previous_pnl = 0.0
def start_of_session(self, ledger, session, data_portal):
self._previous_pnl = ledger.portfolio.pnl
def _end_of_period(self, field, packet, ledger):
pnl = ledger.portfolio.pnl
packet[field]['pnl'] = pnl - self._previous_pnl
packet['cumulative_perf']['pnl'] = ledger.portfolio.pnl
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
self._end_of_period('minute_perf', packet, ledger)
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
self._end_of_period('daily_perf', packet, ledger)
class CashFlow(object):
"""Tracks daily and cumulative cash flow.
Notes
-----
For historical reasons, this field is named 'capital_used' in the packets.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._previous_cash_flow = 0.0
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
cash_flow = ledger.portfolio.cash_flow
packet['minute_perf']['capital_used'] = (
cash_flow - self._previous_cash_flow
)
packet['cumulative_perf']['capital_used'] = cash_flow
def end_of_session(self,
packet,
ledger,
session,
session_ix,
data_portal):
cash_flow = ledger.portfolio.cash_flow
packet['daily_perf']['capital_used'] = (
cash_flow - self._previous_cash_flow
)
packet['cumulative_perf']['capital_used'] = cash_flow
self._previous_cash_flow = cash_flow
class Orders(object):
"""Tracks daily orders.
"""
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['minute_perf']['orders'] = ledger.orders(dt)
def end_of_session(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['daily_perf']['orders'] = ledger.orders()
class Transactions(object):
"""Tracks daily transactions.
"""
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['minute_perf']['transactions'] = ledger.transactions(dt)
def end_of_session(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['daily_perf']['transactions'] = ledger.transactions()
class Positions(object):
"""Tracks daily positions.
"""
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['minute_perf']['positions'] = ledger.positions(dt)
def end_of_session(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['daily_perf']['positions'] = ledger.positions()
class ReturnsStatistic(object):
"""A metric that reports an end of simulation scalar or time series
computed from the algorithm returns.
Parameters
----------
function : callable
The function to call on the daily returns.
field_name : str, optional
The name of the field. If not provided, it will be
``function.__name__``.
"""
def __init__(self, function, field_name=None):
if field_name is None:
field_name = function.__name__
self._function = function
self._field_name = field_name
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
res = self._function(ledger.daily_returns_array[:session_ix + 1])
if not np.isfinite(res):
res = None
packet['cumulative_risk_metrics'][self._field_name] = res
end_of_session = end_of_bar
class AlphaBeta(object):
"""End of simulation alpha and beta to the benchmark.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._daily_returns_array = benchmark_source.daily_returns(
sessions[0],
sessions[-1],
).values
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
risk = packet['cumulative_risk_metrics']
alpha, beta = ep.alpha_beta_aligned(
ledger.daily_returns_array[:session_ix + 1],
self._daily_returns_array[:session_ix + 1],
)
if not np.isfinite(alpha):
alpha = None
if np.isnan(beta):
beta = None
risk['alpha'] = alpha
risk['beta'] = beta
end_of_session = end_of_bar
class MaxLeverage(object):
"""Tracks the maximum account leverage.
"""
def start_of_simulation(self, *args):
self._max_leverage = 0.0
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
self._max_leverage = max(self._max_leverage, ledger.account.leverage)
packet['cumulative_risk_metrics']['max_leverage'] = self._max_leverage
end_of_session = end_of_bar
class NumTradingDays(object):
"""Report the number of trading days.
"""
def start_of_simulation(self, *args):
self._num_trading_days = 0
def start_of_session(self, *args):
self._num_trading_days += 1
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
packet['cumulative_risk_metrics']['trading_days'] = (
self._num_trading_days
)
end_of_session = end_of_bar
class _ConstantCumulativeRiskMetric(object):
"""A metric which does not change, ever.
Notes
-----
This exists to maintain the existing structure of the perf packets. We
should kill this as soon as possible.
"""
def __init__(self, field, value):
self._field = field
self._value = value
def end_of_bar(self, packet, *args):
packet['cumulative_risk_metrics'][self._field] = self._value
def end_of_session(self, packet, *args):
packet['cumulative_risk_metrics'][self._field] = self._value
class PeriodLabel(object):
"""Backwards compat, please kill me.
"""
def start_of_session(self, ledger, session, data_portal):
self._label = session.strftime('%Y-%m')
def end_of_bar(self, packet, *args):
packet['cumulative_risk_metrics']['period_label'] = self._label
end_of_session = end_of_bar
class _ClassicRiskMetrics(object):
"""Produces original risk packet.
"""
def start_of_simulation(self,
ledger,
emission_rate,
trading_calendar,
sessions,
benchmark_source):
self._leverages = np.full_like(sessions, np.nan, dtype='float64')
def end_of_session(self,
packet,
ledger,
dt,
session_ix,
data_portal):
self._leverages[session_ix] = ledger.account.leverage
@classmethod
def risk_metric_period(cls,
start_session,
end_session,
algorithm_returns,
benchmark_returns,
algorithm_leverages):
"""
Creates a dictionary representing the state of the risk report.
Parameters
----------
start_session : pd.Timestamp
Start of period (inclusive) to produce metrics on
end_session : pd.Timestamp
End of period (inclusive) to produce metrics on
algorithm_returns : pd.Series(pd.Timestamp -> float)
Series of algorithm returns as of the end of each session
benchmark_returns : pd.Series(pd.Timestamp -> float)
Series of benchmark returns as of the end of each session
algorithm_leverages : pd.Series(pd.Timestamp -> float)
Series of algorithm leverages as of the end of each session
Returns
-------
risk_metric : dict[str, any]
Dict of metrics that with fields like:
{
'algorithm_period_return': 0.0,
'benchmark_period_return': 0.0,
'treasury_period_return': 0,
'excess_return': 0.0,
'alpha': 0.0,
'beta': 0.0,
'sharpe': 0.0,
'sortino': 0.0,
'period_label': '1970-01',
'trading_days': 0,
'algo_volatility': 0.0,
'benchmark_volatility': 0.0,
'max_drawdown': 0.0,
'max_leverage': 0.0,
}
"""
algorithm_returns = algorithm_returns[
(algorithm_returns.index >= start_session) &
(algorithm_returns.index <= end_session)
]
# Benchmark needs to be masked to the same dates as the algo returns
benchmark_returns = benchmark_returns[
(benchmark_returns.index >= start_session) &
(benchmark_returns.index <= algorithm_returns.index[-1])
]
benchmark_period_returns = ep.cum_returns(benchmark_returns).iloc[-1]
algorithm_period_returns = ep.cum_returns(algorithm_returns).iloc[-1]
alpha, beta = ep.alpha_beta_aligned(
algorithm_returns.values,
benchmark_returns.values,
)
benchmark_volatility = ep.annual_volatility(benchmark_returns)
sharpe = ep.sharpe_ratio(algorithm_returns)
# The consumer currently expects a 0.0 value for sharpe in period,
# this differs from cumulative which was np.nan.
# When factoring out the sharpe_ratio, the different return types
# were collapsed into `np.nan`.
# TODO: Either fix consumer to accept `np.nan` or make the
# `sharpe_ratio` return type configurable.
# In the meantime, convert nan values to 0.0
if pd.isnull(sharpe):
sharpe = 0.0
sortino = ep.sortino_ratio(
algorithm_returns.values,
_downside_risk=ep.downside_risk(algorithm_returns.values),
)
rval = {
'algorithm_period_return': algorithm_period_returns,
'benchmark_period_return': benchmark_period_returns,
'treasury_period_return': 0,
'excess_return': algorithm_period_returns,
'alpha': alpha,
'beta': beta,
'sharpe': sharpe,
'sortino': sortino,
'period_label': end_session.strftime("%Y-%m"),
'trading_days': len(benchmark_returns),
'algo_volatility': ep.annual_volatility(algorithm_returns),
'benchmark_volatility': benchmark_volatility,
'max_drawdown': ep.max_drawdown(algorithm_returns.values),
'max_leverage': algorithm_leverages.max(),
}
# check if a field in rval is nan or inf, and replace it with None
# except period_label which is always a str
return {
k: (
None
if k != 'period_label' and not np.isfinite(v) else
v
)
for k, v in iteritems(rval)
}
@classmethod
def _periods_in_range(cls,
months,
end_session,
end_date,
algorithm_returns,
benchmark_returns,
algorithm_leverages,
months_per):
if months.size < months_per:
return
end_date = end_date
for period_timestamp in months:
period = period_timestamp.to_period(freq='%dM' % months_per)
if period.end_time.tz_localize('utc') > end_date:
break
yield cls.risk_metric_period(
start_session=period.start_time.tz_localize('utc'),
end_session=min(period.end_time.tz_localize('utc'), end_session),
algorithm_returns=algorithm_returns,
benchmark_returns=benchmark_returns,
algorithm_leverages=algorithm_leverages,
)
@classmethod
def risk_report(cls,
algorithm_returns,
benchmark_returns,
algorithm_leverages):
start_session = algorithm_returns.index[0]
end_session = algorithm_returns.index[-1]
end = end_session.replace(day=1) + relativedelta(months=1)
months = pd.date_range(
start=start_session,
# Ensure we have at least one month
end=end - datetime.timedelta(days=1),
freq='M',
tz='utc',
)
periods_in_range = partial(
cls._periods_in_range,
months=months,
end_session=end_session,
end_date=end,
algorithm_returns=algorithm_returns,
benchmark_returns=benchmark_returns,
algorithm_leverages=algorithm_leverages,
)
return {
'one_month': list(periods_in_range(months_per=1)),
'three_month': list(periods_in_range(months_per=3)),
'six_month': list(periods_in_range(months_per=6)),
'twelve_month': list(periods_in_range(months_per=12)),
}
def end_of_simulation(self,
packet,
ledger,
trading_calendar,
sessions,
data_portal,
benchmark_source):
packet.update(self.risk_report(
algorithm_returns=ledger.daily_returns_series,
benchmark_returns=benchmark_source.daily_returns(
sessions[0],
sessions[-1],
),
algorithm_leverages=self._leverages,
)) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/finance/metrics/metric.py | metric.py |
from textwrap import dedent
from functools import partial
from numpy import (
bool_,
dtype,
float32,
float64,
int32,
int64,
int16,
uint16,
ndarray,
uint32,
uint8,
)
from six import iteritems
from toolz import merge_with
from zipline.errors import (
WindowLengthNotPositive,
WindowLengthTooLong,
)
from zipline.lib.labelarray import LabelArray
from zipline.utils.numpy_utils import (
datetime64ns_dtype,
float64_dtype,
int64_dtype,
uint8_dtype,
)
from zipline.utils.memoize import lazyval
# These class names are all the same because of our bootleg templating system.
from ._float64window import AdjustedArrayWindow as Float64Window
from ._int64window import AdjustedArrayWindow as Int64Window
from ._labelwindow import AdjustedArrayWindow as LabelWindow
from ._uint8window import AdjustedArrayWindow as UInt8Window
BOOL_DTYPES = frozenset(
map(dtype, [bool_, uint8]),
)
FLOAT_DTYPES = frozenset(
map(dtype, [float32, float64]),
)
INT_DTYPES = frozenset(
# NOTE: uint64 not supported because it can't be safely cast to int64.
map(dtype, [int16, uint16, int32, int64, uint32]),
)
DATETIME_DTYPES = frozenset(
map(dtype, ['datetime64[ns]', 'datetime64[D]']),
)
# We use object arrays for strings.
OBJECT_DTYPES = frozenset(map(dtype, ['O']))
STRING_KINDS = frozenset(['S', 'U'])
REPRESENTABLE_DTYPES = BOOL_DTYPES.union(
FLOAT_DTYPES,
INT_DTYPES,
DATETIME_DTYPES,
OBJECT_DTYPES,
)
def can_represent_dtype(dtype):
"""
Can we build an AdjustedArray for a baseline of `dtype``?
"""
return dtype in REPRESENTABLE_DTYPES or dtype.kind in STRING_KINDS
def is_categorical(dtype):
"""
Do we represent this dtype with LabelArrays rather than ndarrays?
"""
return dtype in OBJECT_DTYPES or dtype.kind in STRING_KINDS
CONCRETE_WINDOW_TYPES = {
float64_dtype: Float64Window,
int64_dtype: Int64Window,
uint8_dtype: UInt8Window,
}
def _normalize_array(data, missing_value):
"""
Coerce buffer data for an AdjustedArray into a standard scalar
representation, returning the coerced array and a dict of argument to pass
to np.view to use when providing a user-facing view of the underlying data.
- float* data is coerced to float64 with viewtype float64.
- int32, int64, and uint32 are converted to int64 with viewtype int64.
- datetime[*] data is coerced to int64 with a viewtype of datetime64[ns].
- bool_ data is coerced to uint8 with a viewtype of bool_.
Parameters
----------
data : np.ndarray
Returns
-------
coerced, view_kwargs : (np.ndarray, np.dtype)
The input ``data`` array coerced to the appropriate pipeline type.
This may return the original array or a view over the same data.
"""
if isinstance(data, LabelArray):
return data, {}
data_dtype = data.dtype
if data_dtype in BOOL_DTYPES:
return data.astype(uint8, copy=False), {'dtype': dtype(bool_)}
elif data_dtype in FLOAT_DTYPES:
return data.astype(float64, copy=False), {'dtype': dtype(float64)}
elif data_dtype in INT_DTYPES:
return data.astype(int64, copy=False), {'dtype': dtype(int64)}
elif is_categorical(data_dtype):
if not isinstance(missing_value, LabelArray.SUPPORTED_SCALAR_TYPES):
raise TypeError(
"Invalid missing_value for categorical array.\n"
"Expected None, bytes or unicode. Got %r." % missing_value,
)
return LabelArray(data, missing_value), {}
elif data_dtype.kind == 'M':
try:
outarray = data.astype('datetime64[ns]', copy=False).view('int64')
return outarray, {'dtype': datetime64ns_dtype}
except OverflowError:
raise ValueError(
"AdjustedArray received a datetime array "
"not representable as datetime64[ns].\n"
"Min Date: %s\n"
"Max Date: %s\n"
% (data.min(), data.max())
)
else:
raise TypeError(
"Don't know how to construct AdjustedArray "
"on data of type %s." % data_dtype
)
def _merge_simple(adjustment_lists, front_idx, back_idx):
"""
Merge lists of new and existing adjustments for a given index by appending
or prepending new adjustments to existing adjustments.
Notes
-----
This method is meant to be used with ``toolz.merge_with`` to merge
adjustment mappings. In case of a collision ``adjustment_lists`` contains
two lists, existing adjustments at index 0 and new adjustments at index 1.
When there are no collisions, ``adjustment_lists`` contains a single list.
Parameters
----------
adjustment_lists : list[list[Adjustment]]
List(s) of new and/or existing adjustments for a given index.
front_idx : int
Index of list in ``adjustment_lists`` that should be used as baseline
in case of a collision.
back_idx : int
Index of list in ``adjustment_lists`` that should extend baseline list
in case of a collision.
Returns
-------
adjustments : list[Adjustment]
List of merged adjustments for a given index.
"""
if len(adjustment_lists) == 1:
return list(adjustment_lists[0])
else:
return adjustment_lists[front_idx] + adjustment_lists[back_idx]
_merge_methods = {
'append': partial(_merge_simple, front_idx=0, back_idx=1),
'prepend': partial(_merge_simple, front_idx=1, back_idx=0),
}
class AdjustedArray(object):
"""
An array that can be iterated with a variable-length window, and which can
provide different views on data from different perspectives.
Parameters
----------
data : np.ndarray
The baseline data values. This array may be mutated by
``traverse(..., copy=False)`` calls.
adjustments : dict[int -> list[Adjustment]]
A dict mapping row indices to lists of adjustments to apply when we
reach that row.
missing_value : object
A value to use to fill missing data in yielded windows.
Should be a value coercible to `data.dtype`.
"""
__slots__ = (
'_data',
'_view_kwargs',
'adjustments',
'missing_value',
'_invalidated',
'__weakref__',
)
def __init__(self, data, adjustments, missing_value):
self._data, self._view_kwargs = _normalize_array(data, missing_value)
self.adjustments = adjustments
self.missing_value = missing_value
self._invalidated = False
def copy(self):
"""Copy an adjusted array, deep-copying the ``data`` array.
"""
if self._invalidated:
raise ValueError('cannot copy invalidated AdjustedArray')
return type(self)(
self.data.copy(order='F'),
self.adjustments,
self.missing_value,
)
def update_adjustments(self, adjustments, method):
"""
Merge ``adjustments`` with existing adjustments, handling index
collisions according to ``method``.
Parameters
----------
adjustments : dict[int -> list[Adjustment]]
The mapping of row indices to lists of adjustments that should be
appended to existing adjustments.
method : {'append', 'prepend'}
How to handle index collisions. If 'append', new adjustments will
be applied after previously-existing adjustments. If 'prepend', new
adjustments will be applied before previously-existing adjustments.
"""
try:
merge_func = _merge_methods[method]
except KeyError:
raise ValueError(
"Invalid merge method %s\n"
"Valid methods are: %s" % (method, ', '.join(_merge_methods))
)
self.adjustments = merge_with(
merge_func,
self.adjustments,
adjustments,
)
@property
def data(self):
"""
The data stored in this array.
"""
return self._data.view(**self._view_kwargs)
@lazyval
def dtype(self):
"""
The dtype of the data stored in this array.
"""
return self._view_kwargs.get('dtype') or self._data.dtype
@lazyval
def _iterator_type(self):
"""
The iterator produced when `traverse` is called on this Array.
"""
if isinstance(self._data, LabelArray):
return LabelWindow
return CONCRETE_WINDOW_TYPES[self._data.dtype]
def traverse(self,
window_length,
offset=0,
perspective_offset=0,
copy=True):
"""
Produce an iterator rolling windows rows over our data.
Each emitted window will have `window_length` rows.
Parameters
----------
window_length : int
The number of rows in each emitted window.
offset : int, optional
Number of rows to skip before the first window. Default is 0.
perspective_offset : int, optional
Number of rows past the end of the current window from which to
"view" the underlying data.
copy : bool, optional
Copy the underlying data. If ``copy=False``, the adjusted array
will be invalidated and cannot be traversed again.
"""
if self._invalidated:
raise ValueError('cannot traverse invalidated AdjustedArray')
data = self._data
if copy:
data = data.copy(order='F')
else:
self._invalidated = True
_check_window_params(data, window_length)
return self._iterator_type(
data,
self._view_kwargs,
self.adjustments,
offset,
window_length,
perspective_offset,
rounding_places=None,
)
def inspect(self):
"""
Return a string representation of the data stored in this array.
"""
return dedent(
"""\
Adjusted Array ({dtype}):
Data:
{data!r}
Adjustments:
{adjustments}
"""
).format(
dtype=self.dtype.name,
data=self.data,
adjustments=self.adjustments,
)
def update_labels(self, func):
"""
Map a function over baseline and adjustment values in place.
Note that the baseline data values must be a LabelArray.
"""
if not isinstance(self.data, LabelArray):
raise TypeError(
'update_labels only supported if data is of type LabelArray.'
)
# Map the baseline values.
self._data = self._data.map(func)
# Map each of the adjustments.
for _, row_adjustments in iteritems(self.adjustments):
for adjustment in row_adjustments:
adjustment.value = func(adjustment.value)
def ensure_adjusted_array(ndarray_or_adjusted_array, missing_value):
if isinstance(ndarray_or_adjusted_array, AdjustedArray):
return ndarray_or_adjusted_array
elif isinstance(ndarray_or_adjusted_array, ndarray):
return AdjustedArray(
ndarray_or_adjusted_array, {}, missing_value,
)
else:
raise TypeError(
"Can't convert %s to AdjustedArray" %
type(ndarray_or_adjusted_array).__name__
)
def ensure_ndarray(ndarray_or_adjusted_array):
"""
Return the input as a numpy ndarray.
This is a no-op if the input is already an ndarray. If the input is an
adjusted_array, this extracts a read-only view of its internal data buffer.
Parameters
----------
ndarray_or_adjusted_array : numpy.ndarray | zipline.data.adjusted_array
Returns
-------
out : The input, converted to an ndarray.
"""
if isinstance(ndarray_or_adjusted_array, ndarray):
return ndarray_or_adjusted_array
elif isinstance(ndarray_or_adjusted_array, AdjustedArray):
return ndarray_or_adjusted_array.data
else:
raise TypeError(
"Can't convert %s to ndarray" %
type(ndarray_or_adjusted_array).__name__
)
def _check_window_params(data, window_length):
"""
Check that a window of length `window_length` is well-defined on `data`.
Parameters
----------
data : np.ndarray[ndim=2]
The array of data to check.
window_length : int
Length of the desired window.
Returns
-------
None
Raises
------
WindowLengthNotPositive
If window_length < 1.
WindowLengthTooLong
If window_length is greater than the number of rows in `data`.
"""
if window_length < 1:
raise WindowLengthNotPositive(window_length=window_length)
if window_length > data.shape[0]:
raise WindowLengthTooLong(
nrows=data.shape[0],
window_length=window_length,
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/lib/adjusted_array.py | adjusted_array.py |
from functools import partial, total_ordering
from operator import eq, ne
import re
import numpy as np
from numpy import ndarray
import pandas as pd
from toolz import compose
from zipline.utils.compat import unicode
from zipline.utils.functional import instance
from zipline.utils.preprocess import preprocess
from zipline.utils.sentinel import sentinel
from zipline.utils.input_validation import (
coerce,
expect_kinds,
expect_types,
optional,
)
from zipline.utils.numpy_utils import (
bool_dtype,
unsigned_int_dtype_with_size_in_bytes,
is_object,
object_dtype,
)
from zipline.utils.pandas_utils import ignore_pandas_nan_categorical_warning
from ._factorize import (
factorize_strings,
factorize_strings_known_categories,
smallest_uint_that_can_hold,
)
def compare_arrays(left, right):
"Eq check with a short-circuit for identical objects."
return (
left is right
or ((left.shape == right.shape) and (left == right).all())
)
def _make_unsupported_method(name):
def method(*args, **kwargs):
raise NotImplementedError(
"Method %s is not supported on LabelArrays." % name
)
method.__name__ = name
method.__doc__ = "Unsupported LabelArray Method: %s" % name
return method
class MissingValueMismatch(ValueError):
"""
Error raised on attempt to perform operations between LabelArrays with
mismatched missing_values.
"""
def __init__(self, left, right):
super(MissingValueMismatch, self).__init__(
"LabelArray missing_values don't match:"
" left={}, right={}".format(left, right)
)
class CategoryMismatch(ValueError):
"""
Error raised on attempt to perform operations between LabelArrays with
mismatched category arrays.
"""
def __init__(self, left, right):
(mismatches,) = np.where(left != right)
assert len(mismatches), "Not actually a mismatch!"
super(CategoryMismatch, self).__init__(
"LabelArray categories don't match:\n"
"Mismatched Indices: {mismatches}\n"
"Left: {left}\n"
"Right: {right}".format(
mismatches=mismatches,
left=left[mismatches],
right=right[mismatches],
)
)
_NotPassed = sentinel('_NotPassed')
class LabelArray(ndarray):
"""
An ndarray subclass for working with arrays of strings.
Factorizes the input array into integers, but overloads equality on strings
to check against the factor label.
Parameters
----------
values : array-like
Array of values that can be passed to np.asarray with dtype=object.
missing_value : str
Scalar value to treat as 'missing' for operations on ``self``.
categories : list[str], optional
List of values to use as categories. If not supplied, categories will
be inferred as the unique set of entries in ``values``.
sort : bool, optional
Whether to sort categories. If sort is False and categories is
supplied, they are left in the order provided. If sort is False and
categories is None, categories will be constructed in a random order.
Attributes
----------
categories : ndarray[str]
An array containing the unique labels of self.
reverse_categories : dict[str -> int]
Reverse lookup table for ``categories``. Stores the index in
``categories`` at which each entry each unique entry is found.
missing_value : str or None
A sentinel missing value with NaN semantics for comparisons.
Notes
-----
Consumers should be cautious when passing instances of LabelArray to numpy
functions. We attempt to disallow as many meaningless operations as
possible, but since a LabelArray is just an ndarray of ints with some
additional metadata, many numpy functions (for example, trigonometric) will
happily accept a LabelArray and treat its values as though they were
integers.
In a future change, we may be able to disallow more numerical operations by
creating a wrapper dtype which doesn't register an implementation for most
numpy ufuncs. Until that change is made, consumers of LabelArray should
assume that it is undefined behavior to pass a LabelArray to any numpy
ufunc that operates on semantically-numerical data.
See Also
--------
https://docs.scipy.org/doc/numpy-1.11.0/user/basics.subclassing.html
"""
SUPPORTED_SCALAR_TYPES = (bytes, unicode, type(None))
SUPPORTED_NON_NONE_SCALAR_TYPES = (bytes, unicode)
@preprocess(
values=coerce(list, partial(np.asarray, dtype=object)),
# Coerce ``list`` to ``list`` to make a copy. Code internally may call
# ``categories.insert(0, missing_value)`` which will mutate this list
# in place.
categories=coerce((list, np.ndarray, set), list),
)
@expect_types(
values=np.ndarray,
missing_value=SUPPORTED_SCALAR_TYPES,
categories=optional(list),
)
@expect_kinds(values=("O", "S", "U"))
def __new__(cls,
values,
missing_value,
categories=None,
sort=True):
# Numpy's fixed-width string types aren't very efficient. Working with
# object arrays is faster than bytes or unicode arrays in almost all
# cases.
if not is_object(values):
values = values.astype(object)
if values.flags.f_contiguous:
ravel_order = 'F'
else:
ravel_order = 'C'
if categories is None:
codes, categories, reverse_categories = factorize_strings(
values.ravel(ravel_order),
missing_value=missing_value,
sort=sort,
)
else:
codes, categories, reverse_categories = (
factorize_strings_known_categories(
values.ravel(ravel_order),
categories=categories,
missing_value=missing_value,
sort=sort,
)
)
categories.setflags(write=False)
return cls.from_codes_and_metadata(
codes=codes.reshape(values.shape, order=ravel_order),
categories=categories,
reverse_categories=reverse_categories,
missing_value=missing_value,
)
@classmethod
def from_codes_and_metadata(cls,
codes,
categories,
reverse_categories,
missing_value):
"""
Rehydrate a LabelArray from the codes and metadata.
Parameters
----------
codes : np.ndarray[integral]
The codes for the label array.
categories : np.ndarray[object]
The unique string categories.
reverse_categories : dict[str, int]
The mapping from category to its code-index.
missing_value : any
The value used to represent missing data.
"""
ret = codes.view(type=cls, dtype=np.void)
ret._categories = categories
ret._reverse_categories = reverse_categories
ret._missing_value = missing_value
return ret
@classmethod
def from_categorical(cls, categorical, missing_value=None):
"""
Create a LabelArray from a pandas categorical.
Parameters
----------
categorical : pd.Categorical
The categorical object to convert.
missing_value : bytes, unicode, or None, optional
The missing value to use for this LabelArray.
Returns
-------
la : LabelArray
The LabelArray representation of this categorical.
"""
return LabelArray(
categorical,
missing_value,
categorical.categories,
)
@property
def categories(self):
# This is a property because it should be immutable.
return self._categories
@property
def reverse_categories(self):
# This is a property because it should be immutable.
return self._reverse_categories
@property
def missing_value(self):
# This is a property because it should be immutable.
return self._missing_value
@property
def missing_value_code(self):
return self.reverse_categories[self.missing_value]
def has_label(self, value):
return value in self.reverse_categories
def __array_finalize__(self, obj):
"""
Called by Numpy after array construction.
There are three cases where this can happen:
1. Someone tries to directly construct a new array by doing::
>>> ndarray.__new__(LabelArray, ...) # doctest: +SKIP
In this case, obj will be None. We treat this as an error case and
fail.
2. Someone (most likely our own __new__) does::
>>> other_array.view(type=LabelArray) # doctest: +SKIP
In this case, `self` will be the new LabelArray instance, and
``obj` will be the array on which ``view`` is being called.
The caller of ``obj.view`` is responsible for setting category
metadata on ``self`` after we exit.
3. Someone creates a new LabelArray by slicing an existing one.
In this case, ``obj`` will be the original LabelArray. We're
responsible for copying over the parent array's category metadata.
"""
if obj is None:
raise TypeError(
"Direct construction of LabelArrays is not supported."
)
# See docstring for an explanation of when these will or will not be
# set.
self._categories = getattr(obj, 'categories', None)
self._reverse_categories = getattr(obj, 'reverse_categories', None)
self._missing_value = getattr(obj, 'missing_value', None)
def as_int_array(self):
"""
Convert self into a regular ndarray of ints.
This is an O(1) operation. It does not copy the underlying data.
"""
return self.view(
type=ndarray,
dtype=unsigned_int_dtype_with_size_in_bytes(self.itemsize),
)
def as_string_array(self):
"""
Convert self back into an array of strings.
This is an O(N) operation.
"""
return self.categories[self.as_int_array()]
def as_categorical(self):
"""
Coerce self into a pandas categorical.
This is only defined on 1D arrays, since that's all pandas supports.
"""
if len(self.shape) > 1:
raise ValueError("Can't convert a 2D array to a categorical.")
with ignore_pandas_nan_categorical_warning():
return pd.Categorical.from_codes(
self.as_int_array(),
# We need to make a copy because pandas >= 0.17 fails if this
# buffer isn't writeable.
self.categories.copy(),
ordered=False,
)
def as_categorical_frame(self, index, columns, name=None):
"""
Coerce self into a pandas DataFrame of Categoricals.
"""
if len(self.shape) != 2:
raise ValueError(
"Can't convert a non-2D LabelArray into a DataFrame."
)
expected_shape = (len(index), len(columns))
if expected_shape != self.shape:
raise ValueError(
"Can't construct a DataFrame with provided indices:\n\n"
"LabelArray shape is {actual}, but index and columns imply "
"that shape should be {expected}.".format(
actual=self.shape,
expected=expected_shape,
)
)
return pd.Series(
index=pd.MultiIndex.from_product([index, columns]),
data=self.ravel().as_categorical(),
name=name,
).unstack()
def __setitem__(self, indexer, value):
self_categories = self.categories
if isinstance(value, self.SUPPORTED_SCALAR_TYPES):
value_code = self.reverse_categories.get(value, None)
if value_code is None:
raise ValueError("%r is not in LabelArray categories." % value)
self.as_int_array()[indexer] = value_code
elif isinstance(value, LabelArray):
value_categories = value.categories
if compare_arrays(self_categories, value_categories):
return super(LabelArray, self).__setitem__(indexer, value)
elif (self.missing_value == value.missing_value and
set(value.categories) <= set(self.categories)):
rhs = LabelArray.from_codes_and_metadata(
*factorize_strings_known_categories(
value.as_string_array().ravel(),
list(self.categories),
self.missing_value,
False,
),
missing_value=self.missing_value
).reshape(value.shape)
super(LabelArray, self).__setitem__(indexer, rhs)
else:
raise CategoryMismatch(self_categories, value_categories)
else:
raise NotImplementedError(
"Setting into a LabelArray with a value of "
"type {type} is not yet supported.".format(
type=type(value).__name__,
),
)
def set_scalar(self, indexer, value):
"""
Set scalar value into the array.
Parameters
----------
indexer : any
The indexer to set the value at.
value : str
The value to assign at the given locations.
Raises
------
ValueError
Raised when ``value`` is not a value element of this this label
array.
"""
try:
value_code = self.reverse_categories[value]
except KeyError:
raise ValueError("%r is not in LabelArray categories." % value)
self.as_int_array()[indexer] = value_code
def __setslice__(self, i, j, sequence):
"""
This method was deprecated in Python 2.0. It predates slice objects,
but Python 2.7.11 still uses it if you implement it, which ndarray
does. In newer Pythons, __setitem__ is always called, but we need to
manuallly forward in py2.
"""
self.__setitem__(slice(i, j), sequence)
def __getitem__(self, indexer):
result = super(LabelArray, self).__getitem__(indexer)
if result.ndim:
# Result is still a LabelArray, so we can just return it.
return result
# Result is a scalar value, which will be an instance of np.void.
# Map it back to one of our category entries.
index = result.view(
unsigned_int_dtype_with_size_in_bytes(self.itemsize),
)
return self.categories[index]
def is_missing(self):
"""
Like isnan, but checks for locations where we store missing values.
"""
return (
self.as_int_array() == self.reverse_categories[self.missing_value]
)
def not_missing(self):
"""
Like ~isnan, but checks for locations where we store missing values.
"""
return (
self.as_int_array() != self.reverse_categories[self.missing_value]
)
def _equality_check(op):
"""
Shared code for __eq__ and __ne__, parameterized on the actual
comparison operator to use.
"""
def method(self, other):
if isinstance(other, LabelArray):
self_mv = self.missing_value
other_mv = other.missing_value
if self_mv != other_mv:
raise MissingValueMismatch(self_mv, other_mv)
self_categories = self.categories
other_categories = other.categories
if not compare_arrays(self_categories, other_categories):
raise CategoryMismatch(self_categories, other_categories)
return (
op(self.as_int_array(), other.as_int_array())
& self.not_missing()
& other.not_missing()
)
elif isinstance(other, ndarray):
# Compare to ndarrays as though we were an array of strings.
# This is fairly expensive, and should generally be avoided.
return op(self.as_string_array(), other) & self.not_missing()
elif isinstance(other, self.SUPPORTED_SCALAR_TYPES):
i = self._reverse_categories.get(other, -1)
return op(self.as_int_array(), i) & self.not_missing()
return op(super(LabelArray, self), other)
return method
__eq__ = _equality_check(eq)
__ne__ = _equality_check(ne)
del _equality_check
def view(self, dtype=_NotPassed, type=_NotPassed):
if type is _NotPassed and dtype not in (_NotPassed, self.dtype):
raise TypeError("Can't view LabelArray as another dtype.")
# The text signature on ndarray.view makes it look like the default
# values for dtype and type are `None`, but passing None explicitly has
# different semantics than not passing an arg at all, so we reconstruct
# the kwargs dict here to simulate the args not being passed at all.
kwargs = {}
if dtype is not _NotPassed:
kwargs['dtype'] = dtype
if type is not _NotPassed:
kwargs['type'] = type
return super(LabelArray, self).view(**kwargs)
def astype(self,
dtype,
order='K',
casting='unsafe',
subok=True,
copy=True):
if dtype == self.dtype:
if not subok:
array = self.view(type=np.ndarray)
else:
array = self
if copy:
return array.copy()
return array
if dtype == object_dtype:
return self.as_string_array()
if dtype.kind == 'S':
return self.as_string_array().astype(
dtype,
order=order,
casting=casting,
subok=subok,
copy=copy,
)
raise TypeError(
'%s can only be converted into object, string, or void,'
' got: %r' % (
type(self).__name__,
dtype,
),
)
# In general, we support resizing, slicing, and reshaping methods, but not
# numeric methods.
SUPPORTED_NDARRAY_METHODS = frozenset([
'astype',
'base',
'compress',
'copy',
'data',
'diagonal',
'dtype',
'flat',
'flatten',
'item',
'itemset',
'itemsize',
'nbytes',
'ndim',
'ravel',
'repeat',
'reshape',
'resize',
'setflags',
'shape',
'size',
'squeeze',
'strides',
'swapaxes',
'take',
'trace',
'transpose',
'view'
])
PUBLIC_NDARRAY_METHODS = frozenset([
s for s in dir(ndarray) if not s.startswith('_')
])
# Generate failing wrappers for all unsupported methods.
locals().update(
{
method: _make_unsupported_method(method)
for method in PUBLIC_NDARRAY_METHODS - SUPPORTED_NDARRAY_METHODS
}
)
def __repr__(self):
repr_lines = repr(self.as_string_array()).splitlines()
repr_lines[0] = repr_lines[0].replace('array(', 'LabelArray(', 1)
repr_lines[-1] = repr_lines[-1].rsplit(',', 1)[0] + ')'
# The extra spaces here account for the difference in length between
# 'array(' and 'LabelArray('.
return '\n '.join(repr_lines)
def empty_like(self, shape):
"""
Make an empty LabelArray with the same categories as ``self``, filled
with ``self.missing_value``.
"""
return type(self).from_codes_and_metadata(
codes=np.full(
shape,
self.reverse_categories[self.missing_value],
dtype=unsigned_int_dtype_with_size_in_bytes(self.itemsize),
),
categories=self.categories,
reverse_categories=self.reverse_categories,
missing_value=self.missing_value,
)
def map_predicate(self, f):
"""
Map a function from str -> bool element-wise over ``self``.
``f`` will be applied exactly once to each non-missing unique value in
``self``. Missing values will always return False.
"""
# Functions passed to this are of type str -> bool. Don't ever call
# them on None, which is the only non-str value we ever store in
# categories.
if self.missing_value is None:
def f_to_use(x):
return False if x is None else f(x)
else:
f_to_use = f
# Call f on each unique value in our categories.
results = np.vectorize(f_to_use, otypes=[bool_dtype])(self.categories)
# missing_value should produce False no matter what
results[self.reverse_categories[self.missing_value]] = False
# unpack the results form each unique value into their corresponding
# locations in our indices.
return results[self.as_int_array()]
def map(self, f):
"""
Map a function from str -> str element-wise over ``self``.
``f`` will be applied exactly once to each non-missing unique value in
``self``. Missing values will always map to ``self.missing_value``.
"""
# f() should only return None if None is our missing value.
if self.missing_value is None:
allowed_outtypes = self.SUPPORTED_SCALAR_TYPES
else:
allowed_outtypes = self.SUPPORTED_NON_NONE_SCALAR_TYPES
def f_to_use(x,
missing_value=self.missing_value,
otypes=allowed_outtypes):
# Don't call f on the missing value; those locations don't exist
# semantically. We return _sortable_sentinel rather than None
# because the np.unique call below sorts the categories array,
# which raises an error on Python 3 because None and str aren't
# comparable.
if x == missing_value:
return _sortable_sentinel
ret = f(x)
if not isinstance(ret, otypes):
raise TypeError(
"LabelArray.map expected function {f} to return a string"
" or None, but got {type} instead.\n"
"Value was {value}.".format(
f=f.__name__,
type=type(ret).__name__,
value=ret,
)
)
if ret == missing_value:
return _sortable_sentinel
return ret
new_categories_with_duplicates = (
np.vectorize(f_to_use, otypes=[object])(self.categories)
)
# If f() maps multiple inputs to the same output, then we can end up
# with the same code duplicated multiple times. Compress the categories
# by running them through np.unique, and then use the reverse lookup
# table to compress codes as well.
new_categories, bloated_inverse_index = np.unique(
new_categories_with_duplicates,
return_inverse=True
)
if new_categories[0] is _sortable_sentinel:
# f_to_use return _sortable_sentinel for locations that should be
# missing values in our output. Since np.unique returns the uniques
# in sorted order, and since _sortable_sentinel sorts before any
# string, we only need to check the first array entry.
new_categories[0] = self.missing_value
# `reverse_index` will always be a 64 bit integer even if we can hold a
# smaller array.
reverse_index = bloated_inverse_index.astype(
smallest_uint_that_can_hold(len(new_categories))
)
new_codes = np.take(reverse_index, self.as_int_array())
return self.from_codes_and_metadata(
new_codes,
new_categories,
dict(zip(new_categories, range(len(new_categories)))),
missing_value=self.missing_value,
)
def startswith(self, prefix):
"""
Element-wise startswith.
Parameters
----------
prefix : str
Returns
-------
matches : np.ndarray[bool]
An array with the same shape as self indicating whether each
element of self started with ``prefix``.
"""
return self.map_predicate(lambda elem: elem.startswith(prefix))
def endswith(self, suffix):
"""
Elementwise endswith.
Parameters
----------
suffix : str
Returns
-------
matches : np.ndarray[bool]
An array with the same shape as self indicating whether each
element of self ended with ``suffix``
"""
return self.map_predicate(lambda elem: elem.endswith(suffix))
def has_substring(self, substring):
"""
Elementwise contains.
Parameters
----------
substring : str
Returns
-------
matches : np.ndarray[bool]
An array with the same shape as self indicating whether each
element of self ended with ``suffix``.
"""
return self.map_predicate(lambda elem: substring in elem)
@preprocess(pattern=coerce(from_=(bytes, unicode), to=re.compile))
def matches(self, pattern):
"""
Elementwise regex match.
Parameters
----------
pattern : str or compiled regex
Returns
-------
matches : np.ndarray[bool]
An array with the same shape as self indicating whether each
element of self was matched by ``pattern``.
"""
return self.map_predicate(compose(bool, pattern.match))
# These types all implement an O(N) __contains__, so pre-emptively
# coerce to `set`.
@preprocess(container=coerce((list, tuple, np.ndarray), set))
def element_of(self, container):
"""
Check if each element of self is an of ``container``.
Parameters
----------
container : object
An object implementing a __contains__ to call on each element of
``self``.
Returns
-------
is_contained : np.ndarray[bool]
An array with the same shape as self indicating whether each
element of self was an element of ``container``.
"""
return self.map_predicate(container.__contains__)
@instance # This makes _sortable_sentinel a singleton instance.
@total_ordering
class _sortable_sentinel(object):
"""Dummy object that sorts before any other python object.
"""
def __eq__(self, other):
return self is other
def __lt__(self, other):
return True
@expect_types(trues=LabelArray, falses=LabelArray)
def labelarray_where(cond, trues, falses):
"""LabelArray-aware implementation of np.where.
"""
if trues.missing_value != falses.missing_value:
raise ValueError(
"Can't compute where on arrays with different missing values."
)
strs = np.where(cond, trues.as_string_array(), falses.as_string_array())
return LabelArray(strs, missing_value=trues.missing_value) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/lib/labelarray.py | labelarray.py |
import os
import subprocess
import networkx as nx
def debug_mro_failure(name, bases):
graph = build_linearization_graph(name, bases)
cycles = sorted(nx.cycles.simple_cycles(graph), key=len)
cycle = cycles[0]
if os.environ.get('DRAW_MRO_FAILURES'):
output_file = name + '.dot'
else:
output_file = None
# Return a nicely formatted error describing the cycle.
lines = ["Cycle found when trying to compute MRO for {}:\n".format(name)]
for source, dest in list(zip(cycle, cycle[1:])) + [(cycle[-1], cycle[0])]:
label = verbosify_label(graph.get_edge_data(source, dest)['label'])
lines.append("{} comes before {}: cause={}"
.format(source, dest, label))
# Either graphviz graph and tell the user where it went, or tell people how
# to enable that feature.
lines.append('')
if output_file is None:
lines.append("Set the DRAW_MRO_FAILURES environment variable to"
" render a GraphViz graph of this cycle.")
else:
try:
nx.write_dot(graph.subgraph(cycle), output_file)
subprocess.check_call(['dot', '-T', 'svg', '-O', output_file])
lines.append(
"GraphViz rendering written to "
+ output_file + '.svg'
)
except Exception as e:
lines.append(
"Failed to write GraphViz graph. Error was {}".format(e)
)
return '\n'.join(lines)
def build_linearization_graph(child_name, bases):
g = nx.DiGraph()
_build_linearization_graph(g, type(child_name, (object,), {}), bases)
return g
def _build_linearization_graph(g, child, bases):
add_implicit_edges(g, child, bases)
add_direct_edges(g, child, bases)
def add_direct_edges(g, child, bases):
# Enforce that bases are ordered in the order that the appear in child's
# class declaration.
g.add_path([b.__name__ for b in bases], label=child.__name__ + '(O)')
# Add direct edges.
for base in bases:
g.add_edge(child.__name__, base.__name__, label=child.__name__ + '(D)')
add_direct_edges(g, base, base.__bases__)
def add_implicit_edges(g, child, bases):
# Enforce that bases' previous linearizations are preserved.
for base in bases:
g.add_path(
[b.__name__ for b in base.mro()],
label=base.__name__ + '(L)',
)
VERBOSE_LABELS = {
"(D)": "(Direct Subclass)",
"(O)": "(Parent Class Order)",
"(L)": "(Linearization Order)",
}
def verbosify_label(label):
prefix = label[:-3]
suffix = label[-3:]
return " ".join([prefix, VERBOSE_LABELS[suffix]]) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/testing/debug.py | debug.py |
from collections import OrderedDict
from contextlib import contextmanager
import datetime
from functools import partial
import re
from nose.tools import ( # noqa
assert_almost_equal,
assert_almost_equals,
assert_dict_contains_subset,
assert_false,
assert_greater,
assert_greater_equal,
assert_in,
assert_is,
assert_is_instance,
assert_is_none,
assert_is_not,
assert_is_not_none,
assert_less,
assert_less_equal,
assert_multi_line_equal,
assert_not_almost_equal,
assert_not_almost_equals,
assert_not_equal,
assert_not_equals,
assert_not_in,
assert_not_is_instance,
assert_raises,
assert_raises_regexp,
assert_regexp_matches,
assert_true,
assert_tuple_equal,
)
import numpy as np
import pandas as pd
from pandas.testing import (
assert_frame_equal,
assert_series_equal,
assert_index_equal,
)
from six import iteritems, viewkeys, PY2
from six.moves import zip_longest
from toolz import dissoc, keyfilter
import toolz.curried.operator as op
from zipline.assets import Asset
from zipline.dispatch import dispatch
from zipline.lib.adjustment import Adjustment
from zipline.lib.labelarray import LabelArray
from zipline.testing.core import ensure_doctest
from zipline.utils.compat import getargspec, mappingproxy
from zipline.utils.formatting import s
from zipline.utils.functional import dzip_exact, instance
from zipline.utils.math_utils import tolerant_equals
from zipline.utils.numpy_utils import (
assert_array_compare,
compare_datetime_arrays,
)
@instance
@ensure_doctest
class wildcard(object):
"""An object that compares equal to any other object.
This is useful when using :func:`~zipline.testing.predicates.assert_equal`
with a large recursive structure and some fields to be ignored.
Examples
--------
>>> wildcard == 5
True
>>> wildcard == 'ayy'
True
# reflected
>>> 5 == wildcard
True
>>> 'ayy' == wildcard
True
"""
@staticmethod
def __eq__(other):
return True
@staticmethod
def __ne__(other):
return False
def __repr__(self):
return '<%s>' % type(self).__name__
class instance_of(object):
"""An object that compares equal to any instance of a given type or types.
Parameters
----------
types : type or tuple[type]
The types to compare equal to.
exact : bool, optional
Only compare equal to exact instances, not instances of subclasses?
"""
def __init__(self, types, exact=False):
if not isinstance(types, tuple):
types = (types,)
for type_ in types:
if not isinstance(type_, type):
raise TypeError('types must be a type or tuple of types')
self.types = types
self.exact = exact
def __eq__(self, other):
if self.exact:
return type(other) in self.types
return isinstance(other, self.types)
def __ne__(self, other):
return not self == other
def __repr__(self):
typenames = tuple(t.__name__ for t in self.types)
return '%s(%s%s)' % (
type(self).__name__,
(
typenames[0]
if len(typenames) == 1 else
'(%s)' % ', '.join(typenames)
),
', exact=True' if self.exact else ''
)
def keywords(func):
"""Get the argument names of a function
>>> def f(x, y=2):
... pass
>>> keywords(f)
['x', 'y']
Notes
-----
Taken from odo.utils
"""
if isinstance(func, type):
return keywords(func.__init__)
elif isinstance(func, partial):
return keywords(func.func)
return getargspec(func).args
def filter_kwargs(f, kwargs):
"""Return a dict of valid kwargs for `f` from a subset of `kwargs`
Examples
--------
>>> def f(a, b=1, c=2):
... return a + b + c
...
>>> raw_kwargs = dict(a=1, b=3, d=4)
>>> f(**raw_kwargs)
Traceback (most recent call last):
...
TypeError: f() got an unexpected keyword argument 'd'
>>> kwargs = filter_kwargs(f, raw_kwargs)
>>> f(**kwargs)
6
Notes
-----
Taken from odo.utils
"""
return keyfilter(op.contains(keywords(f)), kwargs)
def _fmt_path(path):
"""Format the path for final display.
Parameters
----------
path : iterable of str
The path to the values that are not equal.
Returns
-------
fmtd : str
The formatted path to put into the error message.
"""
if not path:
return ''
return 'path: _' + ''.join(path)
def _fmt_msg(msg):
"""Format the message for final display.
Parameters
----------
msg : str
The message to show to the user to provide additional context.
returns
-------
fmtd : str
The formatted message to put into the error message.
"""
if not msg:
return ''
return msg + '\n'
def _safe_cls_name(cls):
try:
return cls.__name__
except AttributeError:
return repr(cls)
def assert_is_subclass(subcls, cls, msg=''):
"""Assert that ``subcls`` is a subclass of ``cls``.
Parameters
----------
subcls : type
The type to check.
cls : type
The type to check ``subcls`` against.
msg : str, optional
An extra assertion message to print if this fails.
"""
assert issubclass(subcls, cls), (
'%s is not a subclass of %s\n%s' % (
_safe_cls_name(subcls),
_safe_cls_name(cls),
msg,
)
)
def assert_is_not_subclass(not_subcls, cls, msg=''):
"""Assert that ``not_subcls`` is not a subclass of ``cls``.
Parameters
----------
not_subcls : type
The type to check.
cls : type
The type to check ``not_subcls`` against.
msg : str, optional
An extra assertion message to print if this fails.
"""
assert not issubclass(not_subcls, cls), (
'%s is a subclass of %s\n%s' % (
_safe_cls_name(not_subcls),
_safe_cls_name(cls),
msg,
)
)
def assert_regex(result, expected, msg=''):
"""Assert that ``expected`` matches the result.
Parameters
----------
result : str
The string to search.
expected : str or compiled regex
The pattern to search for in ``result``.
msg : str, optional
An extra assertion message to print if this fails.
"""
assert re.search(expected, result), (
'%s%r not found in %r' % (_fmt_msg(msg), expected, result)
)
@contextmanager
def _assert_raises_helper(do_check, exc_type, msg):
try:
yield
except exc_type as e:
do_check(e)
else:
raise AssertionError('%s%s was not raised' % (_fmt_msg(msg), exc_type))
def assert_raises_regex(exc, pattern, msg=''):
"""Assert that some exception is raised in a context and that the message
matches some pattern.
Parameters
----------
exc : type or tuple[type]
The exception type or types to expect.
pattern : str or compiled regex
The pattern to search for in the str of the raised exception.
msg : str, optional
An extra assertion message to print if this fails.
"""
def check_exception(e):
assert re.search(pattern, str(e)), (
'%s%r not found in %r' % (_fmt_msg(msg), pattern, str(e))
)
return _assert_raises_helper(
do_check=check_exception,
exc_type=exc,
msg=msg,
)
def assert_raises_str(exc, expected_str, msg=''):
"""Assert that some exception is raised in a context and that the message
exactly matches some string.
Parameters
----------
exc : type or tuple[type]
The exception type or types to expect.
expected_str : str
The expected result of ``str(exception)``.
msg : str, optional
An extra assertion message to print if this fails.
"""
def check_exception(e):
result = str(e)
assert_messages_equal(result, expected_str, msg=msg)
return _assert_raises_helper(
check_exception,
exc_type=exc,
msg=msg,
)
def make_assert_equal_assertion_error(assertion_message, path, msg):
"""Create an assertion error formatted for use in ``assert_equal``.
Parameters
----------
assertion_message : str
The concrete reason for the failure.
path : tuple[str]
The path leading up to the failure.
msg : str
The user supplied message.
Returns
-------
exception_instance : AssertionError
The new exception instance.
Notes
-----
This doesn't raise the exception, it only returns it.
"""
return AssertionError(
'%s%s\n%s' % (
_fmt_msg(msg),
assertion_message,
_fmt_path(path),
),
)
@dispatch(object, object)
def assert_equal(result, expected, path=(), msg='', **kwargs):
"""Assert that two objects are equal using the ``==`` operator.
Parameters
----------
result : object
The result that came from the function under test.
expected : object
The expected result.
Raises
------
AssertionError
Raised when ``result`` is not equal to ``expected``.
"""
if result != expected:
raise make_assert_equal_assertion_error(
'%s != %s' % (result, expected),
path,
msg,
)
@assert_equal.register(float, float)
def assert_float_equal(result,
expected,
path=(),
msg='',
float_rtol=10e-7,
float_atol=10e-7,
float_equal_nan=True,
**kwargs):
assert tolerant_equals(
result,
expected,
rtol=float_rtol,
atol=float_atol,
equal_nan=float_equal_nan,
), '%s%s != %s with rtol=%s and atol=%s%s\n%s' % (
_fmt_msg(msg),
result,
expected,
float_rtol,
float_atol,
(' (with nan != nan)' if not float_equal_nan else ''),
_fmt_path(path),
)
def _check_sets(result, expected, msg, path, type_):
"""Compare two sets. This is used to check dictionary keys and sets.
Parameters
----------
result : set
expected : set
msg : str
path : tuple
type : str
The type of an element. For dict we use ``'key'`` and for set we use
``'element'``.
"""
if result != expected:
if result > expected:
diff = result - expected
msg = 'extra %s in result: %r' % (s(type_, diff), diff)
elif result < expected:
diff = expected - result
msg = 'result is missing %s: %r' % (s(type_, diff), diff)
else:
in_result = result - expected
in_expected = expected - result
msg = '%s only in result: %s\n%s only in expected: %s' % (
s(type_, in_result),
in_result,
s(type_, in_expected),
in_expected,
)
raise AssertionError(
'%ss do not match\n%s%s' % (
type_,
_fmt_msg(msg),
_fmt_path(path),
),
)
@assert_equal.register(dict, dict)
def assert_dict_equal(result, expected, path=(), msg='', **kwargs):
_check_sets(
viewkeys(result),
viewkeys(expected),
msg,
path + ('.%s()' % ('viewkeys' if PY2 else 'keys'),),
'key',
)
failures = []
for k, (resultv, expectedv) in iteritems(dzip_exact(result, expected)):
try:
assert_equal(
resultv,
expectedv,
path=path + ('[%r]' % (k,),),
msg=msg,
**kwargs
)
except AssertionError as e:
failures.append(str(e))
if failures:
raise AssertionError('\n===\n'.join(failures))
@assert_equal.register(mappingproxy, mappingproxy)
def asssert_mappingproxy_equal(result, expected, path=(), msg='', **kwargs):
# mappingproxies compare like dict but shouldn't compare to dicts
_check_sets(
set(result),
set(expected),
msg,
path + ('.keys()',),
'key',
)
failures = []
for k, resultv in iteritems(result):
# we know this exists because of the _check_sets call above
expectedv = expected[k]
try:
assert_equal(
resultv,
expectedv,
path=path + ('[%r]' % (k,),),
msg=msg,
**kwargs
)
except AssertionError as e:
failures.append(str(e))
if failures:
raise AssertionError('\n'.join(failures))
@assert_equal.register(OrderedDict, OrderedDict)
def assert_ordereddict_equal(result, expected, path=(), **kwargs):
assert_sequence_equal(
result.items(),
expected.items(),
path=path + ('.items()',),
**kwargs
)
@assert_equal.register(list, list)
@assert_equal.register(tuple, tuple)
def assert_sequence_equal(result, expected, path=(), msg='', **kwargs):
result_len = len(result)
expected_len = len(expected)
assert result_len == expected_len, (
'%s%s lengths do not match: %d != %d\n%s' % (
_fmt_msg(msg),
type(result).__name__,
result_len,
expected_len,
_fmt_path(path),
)
)
for n, (resultv, expectedv) in enumerate(zip(result, expected)):
assert_equal(
resultv,
expectedv,
path=path + ('[%d]' % n,),
msg=msg,
**kwargs
)
@assert_equal.register(set, set)
def assert_set_equal(result, expected, path=(), msg='', **kwargs):
_check_sets(
result,
expected,
msg,
path,
'element',
)
@assert_equal.register(np.ndarray, np.ndarray)
def assert_array_equal(result,
expected,
path=(),
msg='',
array_verbose=True,
array_decimal=None,
**kwargs):
result_dtype = result.dtype
expected_dtype = expected.dtype
if result_dtype.kind in 'mM' and expected_dtype.kind in 'mM':
assert result_dtype == expected_dtype, (
"\nType mismatch:\n\n"
"result dtype: %s\n"
"expected dtype: %s\n%s"
% (result_dtype, expected_dtype, _fmt_path(path))
)
f = partial(
assert_array_compare,
compare_datetime_arrays,
header='Arrays are not equal',
)
elif array_decimal is not None and expected_dtype.kind not in {'O', 'S'}:
f = partial(
np.testing.assert_array_almost_equal,
decimal=array_decimal,
)
else:
f = np.testing.assert_array_equal
try:
f(
result,
expected,
verbose=array_verbose,
err_msg=msg,
)
except AssertionError as e:
raise AssertionError('\n'.join((str(e), _fmt_path(path))))
@assert_equal.register(LabelArray, LabelArray)
def assert_labelarray_equal(result, expected, path=(), **kwargs):
assert_equal(
result.categories,
expected.categories,
path=path + ('.categories',),
**kwargs
)
assert_equal(
result.as_int_array(),
expected.as_int_array(),
path=path + ('.as_int_array()',),
**kwargs
)
def _register_assert_equal_wrapper(type_, assert_eq):
"""Register a new check for an ndframe object.
Parameters
----------
type_ : type
The class to register an ``assert_equal`` dispatch for.
assert_eq : callable[type_, type_]
The function which checks that if the two ndframes are equal.
Returns
-------
assert_ndframe_equal : callable[type_, type_]
The wrapped function registered with ``assert_equal``.
"""
@assert_equal.register(type_, type_)
def assert_ndframe_equal(result, expected, path=(), msg='', **kwargs):
try:
assert_eq(
result,
expected,
**filter_kwargs(assert_eq, kwargs)
)
except AssertionError as e:
raise AssertionError(
_fmt_msg(msg) + '\n'.join((str(e), _fmt_path(path))),
)
return assert_ndframe_equal
assert_frame_equal = _register_assert_equal_wrapper(
pd.DataFrame,
assert_frame_equal,
)
assert_panel_equal = _register_assert_equal_wrapper(
pd.Panel,
assert_frame_equal,
)
assert_series_equal = _register_assert_equal_wrapper(
pd.Series,
assert_series_equal,
)
assert_index_equal = _register_assert_equal_wrapper(
pd.Index,
assert_index_equal,
)
@assert_equal.register(pd.Categorical, pd.Categorical)
def assert_categorical_equal(result, expected, path=(), msg='', **kwargs):
assert_equal(
result.categories,
expected.categories,
path=path + ('.categories',),
msg=msg,
**kwargs
)
assert_equal(
result.codes,
expected.codes,
path=path + ('.codes',),
msg=msg,
**kwargs
)
@assert_equal.register(Adjustment, Adjustment)
def assert_adjustment_equal(result, expected, path=(), **kwargs):
for attr in ('first_row', 'last_row', 'first_col', 'last_col', 'value'):
assert_equal(
getattr(result, attr),
getattr(expected, attr),
path=path + ('.' + attr,),
**kwargs
)
@assert_equal.register(
(datetime.datetime, np.datetime64),
(datetime.datetime, np.datetime64),
)
def assert_timestamp_and_datetime_equal(result,
expected,
path=(),
msg='',
allow_datetime_coercions=False,
compare_nat_equal=True,
**kwargs):
"""
Branch for comparing python datetime (which includes pandas Timestamp) and
np.datetime64 as equal.
Returns raises unless ``allow_datetime_coercions`` is passed as True.
"""
assert allow_datetime_coercions or type(result) == type(expected), (
"%sdatetime types (%s, %s) don't match and "
"allow_datetime_coercions was not set.\n%s" % (
_fmt_msg(msg),
type(result),
type(expected),
_fmt_path(path),
)
)
if isinstance(result, pd.Timestamp) and isinstance(expected, pd.Timestamp):
assert_equal(
result.tz,
expected.tz,
path=path + ('.tz',),
msg=msg,
**kwargs
)
result = pd.Timestamp(result)
expected = pd.Timestamp(expected)
if compare_nat_equal and pd.isnull(result) and pd.isnull(expected):
return
assert_equal.dispatch(object, object)(
result,
expected,
path=path,
msg=msg,
**kwargs
)
@assert_equal.register(slice, slice)
def assert_slice_equal(result, expected, path=(), msg=''):
diff_start = (
('starts are not equal: %s != %s' % (result.start, result.stop))
if result.start != expected.start else
''
)
diff_stop = (
('stops are not equal: %s != %s' % (result.stop, result.stop))
if result.stop != expected.stop else
''
)
diff_step = (
('steps are not equal: %s != %s' % (result.step, result.stop))
if result.step != expected.step else
''
)
diffs = diff_start, diff_stop, diff_step
assert not any(diffs), '%s%s\n%s' % (
_fmt_msg(msg),
'\n'.join(filter(None, diffs)),
_fmt_path(path),
)
@assert_equal.register(Asset, Asset)
def assert_asset_equal(result, expected, path=(), msg='', **kwargs):
if type(result) is not type(expected):
raise AssertionError(
'%sresult type differs from expected type: %s is not %s\n%s',
_fmt_msg(msg),
type(result).__name__,
type(expected).__name__,
_fmt_path(path),
)
assert_equal(
result.to_dict(),
expected.to_dict(),
path=path + ('.to_dict()',),
msg=msg,
**kwargs
)
def assert_isidentical(result, expected, msg=''):
assert result.isidentical(expected), (
'%s%s is not identical to %s' % (_fmt_msg(msg), result, expected)
)
def assert_messages_equal(result, expected, msg=''):
"""Assertion helper for comparing very long strings (e.g. error messages).
"""
# The arg here is "keepends" which keeps trailing newlines (which
# matters for checking trailing whitespace). You can't pass keepends by
# name :(.
left_lines = result.splitlines(True)
right_lines = expected.splitlines(True)
iter_lines = enumerate(zip_longest(left_lines, right_lines))
for line, (ll, rl) in iter_lines:
if ll != rl:
col = index_of_first_difference(ll, rl)
raise AssertionError(
"{msg}Messages differ on line {line}, col {col}:"
"\n{ll!r}\n!=\n{rl!r}".format(
msg=_fmt_msg(msg), line=line, col=col, ll=ll, rl=rl
)
)
def index_of_first_difference(left, right):
"""Get the index of the first difference between two strings."""
difflocs = (i for (i, (lc, rc)) in enumerate(zip_longest(left, right))
if lc != rc)
try:
return next(difflocs)
except StopIteration:
raise ValueError("Left was equal to right!")
try:
# pull the dshape cases in
from datashape.util.testing import assert_dshape_equal
except ImportError:
pass
else:
assert_equal.funcs.update(
dissoc(assert_dshape_equal.funcs, (object, object)),
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/testing/predicates.py | predicates.py |
import os
import sqlite3
from unittest import TestCase
import warnings
from logbook import NullHandler, Logger
import numpy as np
import pandas as pd
from pandas.errors import PerformanceWarning
from six import with_metaclass, iteritems, itervalues, PY2
import responses
from toolz import flip, groupby, merge
from trading_calendars import (
get_calendar,
register_calendar_alias,
)
import h5py
import zipline
from zipline.algorithm import TradingAlgorithm
from zipline.assets import Equity, Future
from zipline.assets.continuous_futures import CHAIN_PREDICATES
from zipline.data.benchmarks import get_benchmark_returns_from_file
from zipline.data.fx import DEFAULT_FX_RATE
from zipline.finance.asset_restrictions import NoRestrictions
from zipline.utils.memoize import classlazyval
from zipline.pipeline import SimplePipelineEngine
from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.data.testing import TestingDataSet
from zipline.pipeline.domain import GENERIC, US_EQUITIES
from zipline.pipeline.loaders import USEquityPricingLoader
from zipline.pipeline.loaders.testing import make_seeded_random_loader
from zipline.protocol import BarData
from zipline.utils.compat import ExitStack
from zipline.utils.paths import ensure_directory, ensure_directory_containing
from .core import (
create_daily_bar_data,
create_minute_bar_data,
make_simple_equity_info,
tmp_asset_finder,
tmp_dir,
write_hdf5_daily_bars,
)
from .debug import debug_mro_failure
from ..data.adjustments import (
SQLiteAdjustmentReader,
SQLiteAdjustmentWriter,
)
from ..data.bcolz_daily_bars import (
BcolzDailyBarReader,
BcolzDailyBarWriter,
)
from ..data.data_portal import (
DataPortal,
DEFAULT_MINUTE_HISTORY_PREFETCH,
DEFAULT_DAILY_HISTORY_PREFETCH,
)
from ..data.fx import (
InMemoryFXRateReader,
HDF5FXRateReader,
HDF5FXRateWriter,
)
from ..data.hdf5_daily_bars import (
HDF5DailyBarReader,
HDF5DailyBarWriter,
MultiCountryDailyBarReader,
)
from ..data.minute_bars import (
BcolzMinuteBarReader,
BcolzMinuteBarWriter,
US_EQUITIES_MINUTES_PER_DAY,
FUTURES_MINUTES_PER_DAY,
)
from ..data.resample import (
minute_frame_to_session_frame,
MinuteResampleSessionBarReader
)
from ..finance.trading import SimulationParameters
from ..utils.classproperty import classproperty
from ..utils.final import FinalMeta, final
from ..utils.memoize import remember_last
zipline_dir = os.path.dirname(zipline.__file__)
class DebugMROMeta(FinalMeta):
"""Metaclass that helps debug MRO resolution errors.
"""
def __new__(mcls, name, bases, clsdict):
try:
return super(DebugMROMeta, mcls).__new__(
mcls, name, bases, clsdict
)
except TypeError as e:
if "(MRO)" in str(e):
msg = debug_mro_failure(name, bases)
raise TypeError(msg)
else:
raise
class ZiplineTestCase(with_metaclass(DebugMROMeta, TestCase)):
"""
Shared extensions to core unittest.TestCase.
Overrides the default unittest setUp/tearDown functions with versions that
use ExitStack to correctly clean up resources, even in the face of
exceptions that occur during setUp/setUpClass.
Subclasses **should not override setUp or setUpClass**!
Instead, they should implement `init_instance_fixtures` for per-test-method
resources, and `init_class_fixtures` for per-class resources.
Resources that need to be cleaned up should be registered using
either `enter_{class,instance}_context` or `add_{class,instance}_callback}.
"""
_in_setup = False
@final
@classmethod
def setUpClass(cls):
# Hold a set of all the "static" attributes on the class. These are
# things that are not populated after the class was created like
# methods or other class level attributes.
cls._static_class_attributes = set(vars(cls))
cls._class_teardown_stack = ExitStack()
try:
cls._base_init_fixtures_was_called = False
cls.init_class_fixtures()
assert cls._base_init_fixtures_was_called, (
"ZiplineTestCase.init_class_fixtures() was not called.\n"
"This probably means that you overrode init_class_fixtures"
" without calling super()."
)
except BaseException: # Clean up even on KeyboardInterrupt
cls.tearDownClass()
raise
@classmethod
def init_class_fixtures(cls):
"""
Override and implement this classmethod to register resources that
should be created and/or torn down on a per-class basis.
Subclass implementations of this should always invoke this with super()
to ensure that fixture mixins work properly.
"""
if cls._in_setup:
raise ValueError(
'Called init_class_fixtures from init_instance_fixtures.'
' Did you write super(..., self).init_class_fixtures() instead'
' of super(..., self).init_instance_fixtures()?',
)
cls._base_init_fixtures_was_called = True
@final
@classmethod
def tearDownClass(cls):
# We need to get this before it's deleted by the loop.
stack = cls._class_teardown_stack
for name in set(vars(cls)) - cls._static_class_attributes:
# Remove all of the attributes that were added after the class was
# constructed. This cleans up any large test data that is class
# scoped while still allowing subclasses to access class level
# attributes.
delattr(cls, name)
stack.close()
@final
@classmethod
def enter_class_context(cls, context_manager):
"""
Enter a context manager to be exited during the tearDownClass
"""
if cls._in_setup:
raise ValueError(
'Attempted to enter a class context in init_instance_fixtures.'
'\nDid you mean to call enter_instance_context?',
)
return cls._class_teardown_stack.enter_context(context_manager)
@final
@classmethod
def add_class_callback(cls, callback, *args, **kwargs):
"""
Register a callback to be executed during tearDownClass.
Parameters
----------
callback : callable
The callback to invoke at the end of the test suite.
"""
if cls._in_setup:
raise ValueError(
'Attempted to add a class callback in init_instance_fixtures.'
'\nDid you mean to call add_instance_callback?',
)
return cls._class_teardown_stack.callback(callback, *args, **kwargs)
@final
def setUp(self):
type(self)._in_setup = True
self._pre_setup_attrs = set(vars(self))
self._instance_teardown_stack = ExitStack()
try:
self._init_instance_fixtures_was_called = False
self.init_instance_fixtures()
assert self._init_instance_fixtures_was_called, (
"ZiplineTestCase.init_instance_fixtures() was not"
" called.\n"
"This probably means that you overrode"
" init_instance_fixtures without calling super()."
)
except BaseException: # Clean up even on KeyboardInterrupt
self.tearDown()
raise
finally:
type(self)._in_setup = False
def init_instance_fixtures(self):
self._init_instance_fixtures_was_called = True
@final
def tearDown(self):
# We need to get this before it's deleted by the loop.
stack = self._instance_teardown_stack
for attr in set(vars(self)) - self._pre_setup_attrs:
delattr(self, attr)
stack.close()
@final
def enter_instance_context(self, context_manager):
"""
Enter a context manager that should be exited during tearDown.
"""
return self._instance_teardown_stack.enter_context(context_manager)
@final
def add_instance_callback(self, callback):
"""
Register a callback to be executed during tearDown.
Parameters
----------
callback : callable
The callback to invoke at the end of each test.
"""
return self._instance_teardown_stack.callback(callback)
if PY2:
def assertRaisesRegex(self, *args, **kwargs):
return self.assertRaisesRegexp(*args, **kwargs)
def alias(attr_name):
"""Make a fixture attribute an alias of another fixture's attribute by
default.
Parameters
----------
attr_name : str
The name of the attribute to alias.
Returns
-------
p : classproperty
A class property that does the property aliasing.
Examples
--------
>>> class C(object):
... attr = 1
...
>>> class D(C):
... attr_alias = alias('attr')
...
>>> D.attr
1
>>> D.attr_alias
1
>>> class E(D):
... attr_alias = 2
...
>>> E.attr
1
>>> E.attr_alias
2
"""
return classproperty(flip(getattr, attr_name))
class WithDefaultDateBounds(with_metaclass(DebugMROMeta, object)):
"""
ZiplineTestCase mixin which makes it possible to synchronize date bounds
across fixtures.
This fixture should always be the last fixture in bases of any fixture or
test case that uses it.
Attributes
----------
START_DATE : datetime
END_DATE : datetime
The date bounds to be used for fixtures that want to have consistent
dates.
"""
START_DATE = pd.Timestamp('2006-01-03', tz='utc')
END_DATE = pd.Timestamp('2006-12-29', tz='utc')
class WithLogger(object):
"""
ZiplineTestCase mixin providing cls.log_handler as an instance-level
fixture.
After init_instance_fixtures has been called `self.log_handler` will be a
new ``logbook.NullHandler``.
Methods
-------
make_log_handler() -> logbook.LogHandler
A class method which constructs the new log handler object. By default
this will construct a ``NullHandler``.
"""
make_log_handler = NullHandler
@classmethod
def init_class_fixtures(cls):
super(WithLogger, cls).init_class_fixtures()
cls.log = Logger()
cls.log_handler = cls.enter_class_context(
cls.make_log_handler().applicationbound(),
)
class WithAssetFinder(WithDefaultDateBounds):
"""
ZiplineTestCase mixin providing cls.asset_finder as a class-level fixture.
After init_class_fixtures has been called, `cls.asset_finder` is populated
with an AssetFinder.
Attributes
----------
ASSET_FINDER_EQUITY_SIDS : iterable[int]
The default sids to construct equity data for.
ASSET_FINDER_EQUITY_SYMBOLS : iterable[str]
The default symbols to use for the equities.
ASSET_FINDER_EQUITY_START_DATE : datetime
The default start date to create equity data for. This defaults to
``START_DATE``.
ASSET_FINDER_EQUITY_END_DATE : datetime
The default end date to create equity data for. This defaults to
``END_DATE``.
ASSET_FINDER_EQUITY_NAMES: iterable[str]
The default names to use for the equities.
ASSET_FINDER_EQUITY_EXCHANGE : str
The default exchange to assign each equity.
ASSET_FINDER_COUNTRY_CODE : str
The default country code to assign each exchange.
Methods
-------
make_equity_info() -> pd.DataFrame
A class method which constructs the dataframe of equity info to write
to the class's asset db. By default this is empty.
make_futures_info() -> pd.DataFrame
A class method which constructs the dataframe of futures contract info
to write to the class's asset db. By default this is empty.
make_exchanges_info() -> pd.DataFrame
A class method which constructs the dataframe of exchange information
to write to the class's assets db. By default this is empty.
make_root_symbols_info() -> pd.DataFrame
A class method which constructs the dataframe of root symbols
information to write to the class's assets db. By default this is
empty.
make_asset_finder_db_url() -> string
A class method which returns the URL at which to create the SQLAlchemy
engine. By default provides a URL for an in-memory database.
make_asset_finder() -> pd.DataFrame
A class method which constructs the actual asset finder object to use
for the class. If this method is overridden then the ``make_*_info``
methods may not be respected.
See Also
--------
zipline.testing.make_simple_equity_info
zipline.testing.make_jagged_equity_info
zipline.testing.make_rotating_equity_info
zipline.testing.make_future_info
zipline.testing.make_commodity_future_info
"""
ASSET_FINDER_EQUITY_SIDS = ord('A'), ord('B'), ord('C')
ASSET_FINDER_EQUITY_SYMBOLS = None
ASSET_FINDER_EQUITY_NAMES = None
ASSET_FINDER_EQUITY_EXCHANGE = 'TEST'
ASSET_FINDER_EQUITY_START_DATE = alias('START_DATE')
ASSET_FINDER_EQUITY_END_DATE = alias('END_DATE')
ASSET_FINDER_FUTURE_CHAIN_PREDICATES = CHAIN_PREDICATES
ASSET_FINDER_COUNTRY_CODE = '??'
@classmethod
def _make_info(cls, *args):
return None
make_futures_info = _make_info
make_exchanges_info = _make_info
make_root_symbols_info = _make_info
make_equity_supplementary_mappings = _make_info
del _make_info
@classmethod
def make_equity_info(cls):
return make_simple_equity_info(
cls.ASSET_FINDER_EQUITY_SIDS,
cls.ASSET_FINDER_EQUITY_START_DATE,
cls.ASSET_FINDER_EQUITY_END_DATE,
cls.ASSET_FINDER_EQUITY_SYMBOLS,
cls.ASSET_FINDER_EQUITY_NAMES,
cls.ASSET_FINDER_EQUITY_EXCHANGE,
)
@classmethod
def make_asset_finder_db_url(cls):
return 'sqlite:///:memory:'
@classmethod
def make_asset_finder(cls):
"""Returns a new AssetFinder
Returns
-------
asset_finder : zipline.assets.AssetFinder
"""
equities = cls.make_equity_info()
futures = cls.make_futures_info()
root_symbols = cls.make_root_symbols_info()
exchanges = cls.make_exchanges_info(equities, futures, root_symbols)
if exchanges is None:
exchange_names = [
df['exchange']
for df in (equities, futures, root_symbols)
if df is not None
]
if exchange_names:
exchanges = pd.DataFrame({
'exchange': pd.concat(exchange_names).unique(),
'country_code': cls.ASSET_FINDER_COUNTRY_CODE,
})
return cls.enter_class_context(tmp_asset_finder(
url=cls.make_asset_finder_db_url(),
equities=equities,
futures=futures,
exchanges=exchanges,
root_symbols=root_symbols,
equity_supplementary_mappings=(
cls.make_equity_supplementary_mappings()
),
future_chain_predicates=cls.ASSET_FINDER_FUTURE_CHAIN_PREDICATES,
))
@classmethod
def init_class_fixtures(cls):
super(WithAssetFinder, cls).init_class_fixtures()
cls.asset_finder = cls.make_asset_finder()
@classlazyval
def all_assets(cls):
"""A list of Assets for all sids in cls.asset_finder.
"""
return cls.asset_finder.retrieve_all(cls.asset_finder.sids)
@classlazyval
def exchange_names(cls):
"""A list of canonical exchange names for all exchanges in this suite.
"""
infos = itervalues(cls.asset_finder.exchange_info)
return sorted(i.canonical_name for i in infos)
@classlazyval
def assets_by_calendar(cls):
"""A dict from calendar -> list of assets with that calendar.
"""
return groupby(lambda a: get_calendar(a.exchange), cls.all_assets)
@classlazyval
def all_calendars(cls):
"""A list of all calendars for assets in this test suite.
"""
return list(cls.assets_by_calendar)
# TODO_SS: The API here doesn't make sense in a multi-country test scenario.
class WithTradingCalendars(object):
"""
ZiplineTestCase mixin providing cls.trading_calendar,
cls.all_trading_calendars, cls.trading_calendar_for_asset_type as a
class-level fixture.
After ``init_class_fixtures`` has been called:
- `cls.trading_calendar` is populated with a default of the nyse trading
calendar for compatibility with existing tests
- `cls.all_trading_calendars` is populated with the trading calendars
keyed by name,
- `cls.trading_calendar_for_asset_type` is populated with the trading
calendars keyed by the asset type which uses the respective calendar.
Attributes
----------
TRADING_CALENDAR_STRS : iterable
iterable of identifiers of the calendars to use.
TRADING_CALENDAR_FOR_ASSET_TYPE : dict
A dictionary which maps asset type names to the calendar associated
with that asset type.
"""
TRADING_CALENDAR_STRS = ('NYSE',)
TRADING_CALENDAR_FOR_ASSET_TYPE = {Equity: 'NYSE', Future: 'us_futures'}
# For backwards compatibility, exisitng tests and fixtures refer to
# `trading_calendar` with the assumption that the value is the NYSE
# calendar.
TRADING_CALENDAR_PRIMARY_CAL = 'NYSE'
@classmethod
def init_class_fixtures(cls):
super(WithTradingCalendars, cls).init_class_fixtures()
cls.trading_calendars = {}
# Silence `pandas.errors.PerformanceWarning: Non-vectorized DateOffset
# being applied to Series or DatetimeIndex` in trading calendar
# construction. This causes nosetest to fail.
with warnings.catch_warnings():
warnings.simplefilter("ignore", PerformanceWarning)
for cal_str in (
set(cls.TRADING_CALENDAR_STRS) |
{cls.TRADING_CALENDAR_PRIMARY_CAL}
):
# Set name to allow aliasing.
calendar = get_calendar(cal_str)
setattr(cls,
'{0}_calendar'.format(cal_str.lower()), calendar)
cls.trading_calendars[cal_str] = calendar
type_to_cal = iteritems(cls.TRADING_CALENDAR_FOR_ASSET_TYPE)
for asset_type, cal_str in type_to_cal:
calendar = get_calendar(cal_str)
cls.trading_calendars[asset_type] = calendar
cls.trading_calendar = (
cls.trading_calendars[cls.TRADING_CALENDAR_PRIMARY_CAL]
)
STATIC_BENCHMARK_PATH = os.path.join(
zipline_dir,
'resources',
'market_data',
'SPY_benchmark.csv',
)
@remember_last
def read_checked_in_benchmark_data():
return get_benchmark_returns_from_file(STATIC_BENCHMARK_PATH)
class WithBenchmarkReturns(WithDefaultDateBounds,
WithTradingCalendars):
"""
ZiplineTestCase mixin providing cls.benchmark_returns as a class-level
attribute.
"""
_default_treasury_curves = None
@classproperty
def BENCHMARK_RETURNS(cls):
benchmark_returns = read_checked_in_benchmark_data()
# Zipline ordinarily uses cached benchmark returns data, but when
# running the zipline tests this cache is not always updated to include
# the appropriate dates required by both the futures and equity
# calendars. In order to create more reliable and consistent data
# throughout the entirety of the tests, we read static benchmark
# returns files from source. If a test using this fixture attempts to
# run outside of the static date range of the csv files, raise an
# exception warning the user to either update the csv files in source
# or to use a date range within the current bounds.
static_start_date = benchmark_returns.index[0].date()
static_end_date = benchmark_returns.index[-1].date()
warning_message = (
'The WithBenchmarkReturns fixture uses static data between '
'{static_start} and {static_end}. To use a start and end date '
'of {given_start} and {given_end} you will have to update the '
'file in {benchmark_path} to include the missing dates.'.format(
static_start=static_start_date,
static_end=static_end_date,
given_start=cls.START_DATE.date(),
given_end=cls.END_DATE.date(),
benchmark_path=STATIC_BENCHMARK_PATH,
)
)
if cls.START_DATE.date() < static_start_date or \
cls.END_DATE.date() > static_end_date:
raise AssertionError(warning_message)
return benchmark_returns
class WithSimParams(WithDefaultDateBounds):
"""
ZiplineTestCase mixin providing cls.sim_params as a class level fixture.
Attributes
----------
SIM_PARAMS_CAPITAL_BASE : float
SIM_PARAMS_DATA_FREQUENCY : {'daily', 'minute'}
SIM_PARAMS_EMISSION_RATE : {'daily', 'minute'}
Forwarded to ``SimulationParameters``.
SIM_PARAMS_START : datetime
SIM_PARAMS_END : datetime
Forwarded to ``SimulationParameters``. If not
explicitly overridden these will be ``START_DATE`` and ``END_DATE``
Methods
-------
make_simparams(**overrides)
Construct a ``SimulationParameters`` using the defaults defined by
fixture configuration attributes. Any parameters to
``SimulationParameters`` can be overridden by passing them by keyword.
See Also
--------
zipline.finance.trading.SimulationParameters
"""
SIM_PARAMS_CAPITAL_BASE = 1.0e5
SIM_PARAMS_DATA_FREQUENCY = 'daily'
SIM_PARAMS_EMISSION_RATE = 'daily'
SIM_PARAMS_START = alias('START_DATE')
SIM_PARAMS_END = alias('END_DATE')
@classmethod
def make_simparams(cls, **overrides):
kwargs = dict(
start_session=cls.SIM_PARAMS_START,
end_session=cls.SIM_PARAMS_END,
capital_base=cls.SIM_PARAMS_CAPITAL_BASE,
data_frequency=cls.SIM_PARAMS_DATA_FREQUENCY,
emission_rate=cls.SIM_PARAMS_EMISSION_RATE,
trading_calendar=cls.trading_calendar,
)
kwargs.update(overrides)
return SimulationParameters(**kwargs)
@classmethod
def init_class_fixtures(cls):
super(WithSimParams, cls).init_class_fixtures()
cls.sim_params = cls.make_simparams()
class WithTradingSessions(WithDefaultDateBounds, WithTradingCalendars):
"""
ZiplineTestCase mixin providing cls.trading_days, cls.all_trading_sessions
as a class-level fixture.
After init_class_fixtures has been called, `cls.all_trading_sessions`
is populated with a dictionary of calendar name to the DatetimeIndex
containing the calendar trading days ranging from:
(DATA_MAX_DAY - (cls.TRADING_DAY_COUNT) -> DATA_MAX_DAY)
`cls.trading_days`, for compatibility with existing tests which make the
assumption that trading days are equity only, defaults to the nyse trading
sessions.
Attributes
----------
DATA_MAX_DAY : datetime
The most recent trading day in the calendar.
TRADING_DAY_COUNT : int
The number of days to put in the calendar. The default value of
``TRADING_DAY_COUNT`` is 126 (half a trading-year). Inheritors can
override TRADING_DAY_COUNT to request more or less data.
"""
DATA_MIN_DAY = alias('START_DATE')
DATA_MAX_DAY = alias('END_DATE')
# For backwards compatibility, exisitng tests and fixtures refer to
# `trading_days` with the assumption that the value is days of the NYSE
# calendar.
trading_days = alias('nyse_sessions')
@classmethod
def init_class_fixtures(cls):
super(WithTradingSessions, cls).init_class_fixtures()
cls.trading_sessions = {}
for cal_str in cls.TRADING_CALENDAR_STRS:
trading_calendar = cls.trading_calendars[cal_str]
start_date = cls.DATA_MIN_DAY
end_date = cls.DATA_MAX_DAY
if not start_date.tzinfo:
start_date = start_date.tz_localize('utc')
if not end_date.tzinfo:
end_date = end_date.tz_localize('utc')
sessions = trading_calendar.sessions_in_range(
start_date, end_date)
# Set name for aliasing.
setattr(cls,
'{0}_sessions'.format(cal_str.lower()), sessions)
cls.trading_sessions[cal_str] = sessions
class WithTmpDir(object):
"""
ZiplineTestCase mixing providing cls.tmpdir as a class-level fixture.
After init_class_fixtures has been called, `cls.tmpdir` is populated with
a `testfixtures.TempDirectory` object whose path is `cls.TMP_DIR_PATH`.
Attributes
----------
TMP_DIR_PATH : str
The path to the new directory to create. By default this is None
which will create a unique directory in /tmp.
"""
TMP_DIR_PATH = None
@classmethod
def init_class_fixtures(cls):
super(WithTmpDir, cls).init_class_fixtures()
cls.tmpdir = cls.enter_class_context(
tmp_dir(path=cls.TMP_DIR_PATH),
)
class WithInstanceTmpDir(object):
"""
ZiplineTestCase mixing providing self.tmpdir as an instance-level fixture.
After init_instance_fixtures has been called, `self.tmpdir` is populated
with a `testfixtures.TempDirectory` object whose path is
`cls.TMP_DIR_PATH`.
Attributes
----------
INSTANCE_TMP_DIR_PATH : str
The path to the new directory to create. By default this is None
which will create a unique directory in /tmp.
"""
INSTANCE_TMP_DIR_PATH = None
def init_instance_fixtures(self):
super(WithInstanceTmpDir, self).init_instance_fixtures()
self.instance_tmpdir = self.enter_instance_context(
tmp_dir(path=self.INSTANCE_TMP_DIR_PATH),
)
class WithEquityDailyBarData(WithAssetFinder, WithTradingCalendars):
"""
ZiplineTestCase mixin providing cls.make_equity_daily_bar_data.
Attributes
----------
EQUITY_DAILY_BAR_START_DATE : Timestamp
The date at to which to start creating data. This defaults to
``START_DATE``.
EQUITY_DAILY_BAR_END_DATE = Timestamp
The end date up to which to create data. This defaults to ``END_DATE``.
EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE : bool
If this flag is set, `make_equity_daily_bar_data` will read data from
the minute bars defined by `WithEquityMinuteBarData`.
The current default is `False`, but could be `True` in the future.
EQUITY_DAILY_BAR_COUNTRY_CODES : tuple
The countres to create data for. By default this is populated
with all of the countries present in the asset finder.
Methods
-------
make_equity_daily_bar_data(country_code, sids)
make_equity_daily_bar_currency_codes(country_code, sids)
See Also
--------
WithEquityMinuteBarData
zipline.testing.create_daily_bar_data
""" # noqa
EQUITY_DAILY_BAR_START_DATE = alias('START_DATE')
EQUITY_DAILY_BAR_END_DATE = alias('END_DATE')
EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE = None
@classproperty
def EQUITY_DAILY_BAR_LOOKBACK_DAYS(cls):
# If we're sourcing from minute data, then we almost certainly want the
# minute bar calendar to be aligned with the daily bar calendar, so
# re-use the same lookback parameter.
if cls.EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE:
return cls.EQUITY_MINUTE_BAR_LOOKBACK_DAYS
else:
return 0
@classproperty
def EQUITY_DAILY_BAR_COUNTRY_CODES(cls):
return cls.asset_finder.country_codes
@classmethod
def _make_equity_daily_bar_from_minute(cls):
assert issubclass(cls, WithEquityMinuteBarData), \
"Can't source daily data from minute without minute data!"
assets = cls.asset_finder.retrieve_all(cls.asset_finder.equities_sids)
minute_data = dict(cls.make_equity_minute_bar_data())
for asset in assets:
yield asset.sid, minute_frame_to_session_frame(
minute_data[asset.sid],
cls.trading_calendars[Equity])
@classmethod
def make_equity_daily_bar_data(cls, country_code, sids):
"""
Create daily pricing data.
Parameters
----------
country_code : str
An ISO 3166 alpha-2 country code. Data should be created for
this country.
sids : tuple[int]
The sids to include in the data.
Yields
------
(int, pd.DataFrame)
A sid, dataframe pair to be passed to a daily bar writer.
The dataframe should be indexed by date, with columns of
('open', 'high', 'low', 'close', 'volume', 'day', & 'id').
"""
# Requires a WithEquityMinuteBarData to come before in the MRO.
# Resample that data so that daily and minute bar data are aligned.
if cls.EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE:
return cls._make_equity_daily_bar_from_minute()
else:
return create_daily_bar_data(cls.equity_daily_bar_days, sids)
@classmethod
def make_equity_daily_bar_currency_codes(cls, country_code, sids):
"""Create listing currencies.
Default is to list all assets in USD.
Parameters
----------
country_code : str
An ISO 3166 alpha-2 country code. Data should be created for
this country.
sids : tuple[int]
The sids to include in the data.
Returns
-------
currency_codes : pd.Series[int, str]
Map from sids to currency for that sid's prices.
"""
return pd.Series(index=list(sids), data='USD')
@classmethod
def init_class_fixtures(cls):
super(WithEquityDailyBarData, cls).init_class_fixtures()
trading_calendar = cls.trading_calendars[Equity]
if trading_calendar.is_session(cls.EQUITY_DAILY_BAR_START_DATE):
first_session = cls.EQUITY_DAILY_BAR_START_DATE
else:
first_session = trading_calendar.minute_to_session_label(
pd.Timestamp(cls.EQUITY_DAILY_BAR_START_DATE)
)
if cls.EQUITY_DAILY_BAR_LOOKBACK_DAYS > 0:
first_session = trading_calendar.sessions_window(
first_session,
-1 * cls.EQUITY_DAILY_BAR_LOOKBACK_DAYS
)[0]
days = trading_calendar.sessions_in_range(
first_session,
cls.EQUITY_DAILY_BAR_END_DATE,
)
cls.equity_daily_bar_days = days
class WithFutureDailyBarData(WithAssetFinder, WithTradingCalendars):
"""
ZiplineTestCase mixin providing cls.make_future_daily_bar_data.
Attributes
----------
FUTURE_DAILY_BAR_START_DATE : Timestamp
The date at to which to start creating data. This defaults to
``START_DATE``.
FUTURE_DAILY_BAR_END_DATE = Timestamp
The end date up to which to create data. This defaults to ``END_DATE``.
FUTURE_DAILY_BAR_SOURCE_FROM_MINUTE : bool
If this flag is set, `make_future_daily_bar_data` will read data from
the minute bars defined by `WithFutureMinuteBarData`.
The current default is `False`, but could be `True` in the future.
Methods
-------
make_future_daily_bar_data() -> iterable[(int, pd.DataFrame)]
A class method that returns an iterator of (sid, dataframe) pairs
which will be written to the bcolz files that the class's
``BcolzDailyBarReader`` will read from. By default this creates
some simple synthetic data with
:func:`~zipline.testing.create_daily_bar_data`
See Also
--------
WithFutureMinuteBarData
zipline.testing.create_daily_bar_data
"""
FUTURE_DAILY_BAR_USE_FULL_CALENDAR = False
FUTURE_DAILY_BAR_START_DATE = alias('START_DATE')
FUTURE_DAILY_BAR_END_DATE = alias('END_DATE')
FUTURE_DAILY_BAR_SOURCE_FROM_MINUTE = None
@classproperty
def FUTURE_DAILY_BAR_LOOKBACK_DAYS(cls):
# If we're sourcing from minute data, then we almost certainly want the
# minute bar calendar to be aligned with the daily bar calendar, so
# re-use the same lookback parameter.
if cls.FUTURE_DAILY_BAR_SOURCE_FROM_MINUTE:
return cls.FUTURE_MINUTE_BAR_LOOKBACK_DAYS
else:
return 0
@classmethod
def _make_future_daily_bar_from_minute(cls):
assert issubclass(cls, WithFutureMinuteBarData), \
"Can't source daily data from minute without minute data!"
assets = cls.asset_finder.retrieve_all(cls.asset_finder.futures_sids)
minute_data = dict(cls.make_future_minute_bar_data())
for asset in assets:
yield asset.sid, minute_frame_to_session_frame(
minute_data[asset.sid],
cls.trading_calendars[Future])
@classmethod
def make_future_daily_bar_data(cls):
# Requires a WithFutureMinuteBarData to come before in the MRO.
# Resample that data so that daily and minute bar data are aligned.
if cls.FUTURE_DAILY_BAR_SOURCE_FROM_MINUTE:
return cls._make_future_daily_bar_from_minute()
else:
return create_daily_bar_data(
cls.future_daily_bar_days,
cls.asset_finder.futures_sids,
)
@classmethod
def init_class_fixtures(cls):
super(WithFutureDailyBarData, cls).init_class_fixtures()
trading_calendar = cls.trading_calendars[Future]
if cls.FUTURE_DAILY_BAR_USE_FULL_CALENDAR:
days = trading_calendar.all_sessions
else:
if trading_calendar.is_session(cls.FUTURE_DAILY_BAR_START_DATE):
first_session = cls.FUTURE_DAILY_BAR_START_DATE
else:
first_session = trading_calendar.minute_to_session_label(
pd.Timestamp(cls.FUTURE_DAILY_BAR_START_DATE)
)
if cls.FUTURE_DAILY_BAR_LOOKBACK_DAYS > 0:
first_session = trading_calendar.sessions_window(
first_session,
-1 * cls.FUTURE_DAILY_BAR_LOOKBACK_DAYS
)[0]
days = trading_calendar.sessions_in_range(
first_session,
cls.FUTURE_DAILY_BAR_END_DATE,
)
cls.future_daily_bar_days = days
class WithBcolzEquityDailyBarReader(WithEquityDailyBarData, WithTmpDir):
"""
ZiplineTestCase mixin providing cls.bcolz_daily_bar_path,
cls.bcolz_daily_bar_ctable, and cls.bcolz_equity_daily_bar_reader
class level fixtures.
After init_class_fixtures has been called:
- `cls.bcolz_daily_bar_path` is populated with
`cls.tmpdir.getpath(cls.BCOLZ_DAILY_BAR_PATH)`.
- `cls.bcolz_daily_bar_ctable` is populated with data returned from
`cls.make_equity_daily_bar_data`. By default this calls
:func:`zipline.pipeline.loaders.synthetic.make_equity_daily_bar_data`.
- `cls.bcolz_equity_daily_bar_reader` is a daily bar reader
pointing to the directory that was just written to.
Attributes
----------
BCOLZ_DAILY_BAR_PATH : str
The path inside the tmpdir where this will be written.
EQUITY_DAILY_BAR_LOOKBACK_DAYS : int
The number of days of data to add before the first day. This is used
when a test needs to use history, in which case this should be set to
the largest history window that will be
requested.
EQUITY_DAILY_BAR_USE_FULL_CALENDAR : bool
If this flag is set the ``equity_daily_bar_days`` will be the full
set of trading days from the trading environment. This flag overrides
``EQUITY_DAILY_BAR_LOOKBACK_DAYS``.
BCOLZ_DAILY_BAR_READ_ALL_THRESHOLD : int
If this flag is set, use the value as the `read_all_threshold`
parameter to BcolzDailyBarReader, otherwise use the default
value.
EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE : bool
If this flag is set, `make_equity_daily_bar_data` will read data from
the minute bar reader defined by a `WithBcolzEquityMinuteBarReader`.
Methods
-------
make_bcolz_daily_bar_rootdir_path() -> string
A class method that returns the path for the rootdir of the daily
bars ctable. By default this is a subdirectory BCOLZ_DAILY_BAR_PATH in
the shared temp directory.
See Also
--------
WithBcolzEquityMinuteBarReader
WithDataPortal
zipline.testing.create_daily_bar_data
"""
BCOLZ_DAILY_BAR_PATH = 'daily_equity_pricing.bcolz'
BCOLZ_DAILY_BAR_READ_ALL_THRESHOLD = None
BCOLZ_DAILY_BAR_COUNTRY_CODE = None
EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE = False
# allows WithBcolzEquityDailyBarReaderFromCSVs to call the
# `write_csvs`method without needing to reimplement `init_class_fixtures`
_write_method_name = 'write'
# What to do when data being written is invalid, e.g. nan, inf, etc.
# options are: 'warn', 'raise', 'ignore'
INVALID_DATA_BEHAVIOR = 'warn'
@classproperty
def BCOLZ_DAILY_BAR_COUNTRY_CODE(cls):
return cls.EQUITY_DAILY_BAR_COUNTRY_CODES[0]
@classmethod
def make_bcolz_daily_bar_rootdir_path(cls):
return cls.tmpdir.makedir(cls.BCOLZ_DAILY_BAR_PATH)
@classmethod
def init_class_fixtures(cls):
super(WithBcolzEquityDailyBarReader, cls).init_class_fixtures()
cls.bcolz_daily_bar_path = p = cls.make_bcolz_daily_bar_rootdir_path()
days = cls.equity_daily_bar_days
sids = cls.asset_finder.equities_sids_for_country_code(
cls.BCOLZ_DAILY_BAR_COUNTRY_CODE
)
trading_calendar = cls.trading_calendars[Equity]
cls.bcolz_daily_bar_ctable = t = getattr(
BcolzDailyBarWriter(p, trading_calendar, days[0], days[-1]),
cls._write_method_name,
)(
cls.make_equity_daily_bar_data(
country_code=cls.BCOLZ_DAILY_BAR_COUNTRY_CODE,
sids=sids,
),
invalid_data_behavior=cls.INVALID_DATA_BEHAVIOR
)
if cls.BCOLZ_DAILY_BAR_READ_ALL_THRESHOLD is not None:
cls.bcolz_equity_daily_bar_reader = BcolzDailyBarReader(
t, cls.BCOLZ_DAILY_BAR_READ_ALL_THRESHOLD)
else:
cls.bcolz_equity_daily_bar_reader = BcolzDailyBarReader(t)
class WithBcolzFutureDailyBarReader(WithFutureDailyBarData, WithTmpDir):
"""
ZiplineTestCase mixin providing cls.bcolz_daily_bar_path,
cls.bcolz_daily_bar_ctable, and cls.bcolz_future_daily_bar_reader
class level fixtures.
After init_class_fixtures has been called:
- `cls.bcolz_daily_bar_path` is populated with
`cls.tmpdir.getpath(cls.BCOLZ_DAILY_BAR_PATH)`.
- `cls.bcolz_daily_bar_ctable` is populated with data returned from
`cls.make_future_daily_bar_data`. By default this calls
:func:`zipline.pipeline.loaders.synthetic.make_future_daily_bar_data`.
- `cls.bcolz_future_daily_bar_reader` is a daily bar reader
pointing to the directory that was just written to.
Attributes
----------
BCOLZ_DAILY_BAR_PATH : str
The path inside the tmpdir where this will be written.
FUTURE_DAILY_BAR_LOOKBACK_DAYS : int
The number of days of data to add before the first day. This is used
when a test needs to use history, in which case this should be set to
the largest history window that will be
requested.
FUTURE_DAILY_BAR_USE_FULL_CALENDAR : bool
If this flag is set the ``future_daily_bar_days`` will be the full
set of trading days from the trading environment. This flag overrides
``FUTURE_DAILY_BAR_LOOKBACK_DAYS``.
BCOLZ_FUTURE_DAILY_BAR_READ_ALL_THRESHOLD : int
If this flag is set, use the value as the `read_all_threshold`
parameter to BcolzDailyBarReader, otherwise use the default
value.
FUTURE_DAILY_BAR_SOURCE_FROM_MINUTE : bool
If this flag is set, `make_future_daily_bar_data` will read data from
the minute bar reader defined by a `WithBcolzFutureMinuteBarReader`.
Methods
-------
make_bcolz_daily_bar_rootdir_path() -> string
A class method that returns the path for the rootdir of the daily
bars ctable. By default this is a subdirectory BCOLZ_DAILY_BAR_PATH in
the shared temp directory.
See Also
--------
WithBcolzFutureMinuteBarReader
WithDataPortal
zipline.testing.create_daily_bar_data
"""
BCOLZ_FUTURE_DAILY_BAR_PATH = 'daily_future_pricing.bcolz'
BCOLZ_FUTURE_DAILY_BAR_READ_ALL_THRESHOLD = None
FUTURE_DAILY_BAR_SOURCE_FROM_MINUTE = False
# What to do when data being written is invalid, e.g. nan, inf, etc.
# options are: 'warn', 'raise', 'ignore'
BCOLZ_FUTURE_DAILY_BAR_INVALID_DATA_BEHAVIOR = 'warn'
BCOLZ_FUTURE_DAILY_BAR_WRITE_METHOD_NAME = 'write'
@classmethod
def make_bcolz_future_daily_bar_rootdir_path(cls):
return cls.tmpdir.makedir(cls.BCOLZ_FUTURE_DAILY_BAR_PATH)
@classmethod
def init_class_fixtures(cls):
super(WithBcolzFutureDailyBarReader, cls).init_class_fixtures()
p = cls.make_bcolz_future_daily_bar_rootdir_path()
cls.future_bcolz_daily_bar_path = p
days = cls.future_daily_bar_days
trading_calendar = cls.trading_calendars[Future]
cls.future_bcolz_daily_bar_ctable = t = getattr(
BcolzDailyBarWriter(p, trading_calendar, days[0], days[-1]),
cls.BCOLZ_FUTURE_DAILY_BAR_WRITE_METHOD_NAME,
)(
cls.make_future_daily_bar_data(),
invalid_data_behavior=(
cls.BCOLZ_FUTURE_DAILY_BAR_INVALID_DATA_BEHAVIOR
)
)
if cls.BCOLZ_FUTURE_DAILY_BAR_READ_ALL_THRESHOLD is not None:
cls.bcolz_future_daily_bar_reader = BcolzDailyBarReader(
t, cls.BCOLZ_FUTURE_DAILY_BAR_READ_ALL_THRESHOLD)
else:
cls.bcolz_future_daily_bar_reader = BcolzDailyBarReader(t)
class WithBcolzEquityDailyBarReaderFromCSVs(WithBcolzEquityDailyBarReader):
"""
ZiplineTestCase mixin that provides
cls.bcolz_equity_daily_bar_reader from a mapping of sids to CSV
file paths.
"""
_write_method_name = 'write_csvs'
def _trading_days_for_minute_bars(calendar,
start_date,
end_date,
lookback_days):
first_session = calendar.minute_to_session_label(start_date)
if lookback_days > 0:
first_session = calendar.sessions_window(
first_session,
-1 * lookback_days
)[0]
if not first_session.tzinfo:
first_session = fist_session.tz_localize('utc')
if not end_date.tzinfo:
end_date = end_date.tz_localize('utc')
return calendar.sessions_in_range(first_session, end_date)
# TODO_SS: This currently doesn't define any relationship between country_code
# and calendar, which would be useful downstream.
class WithWriteHDF5DailyBars(WithEquityDailyBarData,
WithTmpDir):
"""
Fixture class defining the capability of writing HDF5 daily bars to disk.
Uses cls.make_equity_daily_bar_data (inherited from WithEquityDailyBarData)
to determine the data to write.
Methods
-------
write_hdf5_daily_bars(cls, path, country_codes)
Creates an HDF5 file on disk and populates it with pricing data.
Attributes
----------
HDF5_DAILY_BAR_CHUNK_SIZE
"""
HDF5_DAILY_BAR_CHUNK_SIZE = 30
@classmethod
def write_hdf5_daily_bars(cls, path, country_codes):
"""
Write HDF5 pricing data using an HDF5DailyBarWriter.
Parameters
----------
path : str
Location (relative to cls.tmpdir) at which to write data.
country_codes : list[str]
List of country codes to write.
Returns
-------
written : h5py.File
A read-only h5py.File pointing at the written data. The returned
file is registered to be closed automatically during class
teardown.
"""
ensure_directory_containing(path)
writer = HDF5DailyBarWriter(path, cls.HDF5_DAILY_BAR_CHUNK_SIZE)
write_hdf5_daily_bars(
writer,
cls.asset_finder,
country_codes,
cls.make_equity_daily_bar_data,
cls.make_equity_daily_bar_currency_codes,
)
# Open the file and mark it for closure during teardown.
return cls.enter_class_context(writer.h5_file(mode='r'))
class WithHDF5EquityMultiCountryDailyBarReader(WithWriteHDF5DailyBars):
"""
Fixture providing cls.hdf5_daily_bar_path and
cls.hdf5_equity_daily_bar_reader class level fixtures.
After init_class_fixtures has been called:
- `cls.hdf5_daily_bar_path` is populated with
`cls.tmpdir.getpath(cls.HDF5_DAILY_BAR_PATH)`.
- The file at `cls.hdf5_daily_bar_path` is populated with data returned
from `cls.make_equity_daily_bar_data`. By default this calls
:func:`zipline.pipeline.loaders.synthetic.make_equity_daily_bar_data`.
- `cls.hdf5_equity_daily_bar_reader` is a daily bar reader pointing
to the file that was just written to.
Attributes
----------
HDF5_DAILY_BAR_PATH : str
The path inside the tmpdir where this will be written.
HDF5_DAILY_BAR_COUNTRY_CODE : str
The ISO 3166 alpha-2 country code for the country to write/read.
Methods
-------
make_hdf5_daily_bar_path() -> string
A class method that returns the path for the rootdir of the daily
bars ctable. By default this is a subdirectory HDF5_DAILY_BAR_PATH in
the shared temp directory.
See Also
--------
WithDataPortal
zipline.testing.create_daily_bar_data
"""
HDF5_DAILY_BAR_PATH = 'daily_equity_pricing.h5'
HDF5_DAILY_BAR_COUNTRY_CODES = alias('EQUITY_DAILY_BAR_COUNTRY_CODES')
@classmethod
def make_hdf5_daily_bar_path(cls):
return cls.tmpdir.getpath(cls.HDF5_DAILY_BAR_PATH)
@classmethod
def init_class_fixtures(cls):
super(
WithHDF5EquityMultiCountryDailyBarReader,
cls,
).init_class_fixtures()
cls.hdf5_daily_bar_path = path = cls.make_hdf5_daily_bar_path()
f = cls.write_hdf5_daily_bars(path, cls.HDF5_DAILY_BAR_COUNTRY_CODES)
cls.single_country_hdf5_equity_daily_bar_readers = {
country_code: HDF5DailyBarReader.from_file(f, country_code)
for country_code in f
}
cls.hdf5_equity_daily_bar_reader = MultiCountryDailyBarReader(
cls.single_country_hdf5_equity_daily_bar_readers
)
class WithEquityMinuteBarData(WithAssetFinder, WithTradingCalendars):
"""
ZiplineTestCase mixin providing cls.equity_minute_bar_days.
After init_class_fixtures has been called:
- `cls.equity_minute_bar_days` has the range over which data has been
generated.
Attributes
----------
EQUITY_MINUTE_BAR_LOOKBACK_DAYS : int
The number of days of data to add before the first day.
This is used when a test needs to use history, in which case this
should be set to the largest history window that will be requested.
EQUITY_MINUTE_BAR_START_DATE : Timestamp
The date at to which to start creating data. This defaults to
``START_DATE``.
EQUITY_MINUTE_BAR_END_DATE = Timestamp
The end date up to which to create data. This defaults to ``END_DATE``.
Methods
-------
make_equity_minute_bar_data() -> iterable[(int, pd.DataFrame)]
Classmethod producing an iterator of (sid, minute_data) pairs.
The default implementation invokes
zipline.testing.core.create_minute_bar_data.
See Also
--------
WithEquityDailyBarData
zipline.testing.create_minute_bar_data
"""
EQUITY_MINUTE_BAR_LOOKBACK_DAYS = 0
EQUITY_MINUTE_BAR_START_DATE = alias('START_DATE')
EQUITY_MINUTE_BAR_END_DATE = alias('END_DATE')
@classmethod
def make_equity_minute_bar_data(cls):
trading_calendar = cls.trading_calendars[Equity]
return create_minute_bar_data(
trading_calendar.minutes_for_sessions_in_range(
cls.equity_minute_bar_days[0],
cls.equity_minute_bar_days[-1],
),
cls.asset_finder.equities_sids,
)
@classmethod
def init_class_fixtures(cls):
super(WithEquityMinuteBarData, cls).init_class_fixtures()
trading_calendar = cls.trading_calendars[Equity]
cls.equity_minute_bar_days = _trading_days_for_minute_bars(
trading_calendar,
cls.EQUITY_MINUTE_BAR_START_DATE,
cls.EQUITY_MINUTE_BAR_END_DATE,
cls.EQUITY_MINUTE_BAR_LOOKBACK_DAYS
)
class WithFutureMinuteBarData(WithAssetFinder, WithTradingCalendars):
"""
ZiplineTestCase mixin providing cls.future_minute_bar_days.
After init_class_fixtures has been called:
- `cls.future_minute_bar_days` has the range over which data has been
generated.
Attributes
----------
FUTURE_MINUTE_BAR_LOOKBACK_DAYS : int
The number of days of data to add before the first day.
This is used when a test needs to use history, in which case this
should be set to the largest history window that will be requested.
FUTURE_MINUTE_BAR_START_DATE : Timestamp
The date at to which to start creating data. This defaults to
``START_DATE``.
FUTURE_MINUTE_BAR_END_DATE = Timestamp
The end date up to which to create data. This defaults to ``END_DATE``.
Methods
-------
make_future_minute_bar_data() -> iterable[(int, pd.DataFrame)]
A class method that returns a dict mapping sid to dataframe
which will be written to into the the format of the inherited
class which writes the minute bar data for use by a reader.
By default this creates some simple sythetic data with
:func:`~zipline.testing.create_minute_bar_data`
See Also
--------
zipline.testing.create_minute_bar_data
"""
FUTURE_MINUTE_BAR_LOOKBACK_DAYS = 0
FUTURE_MINUTE_BAR_START_DATE = alias('START_DATE')
FUTURE_MINUTE_BAR_END_DATE = alias('END_DATE')
@classmethod
def make_future_minute_bar_data(cls):
trading_calendar = get_calendar('us_futures')
return create_minute_bar_data(
trading_calendar.minutes_for_sessions_in_range(
cls.future_minute_bar_days[0],
cls.future_minute_bar_days[-1],
),
cls.asset_finder.futures_sids,
)
@classmethod
def init_class_fixtures(cls):
super(WithFutureMinuteBarData, cls).init_class_fixtures()
trading_calendar = get_calendar('us_futures')
cls.future_minute_bar_days = _trading_days_for_minute_bars(
trading_calendar,
cls.FUTURE_MINUTE_BAR_START_DATE,
cls.FUTURE_MINUTE_BAR_END_DATE,
cls.FUTURE_MINUTE_BAR_LOOKBACK_DAYS
)
class WithBcolzEquityMinuteBarReader(WithEquityMinuteBarData, WithTmpDir):
"""
ZiplineTestCase mixin providing cls.bcolz_minute_bar_path,
cls.bcolz_minute_bar_ctable, and cls.bcolz_equity_minute_bar_reader
class level fixtures.
After init_class_fixtures has been called:
- `cls.bcolz_minute_bar_path` is populated with
`cls.tmpdir.getpath(cls.BCOLZ_MINUTE_BAR_PATH)`.
- `cls.bcolz_minute_bar_ctable` is populated with data returned from
`cls.make_equity_minute_bar_data`. By default this calls
:func:`zipline.pipeline.loaders.synthetic.make_equity_minute_bar_data`.
- `cls.bcolz_equity_minute_bar_reader` is a minute bar reader
pointing to the directory that was just written to.
Attributes
----------
BCOLZ_MINUTE_BAR_PATH : str
The path inside the tmpdir where this will be written.
Methods
-------
make_bcolz_minute_bar_rootdir_path() -> string
A class method that returns the path for the directory that contains
the minute bar ctables. By default this is a subdirectory
BCOLZ_MINUTE_BAR_PATH in the shared temp directory.
See Also
--------
WithBcolzEquityDailyBarReader
WithDataPortal
zipline.testing.create_minute_bar_data
"""
BCOLZ_EQUITY_MINUTE_BAR_PATH = 'minute_equity_pricing'
@classmethod
def make_bcolz_equity_minute_bar_rootdir_path(cls):
return cls.tmpdir.makedir(cls.BCOLZ_EQUITY_MINUTE_BAR_PATH)
@classmethod
def init_class_fixtures(cls):
super(WithBcolzEquityMinuteBarReader, cls).init_class_fixtures()
cls.bcolz_equity_minute_bar_path = p = \
cls.make_bcolz_equity_minute_bar_rootdir_path()
days = cls.equity_minute_bar_days
writer = BcolzMinuteBarWriter(
p,
cls.trading_calendars[Equity],
days[0],
days[-1],
US_EQUITIES_MINUTES_PER_DAY
)
writer.write(cls.make_equity_minute_bar_data())
cls.bcolz_equity_minute_bar_reader = \
BcolzMinuteBarReader(p)
class WithBcolzFutureMinuteBarReader(WithFutureMinuteBarData, WithTmpDir):
"""
ZiplineTestCase mixin providing cls.bcolz_minute_bar_path,
cls.bcolz_minute_bar_ctable, and cls.bcolz_equity_minute_bar_reader
class level fixtures.
After init_class_fixtures has been called:
- `cls.bcolz_minute_bar_path` is populated with
`cls.tmpdir.getpath(cls.BCOLZ_MINUTE_BAR_PATH)`.
- `cls.bcolz_minute_bar_ctable` is populated with data returned from
`cls.make_equity_minute_bar_data`. By default this calls
:func:`zipline.pipeline.loaders.synthetic.make_equity_minute_bar_data`.
- `cls.bcolz_equity_minute_bar_reader` is a minute bar reader
pointing to the directory that was just written to.
Attributes
----------
BCOLZ_FUTURE_MINUTE_BAR_PATH : str
The path inside the tmpdir where this will be written.
Methods
-------
make_bcolz_minute_bar_rootdir_path() -> string
A class method that returns the path for the directory that contains
the minute bar ctables. By default this is a subdirectory
BCOLZ_MINUTE_BAR_PATH in the shared temp directory.
See Also
--------
WithBcolzEquityDailyBarReader
WithDataPortal
zipline.testing.create_minute_bar_data
"""
BCOLZ_FUTURE_MINUTE_BAR_PATH = 'minute_future_pricing'
OHLC_RATIOS_PER_SID = None
@classmethod
def make_bcolz_future_minute_bar_rootdir_path(cls):
return cls.tmpdir.makedir(cls.BCOLZ_FUTURE_MINUTE_BAR_PATH)
@classmethod
def init_class_fixtures(cls):
super(WithBcolzFutureMinuteBarReader, cls).init_class_fixtures()
trading_calendar = get_calendar('us_futures')
cls.bcolz_future_minute_bar_path = p = \
cls.make_bcolz_future_minute_bar_rootdir_path()
days = cls.future_minute_bar_days
writer = BcolzMinuteBarWriter(
p,
trading_calendar,
days[0],
days[-1],
FUTURES_MINUTES_PER_DAY,
ohlc_ratios_per_sid=cls.OHLC_RATIOS_PER_SID,
)
writer.write(cls.make_future_minute_bar_data())
cls.bcolz_future_minute_bar_reader = \
BcolzMinuteBarReader(p)
class WithConstantEquityMinuteBarData(WithEquityMinuteBarData):
EQUITY_MINUTE_CONSTANT_LOW = 3.0
EQUITY_MINUTE_CONSTANT_OPEN = 4.0
EQUITY_MINUTE_CONSTANT_CLOSE = 5.0
EQUITY_MINUTE_CONSTANT_HIGH = 6.0
EQUITY_MINUTE_CONSTANT_VOLUME = 100.0
@classmethod
def make_equity_minute_bar_data(cls):
trading_calendar = cls.trading_calendars[Equity]
sids = cls.asset_finder.equities_sids
minutes = trading_calendar.minutes_for_sessions_in_range(
cls.equity_minute_bar_days[0],
cls.equity_minute_bar_days[-1],
)
frame = pd.DataFrame(
{
'open': cls.EQUITY_MINUTE_CONSTANT_OPEN,
'high': cls.EQUITY_MINUTE_CONSTANT_HIGH,
'low': cls.EQUITY_MINUTE_CONSTANT_LOW,
'close': cls.EQUITY_MINUTE_CONSTANT_CLOSE,
'volume': cls.EQUITY_MINUTE_CONSTANT_VOLUME,
},
index=minutes,
)
return ((sid, frame) for sid in sids)
class WithConstantFutureMinuteBarData(WithFutureMinuteBarData):
FUTURE_MINUTE_CONSTANT_LOW = 3.0
FUTURE_MINUTE_CONSTANT_OPEN = 4.0
FUTURE_MINUTE_CONSTANT_CLOSE = 5.0
FUTURE_MINUTE_CONSTANT_HIGH = 6.0
FUTURE_MINUTE_CONSTANT_VOLUME = 100.0
@classmethod
def make_future_minute_bar_data(cls):
trading_calendar = cls.trading_calendars[Future]
sids = cls.asset_finder.futures_sids
minutes = trading_calendar.minutes_for_sessions_in_range(
cls.future_minute_bar_days[0],
cls.future_minute_bar_days[-1],
)
frame = pd.DataFrame(
{
'open': cls.FUTURE_MINUTE_CONSTANT_OPEN,
'high': cls.FUTURE_MINUTE_CONSTANT_HIGH,
'low': cls.FUTURE_MINUTE_CONSTANT_LOW,
'close': cls.FUTURE_MINUTE_CONSTANT_CLOSE,
'volume': cls.FUTURE_MINUTE_CONSTANT_VOLUME,
},
index=minutes,
)
return ((sid, frame) for sid in sids)
class WithAdjustmentReader(WithBcolzEquityDailyBarReader):
"""
ZiplineTestCase mixin providing cls.adjustment_reader as a class level
fixture.
After init_class_fixtures has been called, `cls.adjustment_reader` will be
populated with a new SQLiteAdjustmentReader object. The data that will be
written can be passed by overriding `make_{field}_data` where field may
be `splits`, `mergers` `dividends`, or `stock_dividends`.
The daily bar reader used for this adjustment reader may be customized
by overriding `make_adjustment_writer_equity_daily_bar_reader`.
This is useful to providing a `MockDailyBarReader`.
Methods
-------
make_splits_data() -> pd.DataFrame
A class method that returns a dataframe of splits data to write to the
class's adjustment db. By default this is empty.
make_mergers_data() -> pd.DataFrame
A class method that returns a dataframe of mergers data to write to the
class's adjustment db. By default this is empty.
make_dividends_data() -> pd.DataFrame
A class method that returns a dataframe of dividends data to write to
the class's adjustment db. By default this is empty.
make_stock_dividends_data() -> pd.DataFrame
A class method that returns a dataframe of stock dividends data to
write to the class's adjustment db. By default this is empty.
make_adjustment_db_conn_str() -> string
A class method that returns the sqlite3 connection string for the
database in to which the adjustments will be written. By default this
is an in-memory database.
make_adjustment_writer_equity_daily_bar_reader() -> pd.DataFrame
A class method that returns the daily bar reader to use for the class's
adjustment writer. By default this is the class's actual
``bcolz_equity_daily_bar_reader`` as inherited from
``WithBcolzEquityDailyBarReader``. This should probably not be
overridden; however, some tests used a ``MockDailyBarReader``
for this.
make_adjustment_writer(conn: sqlite3.Connection) -> AdjustmentWriter
A class method that constructs the adjustment which will be used
to write the data into the connection to be used by the class's
adjustment reader.
See Also
--------
zipline.testing.MockDailyBarReader
"""
@classmethod
def _make_data(cls):
return None
make_splits_data = _make_data
make_mergers_data = _make_data
make_dividends_data = _make_data
make_stock_dividends_data = _make_data
del _make_data
@classmethod
def make_adjustment_writer(cls, conn):
return SQLiteAdjustmentWriter(
conn,
cls.make_adjustment_writer_equity_daily_bar_reader(),
)
@classmethod
def make_adjustment_writer_equity_daily_bar_reader(cls):
return cls.bcolz_equity_daily_bar_reader
@classmethod
def make_adjustment_db_conn_str(cls):
return ':memory:'
@classmethod
def init_class_fixtures(cls):
super(WithAdjustmentReader, cls).init_class_fixtures()
conn = sqlite3.connect(cls.make_adjustment_db_conn_str())
# Silence numpy DeprecationWarnings which cause nosetest to fail
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
cls.make_adjustment_writer(conn).write(
splits=cls.make_splits_data(),
mergers=cls.make_mergers_data(),
dividends=cls.make_dividends_data(),
stock_dividends=cls.make_stock_dividends_data(),
)
cls.adjustment_reader = SQLiteAdjustmentReader(conn)
class WithUSEquityPricingPipelineEngine(WithAdjustmentReader,
WithTradingSessions):
"""
Mixin providing the following as a class-level fixtures.
- cls.data_root_dir
- cls.findata_dir
- cls.pipeline_engine
- cls.adjustments_db_path
"""
@classmethod
def init_class_fixtures(cls):
cls.data_root_dir = cls.enter_class_context(tmp_dir())
cls.findata_dir = cls.data_root_dir.makedir('findata')
super(WithUSEquityPricingPipelineEngine, cls).init_class_fixtures()
loader = USEquityPricingLoader.without_fx(
cls.bcolz_equity_daily_bar_reader,
SQLiteAdjustmentReader(cls.adjustments_db_path),
)
def get_loader(column):
if column in USEquityPricing.columns:
return loader
else:
raise AssertionError("No loader registered for %s" % column)
cls.pipeline_engine = SimplePipelineEngine(
get_loader=get_loader,
asset_finder=cls.asset_finder,
default_domain=US_EQUITIES,
)
@classmethod
def make_adjustment_db_conn_str(cls):
cls.adjustments_db_path = os.path.join(
cls.findata_dir,
'adjustments',
cls.END_DATE.strftime("%Y-%m-%d-adjustments.db")
)
ensure_directory(os.path.dirname(cls.adjustments_db_path))
return cls.adjustments_db_path
class WithSeededRandomPipelineEngine(WithTradingSessions, WithAssetFinder):
"""
ZiplineTestCase mixin providing class-level fixtures for running pipelines
against deterministically-generated random data.
Attributes
----------
SEEDED_RANDOM_PIPELINE_SEED : int
Fixture input. Random seed used to initialize the random state loader.
seeded_random_loader : SeededRandomLoader
Fixture output. Loader capable of providing columns for
zipline.pipeline.data.testing.TestingDataSet.
seeded_random_engine : SimplePipelineEngine
Fixture output. A pipeline engine that will use seeded_random_loader
as its only data provider.
Methods
-------
run_pipeline(start_date, end_date)
Run a pipeline with self.seeded_random_engine.
See Also
--------
zipline.pipeline.loaders.synthetic.SeededRandomLoader
zipline.pipeline.loaders.testing.make_seeded_random_loader
zipline.pipeline.engine.SimplePipelineEngine
"""
SEEDED_RANDOM_PIPELINE_SEED = 42
SEEDED_RANDOM_PIPELINE_DEFAULT_DOMAIN = GENERIC
@classmethod
def init_class_fixtures(cls):
super(WithSeededRandomPipelineEngine, cls).init_class_fixtures()
cls._sids = cls.asset_finder.sids
cls.seeded_random_loader = loader = make_seeded_random_loader(
cls.SEEDED_RANDOM_PIPELINE_SEED,
cls.trading_days,
cls._sids,
columns=cls.make_seeded_random_loader_columns(),
)
cls.seeded_random_engine = SimplePipelineEngine(
get_loader=lambda column: loader,
asset_finder=cls.asset_finder,
default_domain=cls.SEEDED_RANDOM_PIPELINE_DEFAULT_DOMAIN,
default_hooks=cls.make_seeded_random_pipeline_engine_hooks(),
populate_initial_workspace=(
cls.make_seeded_random_populate_initial_workspace()
),
)
@classmethod
def make_seeded_random_pipeline_engine_hooks(cls):
return []
@classmethod
def make_seeded_random_populate_initial_workspace(cls):
return None
@classmethod
def make_seeded_random_loader_columns(cls):
return TestingDataSet.columns
def raw_expected_values(self, column, start_date, end_date):
"""
Get an array containing the raw values we expect to be produced for the
given dates between start_date and end_date, inclusive.
"""
all_values = self.seeded_random_loader.values(
column.dtype,
self.trading_days,
self._sids,
)
row_slice = self.trading_days.slice_indexer(start_date, end_date)
return all_values[row_slice]
def run_pipeline(self, pipeline, start_date, end_date, hooks=None):
"""
Run a pipeline with self.seeded_random_engine.
"""
return self.seeded_random_engine.run_pipeline(
pipeline,
start_date,
end_date,
hooks=hooks,
)
def run_chunked_pipeline(self,
pipeline,
start_date,
end_date,
chunksize,
hooks=None):
"""
Run a chunked pipeline with self.seeded_random_engine.
"""
return self.seeded_random_engine.run_chunked_pipeline(
pipeline,
start_date,
end_date,
chunksize=chunksize,
hooks=hooks,
)
class WithDataPortal(WithAdjustmentReader,
# Ordered so that bcolz minute reader is used first.
WithBcolzEquityMinuteBarReader,
WithBcolzFutureMinuteBarReader):
"""
ZiplineTestCase mixin providing self.data_portal as an instance level
fixture.
After init_instance_fixtures has been called, `self.data_portal` will be
populated with a new data portal created by passing in the class's
trading env, `cls.bcolz_equity_minute_bar_reader`,
`cls.bcolz_equity_daily_bar_reader`, and `cls.adjustment_reader`.
Attributes
----------
DATA_PORTAL_USE_DAILY_DATA : bool
Should the daily bar reader be used? Defaults to True.
DATA_PORTAL_USE_MINUTE_DATA : bool
Should the minute bar reader be used? Defaults to True.
DATA_PORTAL_USE_ADJUSTMENTS : bool
Should the adjustment reader be used? Defaults to True.
Methods
-------
make_data_portal() -> DataPortal
Method which returns the data portal to be used for each test case.
If this is overridden, the ``DATA_PORTAL_USE_*`` attributes may not
be respected.
"""
DATA_PORTAL_USE_DAILY_DATA = True
DATA_PORTAL_USE_MINUTE_DATA = True
DATA_PORTAL_USE_ADJUSTMENTS = True
DATA_PORTAL_FIRST_TRADING_DAY = None
DATA_PORTAL_LAST_AVAILABLE_SESSION = None
DATA_PORTAL_LAST_AVAILABLE_MINUTE = None
DATA_PORTAL_MINUTE_HISTORY_PREFETCH = DEFAULT_MINUTE_HISTORY_PREFETCH
DATA_PORTAL_DAILY_HISTORY_PREFETCH = DEFAULT_DAILY_HISTORY_PREFETCH
def make_data_portal(self):
if self.DATA_PORTAL_FIRST_TRADING_DAY is None:
if self.DATA_PORTAL_USE_MINUTE_DATA:
self.DATA_PORTAL_FIRST_TRADING_DAY = (
self.bcolz_equity_minute_bar_reader.
first_trading_day)
elif self.DATA_PORTAL_USE_DAILY_DATA:
self.DATA_PORTAL_FIRST_TRADING_DAY = (
self.bcolz_equity_daily_bar_reader.
first_trading_day)
return DataPortal(
self.asset_finder,
self.trading_calendar,
first_trading_day=self.DATA_PORTAL_FIRST_TRADING_DAY,
equity_daily_reader=(
self.bcolz_equity_daily_bar_reader
if self.DATA_PORTAL_USE_DAILY_DATA else
None
),
equity_minute_reader=(
self.bcolz_equity_minute_bar_reader
if self.DATA_PORTAL_USE_MINUTE_DATA else
None
),
adjustment_reader=(
self.adjustment_reader
if self.DATA_PORTAL_USE_ADJUSTMENTS else
None
),
future_minute_reader=(
self.bcolz_future_minute_bar_reader
if self.DATA_PORTAL_USE_MINUTE_DATA else
None
),
future_daily_reader=(
MinuteResampleSessionBarReader(
self.bcolz_future_minute_bar_reader.trading_calendar,
self.bcolz_future_minute_bar_reader)
if self.DATA_PORTAL_USE_MINUTE_DATA else None
),
last_available_session=self.DATA_PORTAL_LAST_AVAILABLE_SESSION,
last_available_minute=self.DATA_PORTAL_LAST_AVAILABLE_MINUTE,
minute_history_prefetch_length=self.
DATA_PORTAL_MINUTE_HISTORY_PREFETCH,
daily_history_prefetch_length=self.
DATA_PORTAL_DAILY_HISTORY_PREFETCH,
)
def init_instance_fixtures(self):
super(WithDataPortal, self).init_instance_fixtures()
self.data_portal = self.make_data_portal()
class WithResponses(object):
"""
ZiplineTestCase mixin that provides self.responses as an instance
fixture.
After init_instance_fixtures has been called, `self.responses` will be
a new `responses.RequestsMock` object. Users may add new endpoints to this
with the `self.responses.add` method.
"""
def init_instance_fixtures(self):
super(WithResponses, self).init_instance_fixtures()
self.responses = self.enter_instance_context(
responses.RequestsMock(),
)
class WithCreateBarData(WithDataPortal):
CREATE_BARDATA_DATA_FREQUENCY = 'minute'
def create_bardata(self, simulation_dt_func, restrictions=None):
return BarData(
self.data_portal,
simulation_dt_func,
self.CREATE_BARDATA_DATA_FREQUENCY,
self.trading_calendar,
restrictions or NoRestrictions()
)
class WithMakeAlgo(WithBenchmarkReturns,
WithSimParams,
WithLogger,
WithDataPortal):
"""
ZiplineTestCase mixin that provides a ``make_algo`` method.
"""
START_DATE = pd.Timestamp('2014-12-29', tz='UTC')
END_DATE = pd.Timestamp('2015-1-05', tz='UTC')
SIM_PARAMS_DATA_FREQUENCY = 'minute'
DEFAULT_ALGORITHM_CLASS = TradingAlgorithm
@classproperty
def BENCHMARK_SID(cls):
"""The sid to use as a benchmark.
Can be overridden to use an alternative benchmark.
"""
return cls.asset_finder.sids[0]
def merge_with_inherited_algo_kwargs(self,
overriding_type,
suite_overrides,
method_overrides):
"""
Helper for subclasses overriding ``make_algo_kwargs``.
A common pattern for tests using `WithMakeAlgoKwargs` is that a
particular test suite has a set of default keywords it wants to use
everywhere, but also accepts test-specific overrides.
Test suites that fit that pattern can call this method and pass the
test class, suite-specific overrides, and method-specific overrides,
and this method takes care of fetching parent class overrides and
merging them with the suite- and instance-specific overrides.
Parameters
----------
overriding_type : type
The type from which we're being called. This is forwarded to
super().make_algo_kwargs()
suite_overrides : dict
Keywords which should take precedence over kwargs returned by
super(overriding_type, self).make_algo_kwargs(). These are
generally keyword arguments that are constant within a test suite.
method_overrides : dict
Keywords which should take precedence over `suite_overrides` and
superclass kwargs. These are generally keyword arguments that are
overridden on a per-test basis.
"""
# NOTE: This is a weird invocation of super().
# Our goal here is to provide the behavior that the caller would get if
# they called super() in the normal way, so that we dispatch to the
# make_algo_kwargs() for the parent of the type that's calling
# into us. We achieve that goal by requiring the caller to tell us
# what type they're calling us from.
return super(overriding_type, self).make_algo_kwargs(
**merge(suite_overrides, method_overrides)
)
def make_algo_kwargs(self, **overrides):
if self.BENCHMARK_SID is None:
overrides.setdefault('benchmark_returns', self.BENCHMARK_RETURNS)
return merge(
{
'sim_params': self.sim_params,
'data_portal': self.data_portal,
'benchmark_sid': self.BENCHMARK_SID,
},
overrides,
)
def make_algo(self, algo_class=None, **overrides):
if algo_class is None:
algo_class = self.DEFAULT_ALGORITHM_CLASS
return algo_class(**self.make_algo_kwargs(**overrides))
def run_algorithm(self, **overrides):
"""
Create and run an TradingAlgorithm in memory.
"""
return self.make_algo(**overrides).run()
class WithWerror(object):
@classmethod
def init_class_fixtures(cls):
cls.enter_class_context(warnings.catch_warnings())
warnings.simplefilter('error')
super(WithWerror, cls).init_class_fixtures()
register_calendar_alias("TEST", "NYSE")
class WithSeededRandomState(object):
RANDOM_SEED = np.array(list('lmao'), dtype='S1').view('i4').item()
def init_instance_fixtures(self):
super(WithSeededRandomState, self).init_instance_fixtures()
self.rand = np.random.RandomState(self.RANDOM_SEED)
class WithFXRates(object):
"""Fixture providing a factory for in-memory exchange rate data.
"""
# Start date for exchange rates data.
FX_RATES_START_DATE = alias('START_DATE')
# End date for exchange rates data.
FX_RATES_END_DATE = alias('END_DATE')
# Calendar to which exchange rates data is aligned.
FX_RATES_CALENDAR = '24/5'
# Currencies between which exchange rates can be calculated.
FX_RATES_CURRENCIES = ["USD", "CAD", "GBP", "EUR"]
# Kinds of rates for which exchange rate data is present.
FX_RATES_RATE_NAMES = ["mid"]
# Default chunk size used for fx artifact compression.
HDF5_FX_CHUNK_SIZE = 75
# Rate used by default for Pipeline API queries that don't specify a rate
# explicitly.
@classproperty
def FX_RATES_DEFAULT_RATE(cls):
return cls.FX_RATES_RATE_NAMES[0]
@classmethod
def init_class_fixtures(cls):
super(WithFXRates, cls).init_class_fixtures()
cal = get_calendar(cls.FX_RATES_CALENDAR)
cls.fx_rates_sessions = cal.sessions_in_range(
cls.FX_RATES_START_DATE,
cls.FX_RATES_END_DATE,
)
cls.fx_rates = cls.make_fx_rates(
cls.FX_RATES_RATE_NAMES,
cls.FX_RATES_CURRENCIES,
cls.fx_rates_sessions,
)
cls.in_memory_fx_rate_reader = InMemoryFXRateReader(
cls.fx_rates,
cls.FX_RATES_DEFAULT_RATE,
)
@classmethod
def make_fx_rates_from_reference(cls, reference):
"""
Helper method for implementing make_fx_rates.
Takes a (dates x currencies) DataFrame of "reference" values, which are
assumed to be the "true" value of each currency in some unknown
external currency. Computes fx rates from A -> B as by dividing the
reference value for A by the reference value for B.
Parameters
----------
reference : pd.DataFrame
DataFrame of "true" values for currencies.
Returns
-------
rates : dict[str, pd.DataFrame]
Map from quote currency to FX rates for that currency.
"""
out = {}
for quote in reference.columns:
out[quote] = reference.divide(reference[quote], axis=0)
return out
@classmethod
def make_fx_rates(cls, rate_names, currencies, sessions):
rng = np.random.RandomState(42)
out = {}
for rate_name in rate_names:
cols = {}
for currency in currencies:
start, end = sorted(rng.uniform(0.5, 1.5, (2,)))
cols[currency] = np.linspace(start, end, len(sessions))
reference = pd.DataFrame(cols, index=sessions, columns=currencies)
out[rate_name] = cls.make_fx_rates_from_reference(reference)
return out
@classmethod
def write_h5_fx_rates(cls, path):
"""Write cls.fx_rates to disk with an HDF5FXRateWriter.
Returns an HDF5FXRateReader that reader from written data.
"""
sessions = cls.fx_rates_sessions
# Write in-memory data to h5 file.
with h5py.File(path, 'w') as h5_file:
writer = HDF5FXRateWriter(h5_file, cls.HDF5_FX_CHUNK_SIZE)
fx_data = ((rate, quote, quote_frame.values)
for rate, rate_dict in cls.fx_rates.items()
for quote, quote_frame in rate_dict.items())
writer.write(
dts=sessions.values,
currencies=np.array(cls.FX_RATES_CURRENCIES, dtype=object),
data=fx_data,
)
h5_file = cls.enter_class_context(h5py.File(path, 'r'))
return HDF5FXRateReader(
h5_file,
default_rate=cls.FX_RATES_DEFAULT_RATE,
)
@classmethod
def get_expected_fx_rate_scalar(cls, rate, quote, base, dt):
"""Get the expected FX rate for the given scalar coordinates.
"""
if base is None:
return np.nan
if rate == DEFAULT_FX_RATE:
rate = cls.FX_RATES_DEFAULT_RATE
col = cls.fx_rates[rate][quote][base]
if dt < col.index[0]:
return np.nan
# PERF: We call this function a lot in some suites, and get_loc is
# surprisingly expensive, so optimizing it has a meaningful impact on
# overall suite performance. See test_fast_get_loc_ffilled_for
# assurance that this behaves the same as get_loc.
ix = fast_get_loc_ffilled(col.index.values, dt.asm8)
return col.values[ix]
@classmethod
def get_expected_fx_rates(cls, rate, quote, bases, dts):
"""Get an array of expected FX rates for the given indices.
"""
out = np.empty((len(dts), len(bases)), dtype='float64')
for i, dt in enumerate(dts):
for j, base in enumerate(bases):
out[i, j] = cls.get_expected_fx_rate_scalar(
rate, quote, base, dt,
)
return out
@classmethod
def get_expected_fx_rates_columnar(cls, rate, quote, bases, dts):
assert len(bases) == len(dts)
rates = [
cls.get_expected_fx_rate_scalar(rate, quote, base, dt)
for base, dt in zip(bases, dts)
]
return np.array(rates, dtype='float64')
def fast_get_loc_ffilled(dts, dt):
"""
Equivalent to dts.get_loc(dt, method='ffill'), but with reasonable
microperformance.
"""
ix = dts.searchsorted(dt, side='right') - 1
if ix < 0:
raise KeyError(dt)
return ix | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/testing/fixtures.py | fixtures.py |
from abc import ABCMeta, abstractmethod, abstractproperty
from contextlib import contextmanager
import gzip
from itertools import (
combinations,
count,
product,
)
import json
import operator
import os
from os.path import abspath, dirname, join, realpath
import shutil
import sys
import tempfile
from traceback import format_exception
from logbook import TestHandler
from mock import patch
from nose.tools import nottest
from numpy.testing import assert_allclose, assert_array_equal
import pandas as pd
from six import itervalues, iteritems, with_metaclass
from six.moves import filter, map
from sqlalchemy import create_engine
from testfixtures import TempDirectory
from toolz import concat, curry
from trading_calendars import get_calendar
from zipline.assets import AssetFinder, AssetDBWriter
from zipline.assets.synthetic import make_simple_equity_info
from zipline.utils.compat import getargspec, wraps
from zipline.data.data_portal import DataPortal
from zipline.data.minute_bars import (
BcolzMinuteBarReader,
BcolzMinuteBarWriter,
US_EQUITIES_MINUTES_PER_DAY
)
from zipline.data.bcolz_daily_bars import (
BcolzDailyBarReader,
BcolzDailyBarWriter,
)
from zipline.finance.blotter import SimulationBlotter
from zipline.finance.order import ORDER_STATUS
from zipline.lib.labelarray import LabelArray
from zipline.pipeline.data import EquityPricing
from zipline.pipeline.domain import EquitySessionDomain
from zipline.pipeline.engine import SimplePipelineEngine
from zipline.pipeline.factors import CustomFactor
from zipline.pipeline.loaders.testing import make_seeded_random_loader
from zipline.utils import security_list
from zipline.utils.input_validation import expect_dimensions
from zipline.utils.numpy_utils import as_column, isnat
from zipline.utils.pandas_utils import timedelta_to_integral_seconds
from zipline.utils.sentinel import sentinel
import numpy as np
from numpy import float64
EPOCH = pd.Timestamp(0, tz='UTC')
def seconds_to_timestamp(seconds):
return pd.Timestamp(seconds, unit='s', tz='UTC')
def to_utc(time_str):
"""Convert a string in US/Eastern time to UTC"""
return pd.Timestamp(time_str, tz='US/Eastern').tz_convert('UTC')
def str_to_seconds(s):
"""
Convert a pandas-intelligible string to (integer) seconds since UTC.
>>> from pandas import Timestamp
>>> (Timestamp('2014-01-01') - Timestamp(0)).total_seconds()
1388534400.0
>>> str_to_seconds('2014-01-01')
1388534400
"""
return timedelta_to_integral_seconds(pd.Timestamp(s, tz='UTC') - EPOCH)
def drain_zipline(test, zipline):
output = []
transaction_count = 0
msg_counter = 0
# start the simulation
for update in zipline:
msg_counter += 1
output.append(update)
if 'daily_perf' in update:
transaction_count += \
len(update['daily_perf']['transactions'])
return output, transaction_count
def check_algo_results(test,
results,
expected_transactions_count=None,
expected_order_count=None,
expected_positions_count=None,
sid=None):
if expected_transactions_count is not None:
txns = flatten_list(results["transactions"])
test.assertEqual(expected_transactions_count, len(txns))
if expected_positions_count is not None:
raise NotImplementedError
if expected_order_count is not None:
# de-dup orders on id, because orders are put back into perf packets
# whenever they a txn is filled
orders = set([order['id'] for order in
flatten_list(results["orders"])])
test.assertEqual(expected_order_count, len(orders))
def flatten_list(list):
return [item for sublist in list for item in sublist]
def assert_single_position(test, zipline):
output, transaction_count = drain_zipline(test, zipline)
if 'expected_transactions' in test.zipline_test_config:
test.assertEqual(
test.zipline_test_config['expected_transactions'],
transaction_count
)
else:
test.assertEqual(
test.zipline_test_config['order_count'],
transaction_count
)
# the final message is the risk report, the second to
# last is the final day's results. Positions is a list of
# dicts.
closing_positions = output[-2]['daily_perf']['positions']
# confirm that all orders were filled.
# iterate over the output updates, overwriting
# orders when they are updated. Then check the status on all.
orders_by_id = {}
for update in output:
if 'daily_perf' in update:
if 'orders' in update['daily_perf']:
for order in update['daily_perf']['orders']:
orders_by_id[order['id']] = order
for order in itervalues(orders_by_id):
test.assertEqual(
order['status'],
ORDER_STATUS.FILLED,
"")
test.assertEqual(
len(closing_positions),
1,
"Portfolio should have one position."
)
sid = test.zipline_test_config['sid']
test.assertEqual(
closing_positions[0]['sid'],
sid,
"Portfolio should have one position in " + str(sid)
)
return output, transaction_count
@contextmanager
def security_list_copy():
old_dir = security_list.SECURITY_LISTS_DIR
new_dir = tempfile.mkdtemp()
try:
for subdir in os.listdir(old_dir):
shutil.copytree(os.path.join(old_dir, subdir),
os.path.join(new_dir, subdir))
with patch.object(security_list, 'SECURITY_LISTS_DIR', new_dir), \
patch.object(security_list, 'using_copy', True,
create=True):
yield
finally:
shutil.rmtree(new_dir, True)
def add_security_data(adds, deletes):
if not hasattr(security_list, 'using_copy'):
raise Exception('add_security_data must be used within '
'security_list_copy context')
directory = os.path.join(
security_list.SECURITY_LISTS_DIR,
"leveraged_etf_list/20150127/20150125"
)
if not os.path.exists(directory):
os.makedirs(directory)
del_path = os.path.join(directory, "delete")
with open(del_path, 'w') as f:
for sym in deletes:
f.write(sym)
f.write('\n')
add_path = os.path.join(directory, "add")
with open(add_path, 'w') as f:
for sym in adds:
f.write(sym)
f.write('\n')
def all_pairs_matching_predicate(values, pred):
"""
Return an iterator of all pairs, (v0, v1) from values such that
`pred(v0, v1) == True`
Parameters
----------
values : iterable
pred : function
Returns
-------
pairs_iterator : generator
Generator yielding pairs matching `pred`.
Examples
--------
>>> from zipline.testing import all_pairs_matching_predicate
>>> from operator import eq, lt
>>> list(all_pairs_matching_predicate(range(5), eq))
[(0, 0), (1, 1), (2, 2), (3, 3), (4, 4)]
>>> list(all_pairs_matching_predicate("abcd", lt))
[('a', 'b'), ('a', 'c'), ('a', 'd'), ('b', 'c'), ('b', 'd'), ('c', 'd')]
"""
return filter(lambda pair: pred(*pair), product(values, repeat=2))
def product_upper_triangle(values, include_diagonal=False):
"""
Return an iterator over pairs, (v0, v1), drawn from values.
If `include_diagonal` is True, returns all pairs such that v0 <= v1.
If `include_diagonal` is False, returns all pairs such that v0 < v1.
"""
return all_pairs_matching_predicate(
values,
operator.le if include_diagonal else operator.lt,
)
def all_subindices(index):
"""
Return all valid sub-indices of a pandas Index.
"""
return (
index[start:stop]
for start, stop in product_upper_triangle(range(len(index) + 1))
)
def chrange(start, stop):
"""
Construct an iterable of length-1 strings beginning with `start` and ending
with `stop`.
Parameters
----------
start : str
The first character.
stop : str
The last character.
Returns
-------
chars: iterable[str]
Iterable of strings beginning with start and ending with stop.
Examples
--------
>>> chrange('A', 'C')
['A', 'B', 'C']
"""
return list(map(chr, range(ord(start), ord(stop) + 1)))
def make_trade_data_for_asset_info(dates,
asset_info,
price_start,
price_step_by_date,
price_step_by_sid,
volume_start,
volume_step_by_date,
volume_step_by_sid):
"""
Convert the asset info dataframe into a dataframe of trade data for each
sid, and write to the writer if provided. Write NaNs for locations where
assets did not exist. Return a dict of the dataframes, keyed by sid.
"""
trade_data = {}
sids = asset_info.index
price_sid_deltas = np.arange(len(sids), dtype=float64) * price_step_by_sid
price_date_deltas = (np.arange(len(dates), dtype=float64) *
price_step_by_date)
prices = (price_sid_deltas + as_column(price_date_deltas)) + price_start
volume_sid_deltas = np.arange(len(sids)) * volume_step_by_sid
volume_date_deltas = np.arange(len(dates)) * volume_step_by_date
volumes = volume_sid_deltas + as_column(volume_date_deltas) + volume_start
for j, sid in enumerate(sids):
start_date, end_date = asset_info.loc[sid, ['start_date', 'end_date']]
# Normalize here so the we still generate non-NaN values on the minutes
# for an asset's last trading day.
for i, date in enumerate(dates.normalize()):
if not (start_date <= date <= end_date):
prices[i, j] = 0
volumes[i, j] = 0
df = pd.DataFrame(
{
"open": prices[:, j],
"high": prices[:, j],
"low": prices[:, j],
"close": prices[:, j],
"volume": volumes[:, j],
},
index=dates,
)
trade_data[sid] = df
return trade_data
def check_allclose(actual,
desired,
rtol=1e-07,
atol=0,
err_msg='',
verbose=True):
"""
Wrapper around np.testing.assert_allclose that also verifies that inputs
are ndarrays.
See Also
--------
np.assert_allclose
"""
if type(actual) != type(desired):
raise AssertionError("%s != %s" % (type(actual), type(desired)))
return assert_allclose(
actual,
desired,
atol=atol,
rtol=rtol,
err_msg=err_msg,
verbose=verbose,
)
def check_arrays(x, y, err_msg='', verbose=True, check_dtypes=True):
"""
Wrapper around np.testing.assert_array_equal that also verifies that inputs
are ndarrays.
See Also
--------
np.assert_array_equal
"""
assert type(x) == type(y), "{x} != {y}".format(x=type(x), y=type(y))
assert x.dtype == y.dtype, "{x.dtype} != {y.dtype}".format(x=x, y=y)
if isinstance(x, LabelArray):
# Check that both arrays have missing values in the same locations...
assert_array_equal(
x.is_missing(),
y.is_missing(),
err_msg=err_msg,
verbose=verbose,
)
# ...then check the actual values as well.
x = x.as_string_array()
y = y.as_string_array()
elif x.dtype.kind in 'mM':
x_isnat = isnat(x)
y_isnat = isnat(y)
assert_array_equal(
x_isnat,
y_isnat,
err_msg="NaTs not equal",
verbose=verbose,
)
# Fill NaTs with zero for comparison.
x = np.where(x_isnat, np.zeros_like(x), x)
y = np.where(y_isnat, np.zeros_like(y), y)
return assert_array_equal(x, y, err_msg=err_msg, verbose=verbose)
class UnexpectedAttributeAccess(Exception):
pass
class ExplodingObject(object):
"""
Object that will raise an exception on any attribute access.
Useful for verifying that an object is never touched during a
function/method call.
"""
def __getattribute__(self, name):
raise UnexpectedAttributeAccess(name)
def write_minute_data(trading_calendar, tempdir, minutes, sids):
first_session = trading_calendar.minute_to_session_label(
minutes[0], direction="none"
)
last_session = trading_calendar.minute_to_session_label(
minutes[-1], direction="none"
)
sessions = trading_calendar.sessions_in_range(first_session, last_session)
write_bcolz_minute_data(
trading_calendar,
sessions,
tempdir.path,
create_minute_bar_data(minutes, sids),
)
return tempdir.path
def create_minute_bar_data(minutes, sids):
length = len(minutes)
for sid_idx, sid in enumerate(sids):
yield sid, pd.DataFrame(
{
'open': np.arange(length) + 10 + sid_idx,
'high': np.arange(length) + 15 + sid_idx,
'low': np.arange(length) + 8 + sid_idx,
'close': np.arange(length) + 10 + sid_idx,
'volume': 100 + sid_idx,
},
index=minutes,
)
def create_daily_bar_data(sessions, sids):
length = len(sessions)
for sid_idx, sid in enumerate(sids):
yield sid, pd.DataFrame(
{
"open": (np.array(range(10, 10 + length)) + sid_idx),
"high": (np.array(range(15, 15 + length)) + sid_idx),
"low": (np.array(range(8, 8 + length)) + sid_idx),
"close": (np.array(range(10, 10 + length)) + sid_idx),
"volume": np.array(range(100, 100 + length)) + sid_idx,
"day": [session.value for session in sessions]
},
index=sessions,
)
def write_daily_data(tempdir, sim_params, sids, trading_calendar):
path = os.path.join(tempdir.path, "testdaily.bcolz")
BcolzDailyBarWriter(path, trading_calendar,
sim_params.start_session,
sim_params.end_session).write(
create_daily_bar_data(sim_params.sessions, sids),
)
return path
def create_data_portal(asset_finder, tempdir, sim_params, sids,
trading_calendar, adjustment_reader=None):
if sim_params.data_frequency == "daily":
daily_path = write_daily_data(tempdir, sim_params, sids,
trading_calendar)
equity_daily_reader = BcolzDailyBarReader(daily_path)
return DataPortal(
asset_finder, trading_calendar,
first_trading_day=equity_daily_reader.first_trading_day,
equity_daily_reader=equity_daily_reader,
adjustment_reader=adjustment_reader
)
else:
minutes = trading_calendar.minutes_in_range(
sim_params.first_open,
sim_params.last_close
)
minute_path = write_minute_data(trading_calendar, tempdir, minutes,
sids)
equity_minute_reader = BcolzMinuteBarReader(minute_path)
return DataPortal(
asset_finder, trading_calendar,
first_trading_day=equity_minute_reader.first_trading_day,
equity_minute_reader=equity_minute_reader,
adjustment_reader=adjustment_reader
)
def write_bcolz_minute_data(trading_calendar, days, path, data):
BcolzMinuteBarWriter(
path,
trading_calendar,
days[0],
days[-1],
US_EQUITIES_MINUTES_PER_DAY
).write(data)
def create_minute_df_for_asset(trading_calendar,
start_dt,
end_dt,
interval=1,
start_val=1,
minute_blacklist=None):
asset_minutes = trading_calendar.minutes_for_sessions_in_range(
start_dt, end_dt
)
minutes_count = len(asset_minutes)
if interval > 1:
minutes_arr = np.zeros(minutes_count)
minutes_arr[interval-1::interval] = \
np.arange(start_val+interval-1, start_val+minutes_count, interval)
else:
minutes_arr = np.arange(start_val, start_val + minutes_count)
open_ = minutes_arr.copy()
open_[interval-1::interval] += 1
high = minutes_arr.copy()
high[interval-1::interval] += 2
low = minutes_arr.copy()
low[interval - 1::interval] -= 1
df = pd.DataFrame(
{
"open": open_,
"high": high,
"low": low,
"close": minutes_arr,
"volume": 100 * minutes_arr,
},
index=asset_minutes,
)
if minute_blacklist is not None:
for minute in minute_blacklist:
df.loc[minute] = 0
return df
def create_daily_df_for_asset(trading_calendar, start_day, end_day,
interval=1):
days = trading_calendar.sessions_in_range(start_day, end_day)
days_count = len(days)
days_arr = np.arange(days_count) + 2
df = pd.DataFrame(
{
"open": days_arr + 1,
"high": days_arr + 2,
"low": days_arr - 1,
"close": days_arr,
"volume": days_arr * 100,
},
index=days,
)
if interval > 1:
# only keep every 'interval' rows
for idx, _ in enumerate(days_arr):
if (idx + 1) % interval != 0:
df["open"].iloc[idx] = 0
df["high"].iloc[idx] = 0
df["low"].iloc[idx] = 0
df["close"].iloc[idx] = 0
df["volume"].iloc[idx] = 0
return df
def trades_by_sid_to_dfs(trades_by_sid, index):
for sidint, trades in iteritems(trades_by_sid):
opens = []
highs = []
lows = []
closes = []
volumes = []
for trade in trades:
opens.append(trade.open_price)
highs.append(trade.high)
lows.append(trade.low)
closes.append(trade.close_price)
volumes.append(trade.volume)
yield sidint, pd.DataFrame(
{
"open": opens,
"high": highs,
"low": lows,
"close": closes,
"volume": volumes,
},
index=index,
)
def create_data_portal_from_trade_history(asset_finder, trading_calendar,
tempdir, sim_params, trades_by_sid):
if sim_params.data_frequency == "daily":
path = os.path.join(tempdir.path, "testdaily.bcolz")
writer = BcolzDailyBarWriter(
path, trading_calendar,
sim_params.start_session,
sim_params.end_session
)
writer.write(
trades_by_sid_to_dfs(trades_by_sid, sim_params.sessions),
)
equity_daily_reader = BcolzDailyBarReader(path)
return DataPortal(
asset_finder, trading_calendar,
first_trading_day=equity_daily_reader.first_trading_day,
equity_daily_reader=equity_daily_reader,
)
else:
minutes = trading_calendar.minutes_in_range(
sim_params.first_open,
sim_params.last_close
)
length = len(minutes)
assets = {}
for sidint, trades in iteritems(trades_by_sid):
opens = np.zeros(length)
highs = np.zeros(length)
lows = np.zeros(length)
closes = np.zeros(length)
volumes = np.zeros(length)
for trade in trades:
# put them in the right place
idx = minutes.searchsorted(trade.dt)
opens[idx] = trade.open_price * 1000
highs[idx] = trade.high * 1000
lows[idx] = trade.low * 1000
closes[idx] = trade.close_price * 1000
volumes[idx] = trade.volume
assets[sidint] = pd.DataFrame({
"open": opens,
"high": highs,
"low": lows,
"close": closes,
"volume": volumes,
"dt": minutes
}).set_index("dt")
write_bcolz_minute_data(
trading_calendar,
sim_params.sessions,
tempdir.path,
assets
)
equity_minute_reader = BcolzMinuteBarReader(tempdir.path)
return DataPortal(
asset_finder, trading_calendar,
first_trading_day=equity_minute_reader.first_trading_day,
equity_minute_reader=equity_minute_reader,
)
class FakeDataPortal(DataPortal):
def __init__(self, asset_finder, trading_calendar=None,
first_trading_day=None):
if trading_calendar is None:
trading_calendar = get_calendar("NYSE")
super(FakeDataPortal, self).__init__(asset_finder,
trading_calendar,
first_trading_day)
def get_spot_value(self, asset, field, dt, data_frequency):
if field == "volume":
return 100
else:
return 1.0
def get_scalar_asset_spot_value(self, asset, field, dt, data_frequency):
if field == "volume":
return 100
else:
return 1.0
def get_history_window(self, assets, end_dt, bar_count, frequency, field,
data_frequency, ffill=True):
end_idx = self.trading_calendar.all_sessions.searchsorted(end_dt)
days = self.trading_calendar.all_sessions[
(end_idx - bar_count + 1):(end_idx + 1)
]
df = pd.DataFrame(
np.full((bar_count, len(assets)), 100.0),
index=days,
columns=assets
)
if frequency == "1m" and not df.empty:
df = df.reindex(
self.trading_calendar.minutes_for_sessions_in_range(
df.index[0],
df.index[-1],
),
method='ffill',
)
return df
class FetcherDataPortal(DataPortal):
"""
Mock dataportal that returns fake data for history and non-fetcher
spot value.
"""
def __init__(self, asset_finder, trading_calendar, first_trading_day=None):
super(FetcherDataPortal, self).__init__(asset_finder, trading_calendar,
first_trading_day)
def get_spot_value(self, asset, field, dt, data_frequency):
# if this is a fetcher field, exercise the regular code path
if self._is_extra_source(asset, field, self._augmented_sources_map):
return super(FetcherDataPortal, self).get_spot_value(
asset, field, dt, data_frequency)
# otherwise just return a fixed value
return int(asset)
# XXX: These aren't actually the methods that are used by the superclasses,
# so these don't do anything, and this class will likely produce unexpected
# results for history().
def _get_daily_window_for_sid(self, asset, field, days_in_window,
extra_slot=True):
return np.arange(days_in_window, dtype=np.float64)
def _get_minute_window_for_asset(self, asset, field, minutes_for_window):
return np.arange(minutes_for_window, dtype=np.float64)
class tmp_assets_db(object):
"""Create a temporary assets sqlite database.
This is meant to be used as a context manager.
Parameters
----------
url : string
The URL for the database connection.
**frames
The frames to pass to the AssetDBWriter.
By default this maps equities:
('A', 'B', 'C') -> map(ord, 'ABC')
See Also
--------
empty_assets_db
tmp_asset_finder
"""
_default_equities = sentinel('_default_equities')
def __init__(self,
url='sqlite:///:memory:',
equities=_default_equities,
**frames):
self._url = url
self._eng = None
if equities is self._default_equities:
equities = make_simple_equity_info(
list(map(ord, 'ABC')),
pd.Timestamp(0),
pd.Timestamp('2015'),
)
frames['equities'] = equities
self._frames = frames
self._eng = None # set in enter and exit
def __enter__(self):
self._eng = eng = create_engine(self._url)
AssetDBWriter(eng).write(**self._frames)
return eng
def __exit__(self, *excinfo):
assert self._eng is not None, '_eng was not set in __enter__'
self._eng.dispose()
self._eng = None
def empty_assets_db():
"""Context manager for creating an empty assets db.
See Also
--------
tmp_assets_db
"""
return tmp_assets_db(equities=None)
class tmp_asset_finder(tmp_assets_db):
"""Create a temporary asset finder using an in memory sqlite db.
Parameters
----------
url : string
The URL for the database connection.
finder_cls : type, optional
The type of asset finder to create from the assets db.
**frames
Forwarded to ``tmp_assets_db``.
See Also
--------
tmp_assets_db
"""
def __init__(self,
url='sqlite:///:memory:',
finder_cls=AssetFinder,
future_chain_predicates=None,
**frames):
self._finder_cls = finder_cls
self._future_chain_predicates = future_chain_predicates
super(tmp_asset_finder, self).__init__(url=url, **frames)
def __enter__(self):
return self._finder_cls(
super(tmp_asset_finder, self).__enter__(),
future_chain_predicates=self._future_chain_predicates,
)
def empty_asset_finder():
"""Context manager for creating an empty asset finder.
See Also
--------
empty_assets_db
tmp_assets_db
tmp_asset_finder
"""
return tmp_asset_finder(equities=None)
class SubTestFailures(AssertionError):
def __init__(self, *failures):
self.failures = failures
@staticmethod
def _format_exc(exc_info):
# we need to do this weird join-split-join to ensure that the full
# message is indented by 4 spaces
return '\n '.join(''.join(format_exception(*exc_info)).splitlines())
def __str__(self):
return 'failures:\n %s' % '\n '.join(
'\n '.join((
', '.join('%s=%r' % item for item in scope.items()),
self._format_exc(exc_info),
)) for scope, exc_info in self.failures
)
@nottest
def subtest(iterator, *_names):
"""
Construct a subtest in a unittest.
Consider using ``zipline.testing.parameter_space`` when subtests
are constructed over a single input or over the cross-product of multiple
inputs.
``subtest`` works by decorating a function as a subtest. The decorated
function will be run by iterating over the ``iterator`` and *unpacking the
values into the function. If any of the runs fail, the result will be put
into a set and the rest of the tests will be run. Finally, if any failed,
all of the results will be dumped as one failure.
Parameters
----------
iterator : iterable[iterable]
The iterator of arguments to pass to the function.
*name : iterator[str]
The names to use for each element of ``iterator``. These will be used
to print the scope when a test fails. If not provided, it will use the
integer index of the value as the name.
Examples
--------
::
class MyTest(TestCase):
def test_thing(self):
# Example usage inside another test.
@subtest(([n] for n in range(100000)), 'n')
def subtest(n):
self.assertEqual(n % 2, 0, 'n was not even')
subtest()
@subtest(([n] for n in range(100000)), 'n')
def test_decorated_function(self, n):
# Example usage to parameterize an entire function.
self.assertEqual(n % 2, 1, 'n was not odd')
Notes
-----
We use this when we:
* Will never want to run each parameter individually.
* Have a large parameter space we are testing
(see tests/utils/test_events.py).
``nose_parameterized.expand`` will create a test for each parameter
combination which bloats the test output and makes the travis pages slow.
We cannot use ``unittest2.TestCase.subTest`` because nose, pytest, and
nose2 do not support ``addSubTest``.
See Also
--------
zipline.testing.parameter_space
"""
def dec(f):
@wraps(f)
def wrapped(*args, **kwargs):
names = _names
failures = []
for scope in iterator:
scope = tuple(scope)
try:
f(*args + scope, **kwargs)
except Exception:
info = sys.exc_info()
if not names:
names = count()
failures.append((dict(zip(names, scope)), info))
if failures:
raise SubTestFailures(*failures)
return wrapped
return dec
class MockDailyBarReader(object):
def __init__(self, dates):
self.sessions = pd.DatetimeIndex(dates)
def load_raw_arrays(self, columns, start, stop, sids):
dates = self.sessions
if start < dates[0]:
raise ValueError('start date is out of bounds for this reader')
if stop > dates[-1]:
raise ValueError('stop date is out of bounds for this reader')
output_dates = dates[(dates >= start) & (dates <= stop)]
return [
np.full((len(output_dates), len(sids)), 100.0)
for _ in columns
]
def get_value(self, col, sid, dt):
return 100.0
def create_mock_adjustment_data(splits=None, dividends=None, mergers=None):
if splits is None:
splits = create_empty_splits_mergers_frame()
elif not isinstance(splits, pd.DataFrame):
splits = pd.DataFrame(splits)
if mergers is None:
mergers = create_empty_splits_mergers_frame()
elif not isinstance(mergers, pd.DataFrame):
mergers = pd.DataFrame(mergers)
if dividends is None:
dividends = create_empty_dividends_frame()
elif not isinstance(dividends, pd.DataFrame):
dividends = pd.DataFrame(dividends)
return splits, mergers, dividends
def assert_timestamp_equal(left, right, compare_nat_equal=True, msg=""):
"""
Assert that two pandas Timestamp objects are the same.
Parameters
----------
left, right : pd.Timestamp
The values to compare.
compare_nat_equal : bool, optional
Whether to consider `NaT` values equal. Defaults to True.
msg : str, optional
A message to forward to `pd.util.testing.assert_equal`.
"""
if compare_nat_equal and left is pd.NaT and right is pd.NaT:
return
return pd.util.testing.assert_equal(left, right, msg=msg)
def powerset(values):
"""
Return the power set (i.e., the set of all subsets) of entries in `values`.
"""
return concat(combinations(values, i) for i in range(len(values) + 1))
def to_series(knowledge_dates, earning_dates):
"""
Helper for converting a dict of strings to a Series of datetimes.
This is just for making the test cases more readable.
"""
return pd.Series(
index=pd.to_datetime(knowledge_dates),
data=pd.to_datetime(earning_dates),
)
def gen_calendars(start, stop, critical_dates):
"""
Generate calendars to use as inputs.
"""
all_dates = pd.date_range(start, stop, tz='utc')
for to_drop in map(list, powerset(critical_dates)):
# Have to yield tuples.
yield (all_dates.drop(to_drop),)
# Also test with the trading calendar.
trading_days = get_calendar("NYSE").all_days
yield (trading_days[trading_days.slice_indexer(start, stop)],)
@contextmanager
def temp_pipeline_engine(calendar, sids, random_seed, symbols=None):
"""
A contextManager that yields a SimplePipelineEngine holding a reference to
an AssetFinder generated via tmp_asset_finder.
Parameters
----------
calendar : pd.DatetimeIndex
Calendar to pass to the constructed PipelineEngine.
sids : iterable[int]
Sids to use for the temp asset finder.
random_seed : int
Integer used to seed instances of SeededRandomLoader.
symbols : iterable[str], optional
Symbols for constructed assets. Forwarded to make_simple_equity_info.
"""
equity_info = make_simple_equity_info(
sids=sids,
start_date=calendar[0],
end_date=calendar[-1],
symbols=symbols,
)
loader = make_seeded_random_loader(random_seed, calendar, sids)
def get_loader(column):
return loader
with tmp_asset_finder(equities=equity_info) as finder:
yield SimplePipelineEngine(get_loader, calendar, finder)
def bool_from_envvar(name, default=False, env=None):
"""
Get a boolean value from the environment, making a reasonable attempt to
convert "truthy" values to True and "falsey" values to False.
Strings are coerced to bools using ``json.loads(s.lower())``.
Parameters
----------
name : str
Name of the environment variable.
default : bool, optional
Value to use if the environment variable isn't set. Default is False
env : dict-like, optional
Mapping in which to look up ``name``. This is a parameter primarily for
testing purposes. Default is os.environ.
Returns
-------
value : bool
``env[name]`` coerced to a boolean, or ``default`` if ``name`` is not
in ``env``.
"""
if env is None:
env = os.environ
value = env.get(name)
if value is None:
return default
try:
# Try to parse as JSON. This makes strings like "0", "False", and
# "null" evaluate as falsey values.
value = json.loads(value.lower())
except ValueError:
# If the value can't be parsed as json, assume it should be treated as
# a string for the purpose of evaluation.
pass
return bool(value)
_FAIL_FAST_DEFAULT = bool_from_envvar('PARAMETER_SPACE_FAIL_FAST')
def parameter_space(__fail_fast=_FAIL_FAST_DEFAULT, **params):
"""
Wrapper around subtest that allows passing keywords mapping names to
iterables of values.
The decorated test function will be called with the cross-product of all
possible inputs
Examples
--------
>>> from unittest import TestCase
>>> class SomeTestCase(TestCase):
... @parameter_space(x=[1, 2], y=[2, 3])
... def test_some_func(self, x, y):
... # Will be called with every possible combination of x and y.
... self.assertEqual(somefunc(x, y), expected_result(x, y))
See Also
--------
zipline.testing.subtest
"""
def decorator(f):
argspec = getargspec(f)
if argspec.varargs:
raise AssertionError("parameter_space() doesn't support *args")
if argspec.keywords:
raise AssertionError("parameter_space() doesn't support **kwargs")
if argspec.defaults:
raise AssertionError("parameter_space() doesn't support defaults.")
# Skip over implicit self.
argnames = argspec.args
if argnames[0] == 'self':
argnames = argnames[1:]
extra = set(params) - set(argnames)
if extra:
raise AssertionError(
"Keywords %s supplied to parameter_space() are "
"not in function signature." % extra
)
unspecified = set(argnames) - set(params)
if unspecified:
raise AssertionError(
"Function arguments %s were not "
"supplied to parameter_space()." % unspecified
)
def make_param_sets():
return product(*(params[name] for name in argnames))
def clean_f(self, *args, **kwargs):
try:
f(self, *args, **kwargs)
finally:
self.tearDown()
self.setUp()
if __fail_fast:
@wraps(f)
def wrapped(self):
for args in make_param_sets():
clean_f(self, *args)
return wrapped
else:
@wraps(f)
def wrapped(*args, **kwargs):
subtest(make_param_sets(), *argnames)(clean_f)(*args, **kwargs)
return wrapped
return decorator
def create_empty_dividends_frame():
return pd.DataFrame(
np.array(
[],
dtype=[
('ex_date', 'datetime64[ns]'),
('pay_date', 'datetime64[ns]'),
('record_date', 'datetime64[ns]'),
('declared_date', 'datetime64[ns]'),
('amount', 'float64'),
('sid', 'int32'),
],
),
index=pd.DatetimeIndex([], tz='UTC'),
)
def create_empty_splits_mergers_frame():
return pd.DataFrame(
np.array(
[],
dtype=[
('effective_date', 'int64'),
('ratio', 'float64'),
('sid', 'int64'),
],
),
index=pd.DatetimeIndex([]),
)
def make_alternating_boolean_array(shape, first_value=True):
"""
Create a 2D numpy array with the given shape containing alternating values
of False, True, False, True,... along each row and each column.
Examples
--------
>>> make_alternating_boolean_array((4,4))
array([[ True, False, True, False],
[False, True, False, True],
[ True, False, True, False],
[False, True, False, True]], dtype=bool)
>>> make_alternating_boolean_array((4,3), first_value=False)
array([[False, True, False],
[ True, False, True],
[False, True, False],
[ True, False, True]], dtype=bool)
"""
if len(shape) != 2:
raise ValueError(
'Shape must be 2-dimensional. Given shape was {}'.format(shape)
)
alternating = np.empty(shape, dtype=np.bool)
for row in alternating:
row[::2] = first_value
row[1::2] = not(first_value)
first_value = not(first_value)
return alternating
def make_cascading_boolean_array(shape, first_value=True):
"""
Create a numpy array with the given shape containing cascading boolean
values, with `first_value` being the top-left value.
Examples
--------
>>> make_cascading_boolean_array((4,4))
array([[ True, True, True, False],
[ True, True, False, False],
[ True, False, False, False],
[False, False, False, False]], dtype=bool)
>>> make_cascading_boolean_array((4,2))
array([[ True, False],
[False, False],
[False, False],
[False, False]], dtype=bool)
>>> make_cascading_boolean_array((2,4))
array([[ True, True, True, False],
[ True, True, False, False]], dtype=bool)
"""
if len(shape) != 2:
raise ValueError(
'Shape must be 2-dimensional. Given shape was {}'.format(shape)
)
cascading = np.full(shape, not(first_value), dtype=np.bool)
ending_col = shape[1] - 1
for row in cascading:
if ending_col > 0:
row[:ending_col] = first_value
ending_col -= 1
else:
break
return cascading
@expect_dimensions(array=2)
def permute_rows(seed, array):
"""
Shuffle each row in ``array`` based on permutations generated by ``seed``.
Parameters
----------
seed : int
Seed for numpy.RandomState
array : np.ndarray[ndim=2]
Array over which to apply permutations.
"""
rand = np.random.RandomState(seed)
return np.apply_along_axis(rand.permutation, 1, array)
@nottest
def make_test_handler(testcase, *args, **kwargs):
"""
Returns a TestHandler which will be used by the given testcase. This
handler can be used to test log messages.
Parameters
----------
testcase: unittest.TestCase
The test class in which the log handler will be used.
*args, **kwargs
Forwarded to the new TestHandler object.
Returns
-------
handler: logbook.TestHandler
The handler to use for the test case.
"""
handler = TestHandler(*args, **kwargs)
testcase.addCleanup(handler.close)
return handler
def write_compressed(path, content):
"""
Write a compressed (gzipped) file to `path`.
"""
with gzip.open(path, 'wb') as f:
f.write(content)
def read_compressed(path):
"""
Write a compressed (gzipped) file from `path`.
"""
with gzip.open(path, 'rb') as f:
return f.read()
zipline_git_root = abspath(
join(realpath(dirname(__file__)), '..', '..'),
)
@nottest
def test_resource_path(*path_parts):
return os.path.join(zipline_git_root, 'tests', 'resources', *path_parts)
@contextmanager
def patch_os_environment(remove=None, **values):
"""
Context manager for patching the operating system environment.
"""
old_values = {}
remove = remove or []
for key in remove:
old_values[key] = os.environ.pop(key)
for key, value in values.iteritems():
old_values[key] = os.getenv(key)
os.environ[key] = value
try:
yield
finally:
for old_key, old_value in old_values.iteritems():
if old_value is None:
# Value was not present when we entered, so del it out if it's
# still present.
try:
del os.environ[key]
except KeyError:
pass
else:
# Restore the old value.
os.environ[old_key] = old_value
class tmp_dir(TempDirectory, object):
"""New style class that wrapper for TempDirectory in python 2.
"""
pass
class _TmpBarReader(with_metaclass(ABCMeta, tmp_dir)):
"""A helper for tmp_bcolz_equity_minute_bar_reader and
tmp_bcolz_equity_daily_bar_reader.
Parameters
----------
days : pd.DatetimeIndex
The days to write for.
data : dict[int -> pd.DataFrame]
The data to write.
path : str, optional
The path to the directory to write the data into. If not given, this
will be a unique name.
"""
@abstractproperty
def _reader_cls(self):
raise NotImplementedError('_reader')
@abstractmethod
def _write(self, cal, days, path, data):
raise NotImplementedError('_write')
def __init__(self, cal, days, data, path=None):
super(_TmpBarReader, self).__init__(path=path)
self._cal = cal
self._days = days
self._data = data
def __enter__(self):
tmpdir = super(_TmpBarReader, self).__enter__()
try:
self._write(
self._cal,
self._days,
tmpdir.path,
self._data,
)
return self._reader_cls(tmpdir.path)
except BaseException: # Clean up even on KeyboardInterrupt
self.__exit__(None, None, None)
raise
class tmp_bcolz_equity_minute_bar_reader(_TmpBarReader):
"""A temporary BcolzMinuteBarReader object.
Parameters
----------
cal : TradingCalendar
The trading calendar for which we're writing data.
days : pd.DatetimeIndex
The days to write for.
data : iterable[(int, pd.DataFrame)]
The data to write.
path : str, optional
The path to the directory to write the data into. If not given, this
will be a unique name.
See Also
--------
tmp_bcolz_equity_daily_bar_reader
"""
_reader_cls = BcolzMinuteBarReader
_write = staticmethod(write_bcolz_minute_data)
class tmp_bcolz_equity_daily_bar_reader(_TmpBarReader):
"""A temporary BcolzDailyBarReader object.
Parameters
----------
cal : TradingCalendar
The trading calendar for which we're writing data.
days : pd.DatetimeIndex
The days to write for.
data : dict[int -> pd.DataFrame]
The data to write.
path : str, optional
The path to the directory to write the data into. If not given, this
will be a unique name.
See Also
--------
tmp_bcolz_equity_daily_bar_reader
"""
_reader_cls = BcolzDailyBarReader
@staticmethod
def _write(cal, days, path, data):
BcolzDailyBarWriter(path, days).write(data)
@contextmanager
def patch_read_csv(url_map, module=pd, strict=False):
"""Patch pandas.read_csv to map lookups from url to another.
Parameters
----------
url_map : mapping[str or file-like object -> str or file-like object]
The mapping to use to redirect read_csv calls.
module : module, optional
The module to patch ``read_csv`` on. By default this is ``pandas``.
This should be set to another module if ``read_csv`` is early-bound
like ``from pandas import read_csv`` instead of late-bound like:
``import pandas as pd; pd.read_csv``.
strict : bool, optional
If true, then this will assert that ``read_csv`` is only called with
elements in the ``url_map``.
"""
read_csv = pd.read_csv
def patched_read_csv(filepath_or_buffer, *args, **kwargs):
if filepath_or_buffer in url_map:
return read_csv(url_map[filepath_or_buffer], *args, **kwargs)
elif not strict:
return read_csv(filepath_or_buffer, *args, **kwargs)
else:
raise AssertionError(
'attempted to call read_csv on %r which not in the url map' %
filepath_or_buffer,
)
with patch.object(module, 'read_csv', patched_read_csv):
yield
@curry
def ensure_doctest(f, name=None):
"""Ensure that an object gets doctested. This is useful for instances
of objects like curry or partial which are not discovered by default.
Parameters
----------
f : any
The thing to doctest.
name : str, optional
The name to use in the doctest function mapping. If this is None,
Then ``f.__name__`` will be used.
Returns
-------
f : any
``f`` unchanged.
"""
sys._getframe(2).f_globals.setdefault('__test__', {})[
f.__name__ if name is None else name
] = f
return f
class RecordBatchBlotter(SimulationBlotter):
"""Blotter that tracks how its batch_order method was called.
"""
def __init__(self):
super(RecordBatchBlotter, self).__init__()
self.order_batch_called = []
def batch_order(self, *args, **kwargs):
self.order_batch_called.append((args, kwargs))
return super(RecordBatchBlotter, self).batch_order(*args, **kwargs)
class AssetID(CustomFactor):
"""
CustomFactor that returns the AssetID of each asset.
Useful for providing a Factor that produces a different value for each
asset.
"""
window_length = 1
inputs = ()
def compute(self, today, assets, out):
out[:] = assets
class AssetIDPlusDay(CustomFactor):
window_length = 1
inputs = ()
def compute(self, today, assets, out):
out[:] = assets + today.day
class OpenPrice(CustomFactor):
window_length = 1
inputs = [EquityPricing.open]
def compute(self, today, assets, out, open):
out[:] = open
def prices_generating_returns(returns, starting_price):
"""Construct the time series of prices that produce the given returns.
Parameters
----------
returns : np.ndarray[float]
The returns that these prices generate.
starting_price : float
The value of the asset.
Returns
-------
prices : np.ndaray[float]
The prices that generate the given returns. This array will be one
element longer than ``returns`` and ``prices[0] == starting_price``.
"""
raw_prices = starting_price * (1 + np.append([0], returns)).cumprod()
rounded_prices = raw_prices.round(3)
if not np.allclose(raw_prices, rounded_prices):
raise ValueError(
'Prices only have 3 decimal places of precision. There is no valid'
' price series that generate these returns.',
)
return rounded_prices
def random_tick_prices(starting_price,
count,
tick_size=0.01,
tick_range=(-5, 7),
seed=42):
"""
Construct a time series of prices that ticks by a random multiple of
``tick_size`` every period.
Parameters
----------
starting_price : float
The first price of the series.
count : int
Number of price observations to return.
tick_size : float
Unit of price movement between observations.
tick_range : (int, int)
Pair of lower/upper bounds for different in the number of ticks
between price observations.
seed : int, optional
Seed to use for random number generation.
"""
out = np.full(count, starting_price, dtype=float)
rng = np.random.RandomState(seed)
diff = rng.randint(tick_range[0], tick_range[1], size=len(out) - 1)
ticks = starting_price + diff.cumsum() * tick_size
out[1:] = ticks
return out
def simulate_minutes_for_day(open_,
high,
low,
close,
volume,
trading_minutes=390,
random_state=None):
"""Generate a random walk of minute returns which meets the given OHLCV
profile for an asset. The volume will be evenly distributed through the
day.
Parameters
----------
open_ : float
The day's open.
high : float
The day's high.
low : float
The day's low.
close : float
The day's close.
volume : float
The day's volume.
trading_minutes : int, optional
The number of minutes to simulate.
random_state : numpy.random.RandomState, optional
The random state to use. If not provided, the global numpy state is
used.
"""
if random_state is None:
random_state = np.random
sub_periods = 5
values = (random_state.rand(trading_minutes * sub_periods) - 0.5).cumsum()
values *= (high - low) / (values.max() - values.min())
values += np.linspace(
open_ - values[0],
close - values[-1],
len(values),
)
assert np.allclose(open_, values[0])
assert np.allclose(close, values[-1])
max_ = max(close, open_)
where = values > max_
values[where] = (
(values[where] - max_) *
(high - max_) /
(values.max() - max_) +
max_
)
min_ = min(close, open_)
where = values < min_
values[where] = (
(values[where] - min_) *
(low - min_) /
(values.min() - min_) +
min_
)
if not (np.allclose(values.max(), high) and
np.allclose(values.min(), low)):
return simulate_minutes_for_day(
open_,
high,
low,
close,
volume,
trading_minutes,
random_state=random_state,
)
prices = pd.Series(values.round(3)).groupby(
np.arange(trading_minutes).repeat(sub_periods),
)
base_volume, remainder = divmod(volume, trading_minutes)
volume = np.full(trading_minutes, base_volume, dtype='int64')
volume[:remainder] += 1
# TODO: add in volume
return pd.DataFrame({
'open': prices.first(),
'close': prices.last(),
'high': prices.max(),
'low': prices.min(),
'volume': volume,
})
def create_simple_domain(start, end, country_code):
"""Create a new pipeline domain with a simple date_range index.
"""
return EquitySessionDomain(pd.date_range(start, end), country_code)
def write_hdf5_daily_bars(writer,
asset_finder,
country_codes,
generate_data,
generate_currency_codes):
"""Write an HDF5 file of pricing data using an HDF5DailyBarWriter.
"""
asset_finder = asset_finder
for country_code in country_codes:
sids = asset_finder.equities_sids_for_country_code(country_code)
# XXX: The contract for generate_data is that it should return an
# iterator of (sid, df) pairs with entry for each sid in `sids`, and
# the contract for `generate_currency_codes` is that it should return a
# series indexed by the sids it receives.
#
# Unfortunately, some of our tests that were written before the
# introduction of multiple markets (in particular, the ones that use
# EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE), provide a function that always
# returns the same iterator, regardless of the provided `sids`, which
# means there are cases where the sids in `data` don't match the sids
# in `currency_codes`, which causes an assertion failure in
# `write_from_sid_df_pairs`.
#
# The correct fix for this is to update those old tests to respect
# `sids` (most likely by updating `make_equity_minute_bar_sids` to
# support multiple countries). But that requires updating a lot of
# tests, so for now, we call `generate_data` and use the sids it
# produces to determine what to pass to `generate_country_codes`.
data = list(generate_data(country_code=country_code, sids=sids))
data_sids = [p[0] for p in data]
currency_codes = generate_currency_codes(
country_code=country_code,
sids=data_sids,
)
writer.write_from_sid_df_pairs(
country_code,
iter(data),
currency_codes=currency_codes,
)
def exchange_info_for_domains(domains):
"""
Build an exchange_info suitable for passing to an AssetFinder from a list
of EquityCalendarDomain.
"""
return pd.DataFrame.from_records([
{'exchange': domain.calendar.name, 'country_code': domain.country_code}
for domain in domains
]) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/testing/core.py | core.py |
import numpy as np
from zipline.pipeline.factors.factor import CustomFactor
from zipline.pipeline.classifiers.classifier import CustomClassifier
from zipline.utils.idbox import IDBox
from .predicates import assert_equal
class CheckWindowsMixin(object):
params = ('expected_windows',)
def compute(self, today, assets, out, input_, expected_windows):
for asset, expected_by_day in expected_windows:
expected_by_day = expected_by_day.ob
col_ix = np.searchsorted(assets, asset)
if assets[col_ix] != asset:
raise AssertionError('asset %s is not in the window' % asset)
try:
expected = expected_by_day[today]
except KeyError:
pass
else:
expected = np.asanyarray(expected)
actual = input_[:, col_ix]
assert_equal(actual, expected,
array_decimal=(6 if expected.dtype.kind == 'f'
else None))
# output is just latest
out[:] = input_[-1]
class CheckWindowsClassifier(CheckWindowsMixin, CustomClassifier):
"""A custom classifier that makes assertions about the lookback windows that
it gets passed.
Parameters
----------
input_ : Term
The input term to the classifier.
window_length : int
The length of the lookback window.
expected_windows : dict[int, dict[pd.Timestamp, np.ndarray]]
For each asset, for each day, what the expected lookback window is.
Notes
-----
The output of this classifier is the same as ``Latest``. Any assets or days
not in ``expected_windows`` are not checked.
"""
def __new__(cls, input_, window_length, expected_windows):
if input_.dtype.kind == 'V':
dtype = np.dtype('O')
else:
dtype = input_.dtype
return super(CheckWindowsClassifier, cls).__new__(
cls,
inputs=[input_],
dtype=dtype,
window_length=window_length,
expected_windows=frozenset(
(k, IDBox(v)) for k, v in expected_windows.items()
),
)
class CheckWindowsFactor(CheckWindowsMixin, CustomFactor):
"""A custom factor that makes assertions about the lookback windows that
it gets passed.
Parameters
----------
input_ : Term
The input term to the factor.
window_length : int
The length of the lookback window.
expected_windows : dict[int, dict[pd.Timestamp, np.ndarray]]
For each asset, for each day, what the expected lookback window is.
Notes
-----
The output of this factor is the same as ``Latest``. Any assets or days
not in ``expected_windows`` are not checked.
"""
def __new__(cls, input_, window_length, expected_windows):
return super(CheckWindowsFactor, cls).__new__(
cls,
inputs=[input_],
dtype=input_.dtype,
window_length=window_length,
expected_windows=frozenset(
(k, IDBox(v)) for k, v in expected_windows.items()
),
) | zipline-trader | /zipline_trader-1.6.1-cp36-cp36m-win32.whl/zipline/testing/pipeline_terms.py | pipeline_terms.py |
.. image:: https://media.quantopian.com/logos/open_source/zipline-logo-03_.png
:target: https://www.zipline.io
:width: 212px
:align: center
:alt: Zipline
=============
|Gitter|
|pypi version status|
|pypi pyversion status|
|travis status|
|appveyor status|
|Coverage Status|
Zipline is a Pythonic algorithmic trading library. It is an event-driven
system for backtesting. Zipline is currently used in production as the backtesting and live-trading
engine powering `Quantopian <https://www.quantopian.com>`_ -- a free,
community-centered, hosted platform for building and executing trading
strategies. Quantopian also offers a `fully managed service for professionals <https://factset.quantopian.com>`_
that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.
- `Join our Community! <https://groups.google.com/forum/#!forum/zipline>`_
- `Documentation <https://www.zipline.io>`_
- Want to Contribute? See our `Development Guidelines <https://www.zipline.io/development-guidelines>`_
Features
========
- **Ease of Use:** Zipline tries to get out of your way so that you can
focus on algorithm development. See below for a code example.
- **"Batteries Included":** many common statistics like
moving average and linear regression can be readily accessed from
within a user-written algorithm.
- **PyData Integration:** Input of historical data and output of performance statistics are
based on Pandas DataFrames to integrate nicely into the existing
PyData ecosystem.
- **Statistics and Machine Learning Libraries:** You can use libraries like matplotlib, scipy,
statsmodels, and sklearn to support development, analysis, and
visualization of state-of-the-art trading systems.
Installation
============
Zipline currently supports Python 2.7, 3.5, and 3.6, and may be installed via
either pip or conda.
**Note:** Installing Zipline is slightly more involved than the average Python
package. See the full `Zipline Install Documentation`_ for detailed
instructions.
For a development installation (used to develop Zipline itself), create and
activate a virtualenv, then run the ``etc/dev-install`` script.
Quickstart
==========
See our `getting started tutorial <https://www.zipline.io/beginner-tutorial>`_.
The following code implements a simple dual moving average algorithm.
.. code:: python
from zipline.api import order_target, record, symbol
def initialize(context):
context.i = 0
context.asset = symbol('AAPL')
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# data.history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1d").mean()
long_mavg = data.history(context.asset, 'price', bar_count=300, frequency="1d").mean()
# Trading logic
if short_mavg > long_mavg:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.asset, 100)
elif short_mavg < long_mavg:
order_target(context.asset, 0)
# Save values for later inspection
record(AAPL=data.current(context.asset, 'price'),
short_mavg=short_mavg,
long_mavg=long_mavg)
You can then run this algorithm using the Zipline CLI.
First, you must download some sample pricing and asset data:
.. code:: bash
$ zipline ingest
$ zipline run -f dual_moving_average.py --start 2014-1-1 --end 2018-1-1 -o dma.pickle --no-benchmark
This will download asset pricing data data sourced from Quandl, and stream it through the algorithm over the specified time range.
Then, the resulting performance DataFrame is saved in ``dma.pickle``, which you can load and analyze from within Python.
You can find other examples in the ``zipline/examples`` directory.
Questions?
==========
If you find a bug, feel free to `open an issue <https://github.com/quantopian/zipline/issues/new>`_ and fill out the issue template.
Contributing
============
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome. Details on how to set up a development environment can be found in our `development guidelines <https://www.zipline.io/development-guidelines>`_.
If you are looking to start working with the Zipline codebase, navigate to the GitHub `issues` tab and start looking through interesting issues. Sometimes there are issues labeled as `Beginner Friendly <https://github.com/quantopian/zipline/issues?q=is%3Aissue+is%3Aopen+label%3A%22Beginner+Friendly%22>`_ or `Help Wanted <https://github.com/quantopian/zipline/issues?q=is%3Aissue+is%3Aopen+label%3A%22Help+Wanted%22>`_.
Feel free to ask questions on the `mailing list <https://groups.google.com/forum/#!forum/zipline>`_ or on `Gitter <https://gitter.im/quantopian/zipline>`_.
.. note::
Please note that Zipline is not a community-led project. Zipline is
maintained by the Quantopian engineering team, and we are quite small and
often busy.
Because of this, we want to warn you that we may not attend to your pull
request, issue, or direct mention in months, or even years. We hope you
understand, and we hope that this note might help reduce any frustration or
wasted time.
.. |Gitter| image:: https://badges.gitter.im/Join%20Chat.svg
:target: https://gitter.im/quantopian/zipline?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge
.. |pypi version status| image:: https://img.shields.io/pypi/v/zipline.svg
:target: https://pypi.python.org/pypi/zipline
.. |pypi pyversion status| image:: https://img.shields.io/pypi/pyversions/zipline.svg
:target: https://pypi.python.org/pypi/zipline
.. |travis status| image:: https://travis-ci.org/quantopian/zipline.svg?branch=master
:target: https://travis-ci.org/quantopian/zipline
.. |appveyor status| image:: https://ci.appveyor.com/api/projects/status/3dg18e6227dvstw6/branch/master?svg=true
:target: https://ci.appveyor.com/project/quantopian/zipline/branch/master
.. |Coverage Status| image:: https://coveralls.io/repos/quantopian/zipline/badge.svg
:target: https://coveralls.io/r/quantopian/zipline
.. _`Zipline Install Documentation` : https://www.zipline.io/install
| zipline | /zipline-1.4.1.tar.gz/zipline-1.4.1/README.rst | README.rst |
Contributing to Zipline
=======================
For developers of Zipline, people who want to contribute to the Zipline codebase or documentation, or people who want to install from source and make local changes to their copy of Zipline, please refer to the `Development Guidelines`__ if you would like to contribute.
All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome. We `track issues`__ on `GitHub`__ and also have a `mailing list`__ where you can ask questions.
__ https://www.zipline.io/development-guidelines.html
__ https://github.com/quantopian/zipline/issues
__ https://github.com/
__ https://groups.google.com/forum/#!forum/zipline
| zipline | /zipline-1.4.1.tar.gz/zipline-1.4.1/CONTRIBUTING.rst | CONTRIBUTING.rst |
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