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import pandas as pd
import gradio as gr
import plotly.express as px
import gc
from datetime import datetime
trade_metric_choices = [
"mech calls",
"collateral amount",
"earnings",
"net earnings",
"ROI",
]
tool_metric_choices = {
"Weekly Mean Mech Tool Accuracy as (Accurate Responses/All) %": "win_perc",
"Total Weekly Inaccurate Nr of Mech Tool Responses": "losses",
"Total Weekly Accurate Nr of Mech Tool Responses": "wins",
"Total Weekly Nr of Mech Tool Requests": "total_request",
}
default_trade_metric = "ROI"
default_tool_metric = "Weekly Mean Mech Tool Accuracy as (Accurate Responses/All) %"
HEIGHT = 600
WIDTH = 1000
def get_boxplot_metrics(column_name: str, trades_df: pd.DataFrame) -> pd.DataFrame:
trades_filtered = trades_df[
["creation_timestamp", "month_year_week", "market_creator", column_name]
]
# adding the total
trades_filtered_all = trades_df.copy(deep=True)
trades_filtered_all["market_creator"] = "all"
# merging both dataframes
all_filtered_trades = pd.concat(
[trades_filtered, trades_filtered_all], ignore_index=True
)
all_filtered_trades = all_filtered_trades.sort_values(
by="creation_timestamp", ascending=True
)
gc.collect()
return all_filtered_trades
def plot_trade_metrics(
metric_name: str, trades_df: pd.DataFrame, trader_filter: str = None
) -> gr.Plot:
"""Plots the trade metrics."""
if metric_name == "mech calls":
metric_name = "mech_calls"
column_name = "num_mech_calls"
yaxis_title = "Nr of mech calls per trade"
elif metric_name == "ROI":
column_name = "roi"
yaxis_title = "ROI (net profit/cost)"
elif metric_name == "collateral amount":
metric_name = "collateral_amount"
column_name = metric_name
yaxis_title = "Collateral amount per trade (xDAI)"
elif metric_name == "net earnings":
metric_name = "net_earnings"
column_name = metric_name
yaxis_title = "Net profit per trade (xDAI)"
else: # earnings
column_name = metric_name
yaxis_title = "Gross profit per trade (xDAI)"
color_discrete = ["purple", "darkgoldenrod", "darkgreen"]
if trader_filter == "Olas":
trades_filtered = get_boxplot_metrics(
column_name, trades_df.loc[trades_df["staking"] != "non_Olas"]
)
color_discrete = ["darkviolet", "goldenrod", "green"]
elif trader_filter == "non_Olas":
trades_filtered = get_boxplot_metrics(
column_name, trades_df.loc[trades_df["staking"] == "non_Olas"]
)
else:
trades_filtered = get_boxplot_metrics(column_name, trades_df)
# Convert string dates to datetime and sort them
all_dates_dt = sorted(
[
datetime.strptime(date, "%b-%d-%Y")
for date in trades_filtered["month_year_week"].unique()
]
)
# Convert back to string format
all_dates = [date.strftime("%b-%d-%Y") for date in all_dates_dt]
fig = px.box(
trades_filtered,
x="month_year_week",
y=column_name,
color="market_creator",
color_discrete_sequence=color_discrete,
category_orders={"market_creator": ["pearl", "quickstart", "all"]},
)
fig.update_traces(boxmean=True)
fig.update_layout(
xaxis_title="Week",
yaxis_title=yaxis_title,
legend=dict(yanchor="top", y=0.5),
)
fig.update_xaxes(tickformat="%b %d\n%Y")
# Update layout to force x-axis category order (hotfix for a sorting issue)
fig.update_layout(xaxis={"categoryorder": "array", "categoryarray": all_dates})
return gr.Plot(
value=fig,
)
def get_trade_metrics_text(trader_type: str = None) -> gr.Markdown:
if trader_type is None:
metric_text = """
## Description of the graph
These metrics are computed weekly. The statistical measures are:
* min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
* the upper and lower fences to delimit possible outliers
* the average values as the dotted lines
"""
elif trader_type == "Olas":
metric_text = """
## Definition of Olas trader
Agents using Mech, with a service ID and the corresponding safe in the registry
## Description of the graph
These metrics are computed weekly. The statistical measures are:
* min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
* the upper and lower fences to delimit possible outliers
* the average values as the dotted lines
"""
elif trader_type == "non_Olas":
metric_text = """
## Definition of non-Olas trader
Agents using Mech, with no service ID
## Description of the graph
These metrics are computed weekly. The statistical measures are:
* min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
* the upper and lower fences to delimit possible outliers
* the average values as the dotted lines
"""
else: # Unclassified
metric_text = """
## Definition of unclassified trader
Agents (safe/EOAs) not using Mechs
## Description of the graph
These metrics are computed weekly. The statistical measures are:
* min, max, 25th(q1), 50th(median) and 75th(q2) percentiles
* the upper and lower fences to delimit possible outliers
* the average values as the dotted lines
"""
return gr.Markdown(metric_text)
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