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import gradio as gr
import pandas as pd
import plotly.express as px
HEIGHT = 400
WIDTH = 1100
def prepare_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
"""Prepares the trades data for analysis."""
trades_df["creation_timestamp"] = pd.to_datetime(trades_df["creation_timestamp"])
trades_df["creation_timestamp"] = trades_df["creation_timestamp"].dt.tz_convert(
"UTC"
)
trades_df = trades_df.sort_values(by="creation_timestamp", ascending=True)
trades_df["month_year"] = (
trades_df["creation_timestamp"].dt.to_period("M").astype(str)
)
trades_df["month_year_week"] = (
trades_df["creation_timestamp"].dt.to_period("W").dt.strftime("%b-%d")
)
trades_df["winning_trade"] = trades_df["winning_trade"].astype(int)
return trades_df
def get_overall_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
"""Gets the overall trades data"""
trades_count = trades_df.groupby("month_year_week").size().reset_index()
trades_count.columns = trades_count.columns.astype(str)
trades_count.rename(columns={"0": "trades"}, inplace=True)
return trades_count
def get_overall_by_market_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
"""Gets the overall trades data"""
trades_count = (
trades_df.groupby(["month_year_week", "market_creator"], sort=False)
.size()
.reset_index()
)
trades_count.columns = trades_count.columns.astype(str)
trades_count.rename(columns={"0": "trades"}, inplace=True)
return trades_count
def get_overall_winning_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
"""Gets the overall winning trades data for the given tools and calculates the winning percentage."""
winning_trades = (
trades_df.groupby(["month_year_week"])["winning_trade"].sum()
/ trades_df.groupby(["month_year_week"])["winning_trade"].count()
* 100
)
# winning_trades is a series, give it a dataframe
winning_trades = winning_trades.reset_index()
winning_trades.columns = winning_trades.columns.astype(str)
winning_trades.columns = ["month_year_week", "winning_trade"]
return winning_trades
def get_overall_winning_by_market_trades(trades_df: pd.DataFrame) -> pd.DataFrame:
"""Gets the overall winning trades data for the given tools and calculates the winning percentage."""
winning_trades = (
trades_df.groupby(["month_year_week", "market_creator"], sort=False)[
"winning_trade"
].sum()
/ trades_df.groupby(["month_year_week", "market_creator"], sort=False)[
"winning_trade"
].count()
* 100
)
# winning_trades is a series, give it a dataframe
winning_trades = winning_trades.reset_index()
winning_trades.columns = winning_trades.columns.astype(str)
winning_trades.columns = ["month_year_week", "market_creator", "winning_trade"]
return winning_trades
def plot_trades_by_week(trades_df: pd.DataFrame) -> gr.BarPlot:
"""Plots the trades data for the given tools and calculates the winning percentage."""
return gr.BarPlot(
value=trades_df,
x="month_year_week",
y="trades",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "trades"],
height=HEIGHT,
width=WIDTH,
)
def integrated_plot_trades_per_market_by_week(trades_df: pd.DataFrame) -> gr.Plot:
# adding the total
trades_all = trades_df.copy(deep=True)
trades_all["market_creator"] = "all"
# merging both dataframes
all_filtered_trades = pd.concat([trades_df, trades_all], ignore_index=True)
all_filtered_trades = all_filtered_trades.sort_values(
by="creation_timestamp", ascending=True
)
trades = get_overall_by_market_trades(all_filtered_trades)
fig = px.bar(
trades,
x="month_year_week",
y="trades",
color="market_creator",
barmode="group",
color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
category_orders={"market_creator": ["pearl", "quickstart", "all"]},
)
fig.update_layout(
xaxis_title="Week",
yaxis_title="Weekly nr of trades",
legend=dict(yanchor="top", y=0.5),
)
fig.update_layout(width=WIDTH, height=HEIGHT)
fig.update_xaxes(tickformat="%b %d\n%Y")
return gr.Plot(value=fig)
def integrated_plot_winning_trades_per_market_by_week(
trades_df: pd.DataFrame,
) -> gr.Plot:
# adding the total
trades_all = trades_df.copy(deep=True)
trades_all["market_creator"] = "all"
# merging both dataframes
all_filtered_trades = pd.concat([trades_df, trades_all], ignore_index=True)
all_filtered_trades = all_filtered_trades.sort_values(
by="creation_timestamp", ascending=True
)
final_df = get_overall_winning_by_market_trades(all_filtered_trades)
fig = px.bar(
final_df,
x="month_year_week",
y="winning_trade",
color="market_creator",
barmode="group",
color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
category_orders={"market_creator": ["pearl", "quickstart", "all"]},
)
fig.update_layout(
xaxis_title="Week",
yaxis_title="Weekly % of winning trades",
legend=dict(yanchor="top", y=0.5),
)
fig.update_layout(width=WIDTH, height=HEIGHT)
fig.update_xaxes(tickformat="%b %d\n%Y")
return gr.Plot(
value=fig,
)
def plot_winning_trades_by_week(trades_df: pd.DataFrame) -> gr.BarPlot:
"""Plots the winning trades data for the given tools and calculates the winning percentage."""
return gr.BarPlot(
value=trades_df,
x="month_year_week",
y="winning_trade",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "winning_trade"],
height=HEIGHT,
width=WIDTH,
)
def plot_winning_trades_per_market_by_week(
trades_df: pd.DataFrame, market_type: str
) -> gr.Plot:
"""Plots the winning trades data for the given tools and calculates the winning percentage."""
# if market_type is "all then no filter is applied"
if market_type == "quickstart":
trades = trades_df.loc[trades_df["market_creator"] == "quickstart"]
color_sequence = ["goldenrod"]
elif market_type == "pearl":
trades = trades_df.loc[trades_df["market_creator"] == "pearl"]
color_sequence = ["purple"]
else:
trades = trades_df
color_sequence = ["darkgreen"]
fig = px.bar(
trades,
x="month_year_week",
y="winning_trade",
color_discrete_sequence=color_sequence,
title=market_type + " winning trades",
)
fig.update_layout(
xaxis_title="Week",
yaxis_title="Weekly % of winning trades",
)
fig.update_xaxes(tickformat="%b %d\n%Y")
return gr.Plot(
value=fig,
)
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