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import gradio as gr
import pandas as pd
HEIGHT = 600
WIDTH = 1000
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["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").astype(str)
)
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 for the given tools and calculates the winning percentage."""
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 for the given tools and calculates the winning percentage."""
trades_count = (
trades_df.groupby(["market_creator", "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_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(["market_creator", "month_year_week"])["winning_trade"].sum()
/ trades_df.groupby(["market_creator", "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 = ["market_creator", "month_year_week", "winning_trade"]
return winning_trades
def plot_trade_details(trade_detail: str, trades_df: pd.DataFrame) -> gr.LinePlot:
"""Plots the trade details for the given trade detail."""
if trade_detail == "mech calls":
# this is to filter out the data before 2023-09-01
trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
trades_filtered = (
trades_filtered.groupby("month_year_week")["num_mech_calls"]
.quantile([0.25, 0.5, 0.75])
.unstack()
)
trades_filtered.columns = trades_filtered.columns.astype(str)
trades_filtered.reset_index(inplace=True)
trades_filtered.columns = [
"month_year_week",
"25th_percentile",
"50th_percentile",
"75th_percentile",
]
# reformat the data as percentile, date, value
trades_filtered = trades_filtered.melt(
id_vars=["month_year_week"], var_name="percentile", value_name="mech_calls"
)
return gr.LinePlot(
value=trades_filtered,
x="month_year_week",
y="mech_calls",
color="percentile",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "percentile", "mech_calls"],
height=HEIGHT,
width=WIDTH,
)
if trade_detail == "collateral amount":
trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
trades_filtered = (
trades_filtered.groupby("month_year_week")["collateral_amount"]
.quantile([0.25, 0.5, 0.75])
.unstack()
)
trades_filtered.columns = trades_filtered.columns.astype(str)
trades_filtered.reset_index(inplace=True)
trades_filtered.columns = [
"month_year_week",
"25th_percentile",
"50th_percentile",
"75th_percentile",
]
# reformat the data as percentile, date, value
trades_filtered = trades_filtered.melt(
id_vars=["month_year_week"],
var_name="percentile",
value_name="collateral_amount",
)
return gr.LinePlot(
value=trades_filtered,
x="month_year_week",
y="collateral_amount",
color="percentile",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "percentile", "collateral_amount"],
height=HEIGHT,
width=WIDTH,
)
if trade_detail == "earnings":
trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
trades_filtered = (
trades_filtered.groupby("month_year_week")["earnings"]
.quantile([0.25, 0.5, 0.75])
.unstack()
)
trades_filtered.columns = trades_filtered.columns.astype(str)
trades_filtered.reset_index(inplace=True)
trades_filtered.columns = [
"month_year_week",
"25th_percentile",
"50th_percentile",
"75th_percentile",
]
# reformat the data as percentile, date, value
trades_filtered = trades_filtered.melt(
id_vars=["month_year_week"], var_name="percentile", value_name="earnings"
)
return gr.LinePlot(
value=trades_filtered,
x="month_year_week",
y="earnings",
color="percentile",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "percentile", "earnings"],
height=HEIGHT,
width=WIDTH,
)
if trade_detail == "net earnings":
trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
trades_filtered = (
trades_filtered.groupby("month_year_week")["net_earnings"]
.quantile([0.25, 0.5, 0.75])
.unstack()
)
trades_filtered.columns = trades_filtered.columns.astype(str)
trades_filtered.reset_index(inplace=True)
trades_filtered.columns = [
"month_year_week",
"25th_percentile",
"50th_percentile",
"75th_percentile",
]
# reformat the data as percentile, date, value
trades_filtered = trades_filtered.melt(
id_vars=["month_year_week"],
var_name="percentile",
value_name="net_earnings",
)
return gr.LinePlot(
value=trades_filtered,
x="month_year_week",
y="net_earnings",
color="percentile",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "percentile", "net_earnings"],
height=HEIGHT,
width=WIDTH,
)
if trade_detail == "ROI":
trades_filtered = trades_df[trades_df["creation_timestamp"] > "2023-09-01"]
trades_filtered = (
trades_filtered.groupby("month_year_week")["roi"]
.quantile([0.25, 0.5, 0.75])
.unstack()
)
trades_filtered.columns = trades_filtered.columns.astype(str)
trades_filtered.reset_index(inplace=True)
trades_filtered.columns = [
"month_year_week",
"25th_percentile",
"50th_percentile",
"75th_percentile",
]
# reformat the data as percentile, date, value
trades_filtered = trades_filtered.melt(
id_vars=["month_year_week"], var_name="percentile", value_name="ROI"
)
return gr.LinePlot(
value=trades_filtered,
x="month_year_week",
y="ROI",
color="percentile",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "percentile", "ROI"],
height=HEIGHT,
width=WIDTH,
)
def plot_average_roi_per_market_by_week(trades_df: pd.DataFrame) -> gr.LinePlot:
mean_roi_per_market_by_week = (
trades_df.groupby(["market_creator", "month_year_week"])["roi"]
.mean()
.reset_index()
)
mean_roi_per_market_by_week.rename(columns={"roi": "mean_roi"}, inplace=True)
return gr.LinePlot(
value=mean_roi_per_market_by_week,
x="month_year_week",
y="ROI",
color="market_creator",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "market_creator", "mean_roi"],
height=HEIGHT,
width=WIDTH,
)
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 plot_trades_per_market_by_week(trades_df: pd.DataFrame) -> gr.BarPlot:
"""Plots the trades data for the given tools and calculates the winning percentage."""
assert "market_creator" in trades_df.columns
return gr.BarPlot(
value=trades_df,
x="month_year_week",
y="trades",
color="market_creator",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "trades"],
height=HEIGHT,
width=WIDTH,
)
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) -> gr.BarPlot:
"""Plots the winning trades data for the given tools and calculates the winning percentage."""
assert "market_creator" in trades_df.columns
return gr.BarPlot(
value=trades_df,
x="month_year_week",
y="winning_trade",
color="market_creator",
show_label=True,
interactive=True,
show_actions_button=True,
tooltip=["month_year_week", "winning_trade"],
height=HEIGHT,
width=WIDTH,
)
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