File size: 6,922 Bytes
18ab870 |
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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 |
import pandas as pd
import gradio as gr
import gc
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from datetime import datetime, timedelta
from tqdm import tqdm
def plot_daily_trades(trades_df: pd.DataFrame) -> gr.Plot:
# get daily trades
daily_trades_count = (
trades_df.groupby("month_year_week").size().reset_index(name="trades")
)
daily_trades_count.columns = daily_trades_count.columns.astype(str)
print("WIP")
def get_current_week_data(trades_df: pd.DataFrame) -> pd.DataFrame:
# Get current date
now = datetime.now()
# Get start of the current week (Monday)
start_of_week = now - timedelta(days=now.weekday())
start_of_week = start_of_week.replace(hour=0, minute=0, second=0, microsecond=0)
print(f"start of the week = {start_of_week}")
# Get end of the current week (Sunday)
end_of_week = start_of_week + timedelta(days=6)
end_of_week = end_of_week.replace(hour=23, minute=59, second=59, microsecond=999999)
print(f"end of the week = {end_of_week}")
trades_df["creation_date"] = pd.to_datetime(trades_df["creation_date"])
# Filter the dataframe
return trades_df[
(trades_df["creation_date"] >= start_of_week)
& (trades_df["creation_date"] <= end_of_week)
]
def get_boxplot_daily_metrics(
column_name: str, trades_df: pd.DataFrame
) -> pd.DataFrame:
trades_filtered = trades_df[
[
"creation_timestamp",
"creation_date",
"market_creator",
"trader_address",
"staking",
column_name,
]
]
# adding the total
trades_filtered_all = trades_filtered.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_daily_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 = "bet_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)"
if trader_filter == "agent":
trades_filtered = trades_df.loc[trades_df["staking"] != "non_agent"]
elif trader_filter == "non_agent":
trades_filtered = trades_df.loc[trades_df["staking"] == "non_agent"]
else:
trades_filtered = trades_df
color_mapping = [
"darkviolet",
"purple",
"goldenrod",
"darkgoldenrod",
"green",
"darkgreen",
]
# Create binary staking category
trades_filtered["trader_type"] = trades_filtered["staking"].apply(
lambda x: "non_agent" if x == "non_agent" else "agent"
)
trades_filtered["trader_market"] = trades_filtered.apply(
lambda x: (x["trader_type"], x["market_creator"]), axis=1
)
fig = px.box(
trades_filtered,
x="creation_date",
y=column_name,
color="trader_market",
color_discrete_sequence=color_mapping,
category_orders={
"market_creator": ["pearl", "quickstart", "all"],
"trader_market": [
("agent", "pearl"),
("non_agent", "pearl"),
("agent", "quickstart"),
("non_agent", "quickstart"),
("agent", "all"),
("non_agent", "all"),
],
},
facet_col="market_creator",
)
fig.update_traces(boxmean=True)
fig.update_layout(
xaxis_title="Day",
yaxis_title=yaxis_title,
legend=dict(yanchor="top", y=0.5),
)
for axis in fig.layout:
if axis.startswith("xaxis"):
fig.layout[axis].update(title="Day")
fig.update_xaxes(tickformat="%b %d")
return gr.Plot(
value=fig,
)
def plot_daily_metrics_v2(
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"]
trades_filtered = get_boxplot_daily_metrics(column_name, trades_df)
fig = make_subplots(rows=1, cols=2, subplot_titles=("Agent", "Non-Agents"))
# Create first boxplot for staking=True
fig.add_trace(
go.Box(
x=trades_filtered[trades_filtered["staking"] != "non_agent"][
"creation_date"
],
y=trades_filtered[trades_filtered["staking"] != "non_agent"][column_name],
name="Trades from agents",
marker_color=color_discrete[0],
legendgroup="staking_true",
showlegend=True,
),
row=1,
col=1,
)
# Create second boxplot for staking=False
fig.add_trace(
go.Box(
x=trades_filtered[trades_filtered["staking"] == False]["creation_date"],
y=trades_filtered[trades_filtered["staking"] == False][column_name],
name="Staking False",
marker_color=color_discrete[1],
legendgroup="staking_false",
showlegend=True,
),
row=1,
col=2,
)
# Update layout
fig.update_layout(
height=600,
width=1200,
title_text=f"Box Plot of {column_name} by Staking Status",
showlegend=True,
)
# Update y-axes to have the same range
fig.update_yaxes(matches="y")
|