File size: 11,151 Bytes
d8cf478 efabdf9 d8cf478 52d1750 d8cf478 dc11fb3 d8cf478 dc11fb3 330cbe3 dc11fb3 d8cf478 efabdf9 d8cf478 efabdf9 dc98cf9 d41146f 6154c13 db08a72 6154c13 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 |
import gradio as gr
import pandas as pd
import plotly.express as px
trader_metric_choices = [
"mech calls",
"bet amount",
"earnings",
"net earnings",
"ROI",
"nr_trades",
]
default_trader_metric = "ROI"
def get_metrics_text() -> gr.Markdown:
metric_text = """
## Metrics at 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)
def get_interpretation_text() -> gr.Markdown:
interpretation_text = """
## Meaning of KL-divergence values
* Y = 0.05129
* Market accuracy off by 5%
* Y = 0.1053
* Market accuracy off by 10%
* Y = 0.2876
* Market accuracy off by 25%
* Y = 0.5108
* Market accuracy off by 40%
* Y = 1.2040
* Market accuracy off by 70%
* Y = 2.3026
* Market accuracy off by 90%
"""
return gr.Markdown(interpretation_text)
def plot_trader_metrics_by_market_creator(
metric_name: str, traders_df: pd.DataFrame
) -> gr.Plot:
"""Plots the weekly trader metrics."""
if metric_name == "mech calls":
metric_name = "mech_calls"
column_name = "nr_mech_calls"
yaxis_title = "Total nr of mech calls per trader"
elif metric_name == "ROI":
column_name = "roi"
yaxis_title = "Total ROI (net profit/cost)"
elif metric_name == "bet amount":
metric_name = "bet_amount"
column_name = metric_name
yaxis_title = "Total bet amount per trader (xDAI)"
elif metric_name == "net earnings":
metric_name = "net_earnings"
column_name = metric_name
yaxis_title = "Total net profit per trader (xDAI)"
elif metric_name == "nr_trades":
column_name = metric_name
yaxis_title = "Total nr of trades per trader"
else: # earnings
column_name = metric_name
yaxis_title = "Total gross profit per trader (xDAI)"
traders_filtered = traders_df[["month_year_week", "market_creator", column_name]]
fig = px.box(
traders_filtered,
x="month_year_week",
y=column_name,
color="market_creator",
color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
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")
return gr.Plot(
value=fig,
)
def plot_trader_daily_metrics_by_market_creator(
metric_name: str, traders_df: pd.DataFrame
) -> gr.Plot:
"""Plots the daily trader metrics."""
if metric_name == "mech calls":
metric_name = "mech_calls"
column_name = "nr_mech_calls"
yaxis_title = "Total nr of mech calls per trader"
elif metric_name == "ROI":
column_name = "roi"
yaxis_title = "Total ROI (net profit/cost)"
elif metric_name == "bet amount":
metric_name = "bet_amount"
column_name = metric_name
yaxis_title = "Total bet amount per trader (xDAI)"
elif metric_name == "net earnings":
metric_name = "net_earnings"
column_name = metric_name
yaxis_title = "Total net profit per trader (xDAI)"
elif metric_name == "nr_trades":
column_name = metric_name
yaxis_title = "Total nr of trades per trader"
else: # earnings
column_name = metric_name
yaxis_title = "Total gross profit per trader (xDAI)"
traders_filtered = traders_df[["creation_date", "market_creator", column_name]]
fig = px.box(
traders_filtered,
x="creation_date",
y=column_name,
color="market_creator",
color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
category_orders={"market_creator": ["pearl", "quickstart", "all"]},
)
fig.update_traces(boxmean=True)
fig.update_layout(
xaxis_title="Day",
yaxis_title=yaxis_title,
legend=dict(yanchor="top", y=0.5),
)
fig.update_xaxes(tickformat="%b %d\n%Y")
return gr.Plot(
value=fig,
)
def plot_median_roi_by_creation_date(traders_df: pd.DataFrame) -> gr.Plot:
traders_df["creation_date"] = traders_df["creation_timestamp"].dt.date
traders_all = traders_df.copy(deep=True)
traders_all["market_creator"] = "all"
# merging both dataframes
final_traders = pd.concat([traders_all, traders_df], ignore_index=True)
final_traders = final_traders.sort_values(by="creation_date", ascending=True)
roi_daily_metrics = (
final_traders.groupby(
["creation_date", "market_creator", "trader_address"], sort=False
)
.agg(
median_roi=("roi", "median"),
mean_roi=("roi", "mean"),
total_trades=("roi", "count"),
)
.reset_index()
)
# Create the scatter plot with facets for each market_creator
fig = px.scatter(
roi_daily_metrics,
x="creation_date",
y="median_roi",
facet_col="market_creator",
color="market_creator",
color_discrete_map={
"pearl": "purple",
"quickstart": "goldenrod",
"all": "darkgreen",
},
title="Median ROI Over Time by Market Creator",
labels={
"creation_date": "Creation Date",
"median_roi": "Median ROI (%)",
"market_creator": "Market Creator",
},
hover_data={
"creation_date": "|%B %d, %Y", # Custom date format in hover
"median_roi": True,
"mean_roi": True,
"total_trades": True,
},
category_orders={"market_creator": ["pearl", "quickstart", "all"]},
# trendline=None, # Ensure no trendlines are added
)
# Customize the layout for better aesthetics
fig.update_layout(
template="plotly_white",
hovermode="closest",
showlegend=False, # Disable the legend as each facet has its own context
)
# Update each subplot's x-axis to share the same range
fig.update_xaxes(matches="x") # Link x-axes across facets
fig.update_yaxes(matches="y") # Link y-axes across facets
# Add a vertical dashed line in dark red at the specified date
vline_date = "2024-09-29"
vline_datetime = pd.to_datetime(vline_date, format="%Y-%m-%d")
fig.add_vline(
x=vline_datetime,
line_dash="dash",
line_color="darkred",
)
return gr.Plot(
value=fig,
)
import plotly.express as px
def create_median_roi_plot(roi_daily_metrics):
"""
Creates a Plotly scatter plot for median ROI over time, colored by market_creator.
Parameters:
- roi_daily_metrics (pd.DataFrame): Aggregated ROI metrics with columns:
['creation_date', 'market_creator', 'trader_address', 'median_roi', 'mean_roi', 'total_trades']
Returns:
- fig (plotly.graph_objs._figure.Figure): The Plotly figure object.
"""
# Ensure 'creation_date' is in datetime format
roi_daily_metrics["creation_date"] = pd.to_datetime(
roi_daily_metrics["creation_date"]
)
# Create the line plot with scatter markers
fig = px.line(
roi_daily_metrics,
x="creation_date",
y="median_roi",
color="market_creator",
markers=True, # Add markers to lines
title="Median ROI Over Time by Market Creator",
labels={
"creation_date": "Creation Date",
"median_roi": "Median ROI (%)",
"market_creator": "Market Creator",
},
hover_data={
"creation_date": "|%B %d, %Y", # Custom date format in hover
"median_roi": True,
"mean_roi": True,
"total_trades": True,
},
)
# Customize the layout for better aesthetics
fig.update_layout(
xaxis_title="Creation Date",
yaxis_title="Median ROI (%)",
legend_title="Market Creator",
template="plotly_white",
hovermode="x unified",
)
# Optional: Add vertical lines for specific events (e.g., "multibet release")
# Example:
# fig.add_vline(
# x=pd.to_datetime("2023-01-02"),
# line_dash="dash",
# line_color="red",
# annotation_text="Multibet Release",
# annotation_position="top left",
# annotation=dict(
# bgcolor="white",
# font_size=12,
# font_color="red"
# )
# )
return fig
def plot_trader_metrics_by_trader_type(metric_name: str, traders_df: pd.DataFrame):
"""Plots the weekly trader metrics."""
if metric_name == "mech calls":
metric_name = "mech_calls"
column_name = "nr_mech_calls"
yaxis_title = "Total nr of mech calls per trader"
elif metric_name == "ROI":
column_name = "roi"
yaxis_title = "Total ROI (net profit/cost)"
elif metric_name == "bet amount":
metric_name = "bet_amount"
column_name = metric_name
yaxis_title = "Total bet amount per trader (xDAI)"
elif metric_name == "net earnings":
metric_name = "net_earnings"
column_name = metric_name
yaxis_title = "Total net profit per trader (xDAI)"
else: # earnings
column_name = metric_name
yaxis_title = "Total gross profit per trader (xDAI)"
traders_filtered = traders_df[["month_year_week", "trader_type", column_name]]
fig = px.box(
traders_filtered,
x="month_year_week",
y=column_name,
color="trader_type",
color_discrete_sequence=["gray", "orange", "darkblue"],
category_orders={"trader_type": ["singlebet", "multibet", "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")
return gr.Plot(
value=fig,
)
def plot_winning_metric_per_trader(traders_winning_df: pd.DataFrame) -> gr.Plot:
fig = px.box(
traders_winning_df,
x="month_year_week",
y="winning_perc",
color="market_creator",
color_discrete_sequence=["purple", "goldenrod", "darkgreen"],
category_orders={"market_creator": ["pearl", "quickstart", "all"]},
)
fig.update_traces(boxmean=True)
fig.update_layout(
xaxis_title="Week",
yaxis_title="Weekly winning percentage %",
legend=dict(yanchor="top", y=0.5),
width=1000, # Adjusted for better fit on laptop screens
height=600, # Adjusted for better fit on laptop screens
)
fig.update_xaxes(tickformat="%b %d\n%Y")
return gr.Plot(
value=fig,
)
|