updating weekly data

app.py

@@ -12,12 +12,12 @@ from scripts.metrics import (
 )
 from tabs.trader_plots import (
     plot_trader_metrics_by_market_creator,
-    plot_trader_metrics_by_trader_type,
     default_trader_metric,
     trader_metric_choices,
     get_metrics_text,
     plot_winning_metric_per_trader,
     get_interpretation_text,
+    plot_median_roi_by_creation_date,
 )
 
 from tabs.market_plots import (
@@ -157,6 +157,12 @@ with demo:
                 inputs=trader_details_selector,
                 outputs=trader_markets_plot,
             )
+            # with gr.Row():
+            #     gr.Markdown("# Daily Median ROI by market creator")
+            # with gr.Row():
+            #     daily_median = plot_median_roi_by_creation_date(
+            #         traders_df=trader_agents_data
+            #     )
 
         with gr.TabItem("📉Closed Markets Kullback–Leibler divergence"):
             with gr.Row():

tabs/trader_plots.py

@@ -85,11 +85,203 @@ def plot_trader_metrics_by_market_creator(
     )
     fig.update_xaxes(tickformat="%b %d\n%Y")
 
+    # # Add a vertical dashed line for the "multibet release"
+    # release_week = "Sep-29"
+    # # Extract unique categories to determine the position
+    # categories = traders_filtered["month_year_week"].unique()
+
+    # if release_week in categories:
+    #     # Determine the numerical position of the release_week
+    #     x_position = list(categories).index(release_week)
+
+    #     # Add a vertical dashed line using add_shape
+    #     fig.add_shape(
+    #         type="line",
+    #         x0=x_position,
+    #         x1=x_position,
+    #         y0=0,
+    #         y1=1,
+    #         xref="x",
+    #         yref="paper",
+    #         line=dict(color="darkred", dash="dash"),
+    #     )
+
+    #     # Add an annotation for the vertical line
+    #     fig.add_annotation(
+    #         x=x_position,
+    #         y=0.8,
+    #         xref="x",
+    #         yref="paper",
+    #         text="multibet release",
+    #         showarrow=True,
+    #         arrowhead=2,
+    #         ax=0,
+    #         ay=-40,
+    #         font=dict(color="darkred"),
+    #         bgcolor="gray",
+    #         bordercolor="darkred",
+    #         borderwidth=1,
+    #         borderpad=4,
+    #         align="center",
+    #     )
+    # fig.add_vline(
+    #     x=release_week,  # The specific week for the release
+    #     line_dash="dash",  # Dashed line style
+    #     line_color="darkred",  # Color of the vertical line
+    #     # annotation_text="multibet release",
+    #     # annotation_text="multibet release",  # Label for the vertical line
+    #     # annotation_position="top",  # Position of the annotation
+    #     # annotation=dict(
+    #     #     bgcolor="white",  # Background color for better readability
+    #     #     font_size=12,
+    #     #     font_color="red",
+    #     # ),
+    # )
+
     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."""