Adding new plot for daily active agents

app.py

@@ -26,6 +26,7 @@ from tabs.trader_plots import (
     get_interpretation_text,
     plot_total_bet_amount,
     plot_active_traders,
+    plot_rolling_average,
 )
 from tabs.daily_graphs import (
     get_current_week_data,
@@ -628,6 +629,14 @@ with demo:
                 active_traders_plot_qs = plot_active_traders(
                     active_traders, market_creator="quickstart"
                 )
+            with gr.Row():
+                gr.Markdown(" # Daily active traders")
+            with gr.Row():
+                gr.Markdown("## Daily active traders by market categories")
+            with gr.Row():
+                rolling_avg_plot = plot_rolling_average(
+                    active_traders, market_creator="all"
+                )
 
         with gr.TabItem("📉 Markets Kullback–Leibler divergence"):
             with gr.Row():

tabs/trader_plots.py

@@ -301,6 +301,99 @@ def plot_total_bet_amount(
     )
 
 
+def get_sevenday_rolling_average(active_traders_df: pd.DataFrame) -> pd.DataFrame:
+    """Function to get the 7-day rolling average of the number of unique trader_address by market_creator and total"""
+    # Create a local copy of the dataframe
+    local_df = active_traders_df.copy()
+
+    # Convert string dates to datetime
+    local_df["creation_date"] = pd.to_datetime(
+        local_df["creation_date"], format="%b-%d-%Y"
+    )
+    # Sort the dataframe by date
+    local_df = local_df.sort_values(by="creation_date")
+
+    # Group by market_creator and calculate rolling average of unique trader_address
+    rolling_avg_by_market = (
+        local_df.groupby("market_creator")
+        .apply(
+            lambda group: group.set_index("creation_date")
+            .resample("D")["trader_address"]
+            .nunique()
+            .rolling(window=7)
+            .mean()
+        )
+        .reset_index(name="rolling_avg_traders")
+    )
+
+    # Calculate the total rolling average across all market_creators
+    local_df["market_creator"] = "all"
+    rolling_avg_total = (
+        local_df.groupby("market_creator")
+        .apply(
+            lambda group: group.set_index("creation_date")
+            .resample("D")["trader_address"]
+            .nunique()
+            .rolling(window=7)
+            .mean()
+        )
+        .reset_index(name="rolling_avg_traders")
+    )
+
+    # Combine both results
+    combined_rolling_avg = pd.concat(
+        [rolling_avg_by_market, rolling_avg_total], ignore_index=True
+    )
+
+    return combined_rolling_avg
+
+
+def plot_rolling_average(
+    active_traders_df: pd.DataFrame,
+    market_creator: str = None,
+) -> gr.Plot:
+    """Function to plot the rolling average of active traders for the different categories and markets"""
+
+    rolling_avg_df = get_sevenday_rolling_average(active_traders_df)
+
+    if market_creator is not None:
+        rolling_avg_df = rolling_avg_df.loc[
+            rolling_avg_df["market_creator"] == market_creator
+        ]
+
+    # Convert string dates to datetime and sort them
+    all_dates_dt = sorted(
+        [
+            datetime.strptime(date, "%b-%d-%Y")
+            for date in rolling_avg_df["creation_date"].unique()
+        ]
+    )
+    # Convert back to string format
+    all_dates = [date.strftime("%b-%d-%Y") for date in all_dates_dt]
+    fig = px.line(
+        rolling_avg_df,
+        x="creation_date",
+        y="rolling_avg_traders",
+        color="market_creator",
+        color_discrete_sequence=color_mapping,
+        category_orders={
+            "market_creator": ["pearl", "quickstart", "all"],
+        },
+    )
+    fig.update_layout(
+        xaxis_title="Date",
+        yaxis_title="7-day rolling average of active traders",
+        legend=dict(yanchor="top", y=0.5),
+    )
+
+    fig.update_xaxes(tickformat="%b %d")
+    # Update layout to force x-axis category order (hotfix for a sorting issue)
+    fig.update_layout(xaxis={"categoryorder": "array", "categoryarray": all_dates})
+    return gr.Plot(
+        value=fig,
+    )
+
+
 def plot_active_traders(
     active_traders_data: pd.DataFrame,
     market_creator: str = None,