adding DAA for all traders

app.py

@@ -636,7 +636,7 @@ with demo:
                 gr.Markdown("## Daily active agents by market categories")
             with gr.Row():
                 rolling_avg_plot = plot_rolling_average(
-                    active_traders, market_creator="all"
+                    active_traders,
                 )
 
         with gr.TabItem("📉 Markets Kullback–Leibler divergence"):

tabs/trader_plots.py

@@ -301,7 +301,9 @@ def plot_total_bet_amount(
     )
 
 
-def get_sevenday_rolling_average(active_traders_df: pd.DataFrame) -> pd.DataFrame:
+def get_sevenday_rolling_average_by_market_creator(
+    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()
@@ -313,43 +315,66 @@ def get_sevenday_rolling_average(active_traders_df: pd.DataFrame) -> pd.DataFram
     # 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()
-        )
+    # Group by market_creator and creation_date, count unique traders
+    daily_traders = (
+        local_df.groupby(["market_creator", "creation_date"])["trader_address"]
+        .nunique()
         .reset_index()
     )
-    rolling_avg_by_market.rename(columns={0: "rolling_avg_traders"}, inplace=True)
+
+    # Calculate rolling average for each market_creator
+    rolling_avg_by_market = daily_traders.copy()
+    rolling_avg_by_market["rolling_avg_traders"] = rolling_avg_by_market.groupby(
+        "market_creator"
+    )["trader_address"].transform(lambda x: x.rolling(window=7).mean())
 
     # 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()
-        )
+    all_markets = daily_traders.copy()
+    all_markets["market_creator"] = "all"
+    all_markets = (
+        all_markets.groupby(["market_creator", "creation_date"])["trader_address"]
+        .sum()
         .reset_index()
     )
-    rolling_avg_total.rename(columns={0: "rolling_avg_traders"}, inplace=True)
+
+    all_markets["rolling_avg_traders"] = (
+        all_markets["trader_address"].rolling(window=7).mean()
+    )
 
     # Combine both results
     combined_rolling_avg = pd.concat(
-        [rolling_avg_by_market, rolling_avg_total], ignore_index=True
+        [rolling_avg_by_market, all_markets], ignore_index=True
     )
 
     return combined_rolling_avg
 
 
+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").set_index("creation_date")
+
+    # Group by market_creator and calculate rolling average of unique trader_address
+    rolling_avg_by_market = (
+        local_df.resample("D")["trader_address"]
+        .nunique()
+        .rolling(window=7)
+        .mean()
+        .reset_index()
+    )
+    rolling_avg_by_market.rename(
+        columns={"trader_address": "rolling_avg_traders"}, inplace=True
+    )
+    return rolling_avg_by_market
+
+
 def plot_rolling_average(
     active_traders_df: pd.DataFrame,
     market_creator: str = None,
@@ -357,30 +382,25 @@ def plot_rolling_average(
     """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)
-
+    print(rolling_avg_df.head())
     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(
+    # Ensure 'creation_date' is a column, not an index
+    if "creation_date" not in rolling_avg_df.columns:
+        rolling_avg_df = rolling_avg_df.reset_index()
+
+    fig = px.bar(
         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"],
-        },
+        # color="market_creator",
+        # color_discrete_sequence=color_mapping,
+        # category_orders={
+        #     "market_creator": ["pearl", "quickstart", "all"],
+        # },
     )
     fig.update_layout(
         xaxis_title="Date",
@@ -388,9 +408,6 @@ def plot_rolling_average(
         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,
     )