updating ROI Agents graph

app.py

@@ -572,7 +572,7 @@ with demo:
                 gr.Markdown("# 2-weeks rolling average ROI for Pearl agents")
             with gr.Row():
                 pearl_rolling_avg_plot = plot_rolling_average_roi(
-                    weekly_roi_df=weekly_metrics_by_market_creator,
+                    traders_data=traders_data,
                     pearl_agents=pearl_agents_df,
                 )
             with gr.Row():

tabs/agent_graphs.py

@@ -77,26 +77,22 @@ def get_sevenday_rolling_average(daa_df: pd.DataFrame) -> pd.DataFrame:
 
 
 def plot_rolling_average_roi(
-    weekly_roi_df: pd.DataFrame, pearl_agents: pd.DataFrame
+    traders_data: pd.DataFrame, pearl_agents: pd.DataFrame
 ) -> gr.Plot:
     """Function to plot the rolling average of ROI for pearl agents"""
     # Get the list of unique addresses from the daa_pearl_df
     unique_addresses = pearl_agents["safe_address"].unique()
     # Filter the weekly_roi_df to include only those addresses
-    filtered_weekly_roi_df = weekly_roi_df[
-        weekly_roi_df["trader_address"].isin(unique_addresses)
+    filtered_traders_data = traders_data[
+        traders_data["trader_address"].isin(unique_addresses)
     ]
-    # Select only the columns: "roi", "month_year_week", "trader_address"
-    filtered_weekly_roi_df = filtered_weekly_roi_df[
-        ["roi", "month_year_week", "trader_address"]
-    ].copy()
-    # Remove duplicates
-    filtered_weekly_roi_df = filtered_weekly_roi_df.drop_duplicates(
-        subset=["month_year_week", "trader_address"]
-    )
+    # create the date column
+    filtered_traders_data["creation_date"] = filtered_traders_data[
+        "creation_timestamp"
+    ].dt.date
 
     # Get the 2-week rolling average of ROI
-    rolling_avg_roi_df = get_twoweeks_rolling_average_roi(filtered_weekly_roi_df)
+    rolling_avg_roi_df = get_twoweeks_rolling_average_roi(filtered_traders_data)
     print(rolling_avg_roi_df.head())
     # Ensure 'month_year_week' is a column, not an index
     if "month_year_week" not in rolling_avg_roi_df.columns:
@@ -116,24 +112,18 @@ def plot_rolling_average_roi(
     )
 
 
-def get_twoweeks_rolling_average_roi(weekly_roi_df: pd.DataFrame) -> pd.DataFrame:
+def get_twoweeks_rolling_average_roi(traders_data: pd.DataFrame) -> pd.DataFrame:
     """Function to get the 2-week rolling average of the ROI by market_creator and total"""
-    # Create a local copy of the dataframe
-    local_df = weekly_roi_df.copy()
 
-    # Convert string dates to datetime
-    local_df["month_year_week"] = pd.to_datetime(
-        local_df["month_year_week"], format="%b-%d-%Y"
-    )
-    # Sort the dataframe by date
-    local_df = local_df.sort_values(by="month_year_week").set_index("month_year_week")
+    # Aggregate ROI at the date level
+    daily_avg = traders_data.groupby("creation_date")["roi"].mean().reset_index()
+    daily_avg = daily_avg.set_index("creation_date")
+    # Now resample and rolling
+    weekly_avg = daily_avg.resample("W").mean()
+    rolling_avg = weekly_avg.rolling(window=2).mean().reset_index()
 
-    # Group by market_creator and calculate rolling average of unique trader_address
-    trader_rolling_avg_roi = (
-        local_df.resample("W")["roi"].mean().rolling(window=2).mean().reset_index()
-    )
-    trader_rolling_avg_roi.rename(columns={"roi": "rolling_avg_roi"}, inplace=True)
-    return trader_rolling_avg_roi
+    rolling_avg.rename(columns={"roi": "rolling_avg_roi"}, inplace=True)
+    return rolling_avg
 
 
 def get_weekly_average_roi(weekly_roi_df: pd.DataFrame) -> pd.DataFrame: