updating weekly average roi

tabs/agent_graphs.py

@@ -137,58 +137,68 @@ def get_twoweeks_rolling_average_roi(traders_data: pd.DataFrame) -> pd.DataFrame
 
     # Rename columns
     rolling_avg.rename(
-        columns={"roi": "rolling_avg_roi", "creation_date": "month_year_week"},
+        columns={"roi": "rolling_avg_roi", "date": "creation_date"},
         inplace=True,
     )
 
     return rolling_avg
 
 
-def get_weekly_average_roi(weekly_roi_df: pd.DataFrame) -> pd.DataFrame:
-    """Function to get the weekly average ROI for pearl agents"""
-    # Create a local copy of the dataframe
-    local_df = weekly_roi_df.copy()
+def get_weekly_average_roi(traders_data: pd.DataFrame) -> pd.DataFrame:
+    """Function to get the weekly average ROI by market_creator and total"""
 
-    # Convert string dates to datetime
-    local_df["month_year_week"] = pd.to_datetime(
-        local_df["month_year_week"], format="%b-%d-%Y"
-    )
+    # Create a copy to avoid SettingWithCopyWarning
+    local_df = traders_data.copy()
+
+    # Ensure creation_date is datetime64[ns]
+    # Since creation_date comes from .dt.date, it's a date object, not datetime
+    local_df["creation_date"] = pd.to_datetime(local_df["creation_date"])
 
-    # Group by month_year_week and market_creator, then calculate the mean ROI
-    weekly_avg_roi = (
-        local_df.groupby(["month_year_week"], sort=False)["roi"].mean().reset_index()
+    # Aggregate ROI at the date level first
+    daily_avg = local_df.groupby("creation_date")["roi"].mean().reset_index()
+
+    # Set the datetime index
+    daily_avg = daily_avg.set_index("creation_date")
+
+    # Resample to weekly frequency and calculate mean
+    weekly_avg = daily_avg.resample("W").mean().reset_index()
+
+    # Rename columns for consistency
+    weekly_avg.rename(
+        columns={"date": "creation_date", "roi": "weekly_avg_roi"},
+        inplace=True,
     )
-    return weekly_avg_roi
+
+    return weekly_avg
 
 
 def plot_weekly_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 weekly average of ROI for pearl agents"""
     # Get the list of unique addresses from the daa_pearl_df
+    # 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_timestamp"] = pd.to_datetime(
+        filtered_traders_data["creation_timestamp"]
     )
+    filtered_traders_data["creation_date"] = filtered_traders_data[
+        "creation_timestamp"
+    ].dt.date
+
     # Get the weekly average ROI
-    weekly_avg_roi_df = get_weekly_average_roi(filtered_weekly_roi_df)
+    weekly_avg_roi_df = get_weekly_average_roi(filtered_traders_data)
     # plot the weekly average ROI
     print(weekly_avg_roi_df.head())
-    # Ensure 'month_year_week' is a column, not an index
-    if "month_year_week" not in weekly_avg_roi_df.columns:
-        weekly_avg_roi_df = weekly_avg_roi_df.reset_index()
+
     fig = px.line(
         weekly_avg_roi_df,
-        x="month_year_week",
+        x="creation_date",
         y="roi",
     )
     fig.update_layout(