removing extra parameter

tabs/agent_graphs.py

@@ -88,7 +88,7 @@ def plot_rolling_average_roi(
     ]
     # create the date column
     filtered_traders_data["creation_timestamp"] = pd.to_datetime(
-        filtered_traders_data["creation_timestamp"], timezone="UTC"
+        filtered_traders_data["creation_timestamp"]
     )
     filtered_traders_data["creation_date"] = filtered_traders_data[
         "creation_timestamp"
@@ -117,30 +117,30 @@ def plot_rolling_average_roi(
 
 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 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"])
-    
+
     # Aggregate ROI at the date level
     daily_avg = local_df.groupby("creation_date")["roi"].mean().reset_index()
-    
+
     # Set the datetime index
     daily_avg = daily_avg.set_index("creation_date")
-    
+
     # Now resample and rolling average
     weekly_avg = daily_avg.resample("W").mean()
     rolling_avg = weekly_avg.rolling(window=2).mean().reset_index()
-    
+
     # Rename columns
-    rolling_avg.rename(columns={
-        "roi": "rolling_avg_roi",
-        "creation_date": "month_year_week"
-    }, inplace=True)
-    
+    rolling_avg.rename(
+        columns={"roi": "rolling_avg_roi", "creation_date": "month_year_week"},
+        inplace=True,
+    )
+
     return rolling_avg