Update app.py

app.py

@@ -55,19 +55,18 @@ with st.expander("Click here to read more about the methodology", expanded=False
     """)
 
 if st.sidebar.button('Run Analysis'):
+    # Download data; use "Close" and squeeze to ensure a 1D Series.
     stock_data = yf.download(ticker, start=start_date, end=end_date)
-    
     if not stock_data.empty:
-        # Use "Close" column; adjusted close no longer exists. Squeeze to ensure a 1D Series.
         stock_data = stock_data['Close'].squeeze()
-        stock_data = pd.Series(stock_data)  # Ensure it's a Pandas Series
-        stock_data['Returns'] = stock_data.pct_change()
+        # Compute returns separately (do not add to stock_data to avoid modifying the index)
+        returns = stock_data.pct_change()
         current_price = stock_data.iloc[-1]
 
         # Method 1: Volatility over dynamic periods
         fig1 = go.Figure()
         plot_data = stock_data[-rolling_window:]
-        # Convert last index to Timestamp to add DateOffset
+        # Ensure the last index is a Timestamp (avoid concatenation error)
         last_date = pd.to_datetime(plot_data.index[-1])
         date_range = pd.date_range(last_date + pd.DateOffset(1), periods=time_horizon, freq='D')