Update app.py

app.py

@@ -55,21 +55,21 @@ with st.expander("Click here to read more about the methodology", expanded=False
     """)
 
 if st.sidebar.button('Run Analysis'):
-    # Updated get_data: use "Close" and squeeze the result to ensure a 1D Series.
-    def get_data(ticker, start, end):
-        data = yf.download(ticker, start=start, end=end)
-        return data['Close'].squeeze()
-    
-    stock_data = get_data(ticker, start_date, end_date)
+    stock_data = yf.download(ticker, start=start_date, end=end_date)
     
     if not stock_data.empty:
-        stock_data['Returns'] = stock_data.pct_change() if isinstance(stock_data, pd.Series) else stock_data['Close'].pct_change()
+        # Use "Close" since adjusted close no longer exists
+        stock_data = stock_data['Close'].squeeze()
+        stock_data = pd.Series(stock_data)  # ensure it is a Series
+        stock_data['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:]
-        date_range = pd.date_range(plot_data.index[-1] + pd.DateOffset(1), periods=time_horizon, freq='D')
+        # Convert last date to Timestamp before adding DateOffset
+        last_date = pd.to_datetime(plot_data.index[-1])
+        date_range = pd.date_range(last_date + pd.DateOffset(1), periods=time_horizon, freq='D')
         
         st.markdown("""
         ### Method 1: Dynamic Volatility