Update app.py

app.py

@@ -48,10 +48,11 @@ with st.sidebar.expander("Asset Settings", expanded=True):
     start_date = st.date_input("Start Date", value=pd.to_datetime("2020-01-01"), help="Select the start date for historical data.")
     end_date = st.date_input("End Date", value=datetime.today() + pd.DateOffset(1), help="Select the end date for historical data.")
 
+# Modified get_data: squeeze the 'Close' column to ensure a 1D Series
 @st.cache_data
 def get_data(ticker, start, end):
     data = yf.download(ticker, start=start, end=end)
-    return data['Close']
+    return data['Close'].squeeze()
 
 def OU_parameters_ema(data, window):
     window = int(window)  # ensure window is scalar
@@ -75,7 +76,7 @@ def trading_strategy(data, base_window=60, base_alpha=1.0, beta=0.1, trend_windo
     trend = data.rolling(window=trend_window).mean()
 
     for i in range(len(data)):
-        # For early indices where the trend isn't fully defined, append NaNs and no position
+        # For early indices where the trend is not fully defined, skip trading logic.
         if i < trend_window - 1:
             buy_signals.append(np.nan)
             sell_signals.append(np.nan)
@@ -85,7 +86,7 @@ def trading_strategy(data, base_window=60, base_alpha=1.0, beta=0.1, trend_windo
         window = int(windows.iloc[i])
         mu, sigma = OU_parameters_ema(data[:i+1], window=window)
         alpha = base_alpha + beta * float(sigma.iloc[-1])
-        # Force each value to be a float to ensure scalar comparisons
+        # Force each value to be a float to ensure scalar comparisons:
         price = float(data.iloc[i])
         mu_value = float(mu.iloc[-1])
         sigma_value = float(sigma.iloc[-1])
@@ -106,9 +107,9 @@ def trading_strategy(data, base_window=60, base_alpha=1.0, beta=0.1, trend_windo
 
     return buy_signals, sell_signals, positions, trend
 
-# Updated calculate_performance: Convert data to NumPy array explicitly
+# Modified calculate_performance: explicitly convert the price Series to a 1D NumPy array.
 def calculate_performance(data, positions):
-    data_np = data.to_numpy()
+    data_np = data.to_numpy()  # ensure 1D array
     returns = np.diff(data_np) / data_np[:-1]
     strategy_returns = np.array(positions[:-1]) * returns
     equity_curve = np.cumprod(1 + strategy_returns) * 100  # starting equity of 100