Update app.py

app.py

@@ -74,8 +74,8 @@ def trading_strategy(data, base_window=60, base_alpha=1.0, beta=0.1, trend_windo
     positions = []
     trend = data.rolling(window=trend_window).mean()
 
-    # For indices where the trend is not fully defined, append NaNs and no position.
     for i in range(len(data)):
+        # For early indices where the trend isn't fully defined, append NaNs and no position
         if i < trend_window - 1:
             buy_signals.append(np.nan)
             sell_signals.append(np.nan)
@@ -85,7 +85,7 @@ 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])
-        # Convert each to float to ensure scalar comparison
+        # 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])
@@ -106,8 +106,10 @@ 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
 def calculate_performance(data, positions):
-    returns = np.diff(data) / data[:-1]
+    data_np = data.to_numpy()
+    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
     return equity_curve