Update app.py

app.py

@@ -67,10 +67,13 @@ def normalize(X, columns=['open','high','low','close']):
     Y = Rm.transform(X_copy, columns)
     return Y, Rm
 
-# Outlier removal
-def remove_outliers(x, epsilon):
-    z_score = stats.zscore(x['low'])
-    return x[z_score.abs() < epsilon]
+def remove_outliers(df, epsilon):
+    # compute z-scores as a NumPy array…
+    z = stats.zscore(df['low'])
+    # …then build a boolean mask via np.abs
+    mask = np.abs(z) < epsilon
+    # apply it back against the original DataFrame’s index
+    return df.loc[mask].reset_index(drop=True)
 
 # Advanced features
 def calculate_rsi(d, window=14):