Update app.py

app.py

@@ -25,17 +25,21 @@ def identify_signals(data):
                          ((data['Close'] > data['MA5']) & (data['Close'].shift(1) < data['MA5']))
     data['Sell Signal'] = ((data['Close'] > data['Upper Band']) & (data['Close'].shift(1) < data['Upper Band'])) | \
                           ((data['Close'] < data['MA5']) & (data['Close'].shift(1) > data['MA5']))
+    
+    # To properly track the price at which a signal occurs, we ensure signal markers are associated with the price
+    data['Signal Price'] = data.apply(lambda row: row['Close'] if row['Buy Signal'] or row['Sell Signal'] else np.nan, axis=1)
     return data
 
 def signal_table(data):
-    # Creating a DataFrame for signal events
+    # Extracting signal events and necessary information
     signals = pd.DataFrame()
     signals['Date'] = data.index
-    signals['Price'] = data['Close']
+    signals['Price'] = data['Signal Price']
     signals['Signal'] = np.where(data['Buy Signal'], 'Buy', np.where(data['Sell Signal'], 'Sell', ''))
-    signals = signals[signals['Signal'] != '']  # Filter out non-signal days
+    signals = signals.dropna(subset=['Price'])  # Remove rows where no signal occurred
     return signals
 
+
 def plot_data(data):
     plt.figure(figsize=(10, 5))
     plt.plot(data['Close'], label='Close Price')