Update app.py

app.py

@@ -7,6 +7,8 @@ import pandas as pd
 import pandas_ta as ta
 import torch
 import warnings
+import matplotlib.pyplot as plt
+
 
 # Suppress specific FutureWarning
 warnings.filterwarnings("ignore", category=FutureWarning)
@@ -42,19 +44,14 @@ def fetch_stock_data(symbol, start_date, end_date):
 # Step 2: Add Technical Indicators
 def add_technical_indicators(data):
     print("Adding technical indicators...")
-    # Compute RSI
     data['RSI'] = ta.rsi(data['Close'], length=14)
-    
-    # Compute MACD (usually generates three columns: MACD, MACD_Signal, MACD_Histogram)
-    macd = ta.macd(data['Close'], fast=12, slow=26, signal=9)
-    data['MACD'] = macd['MACD_12_26_9']
-    
-    # Compute Bollinger Bands
+    data['MACD'], = ta.macd(data['Close'], fast=12, slow=26)['MACD_12_26_9]
     bbands = ta.bbands(data['Close'], length=20, std=2.0)
-    data['BB_upper'] = bbands[f'BBU_20_2.0'] if f'BBU_20_2.0' in bbands.columns else bbands.iloc[:, 0]
-    data['BB_middle'] = bbands[f'BBM_20_2.0'] if f'BBM_20_2.0' in bbands.columns else bbands.iloc[:, 1]
-    data['BB_lower'] = bbands[f'BBL_20_2.0'] if f'BBL_20_2.0' in bbands.columns else bbands.iloc[:, 2]
-    
+    # Handle potential missing columns
+    if 'BBU_20_2.0' in bbands.columns:
+        data['BB_upper'], data['BB_middle'], data['BB_lower'] = bbands['BBU_20_2.0'], bbands['BBM_20_2.0'], bbands['BBL_20_2.0']
+    else:
+        print("Bollinger Bands data not available.")
     return data
 
 # Step 3: Analyze Sentiment using FinBERT
@@ -75,13 +72,29 @@ def generate_prediction(prompt):
 
 # Step 5: Execute Trade with Alpaca
 def execute_trade(signal, symbol='Genting Bhd', qty=1):
-    print(f"Executing trade signal: {signal}")
-    if signal == "buy":
-        api.submit_order(symbol=symbol, qty=qty, side='buy', type='market', time_in_force='gtc')
-    elif signal == "sell":
-        api.submit_order(symbol=symbol, qty=qty, side='sell', type='market', time_in_force='gtc')
+    try:
+        print(f"Executing trade signal: {signal}")
+        if signal == "buy":
+            api.submit_order(symbol=symbol, qty=qty, side='buy', type='market', time_in_force='gtc')
+            print(f"Executed buy order for {qty} shares of {symbol}.")
+        elif signal == "sell":
+            api.submit_order(symbol=symbol, qty=qty, side='sell', type='market', time_in_force='gtc')
+            print(f"Executed sell order for {qty} shares of {symbol}.")
+    except Exception as e:
+        print(f"Error executing trade: {e}")
+
+# Step 6: Plot KLCI Data with Technical Indicators
+def plot_klci(data):
+    plt.figure(figsize=(14, 7))
+    plt.plot(data['Close'], label='KLCI Close Price', color='blue')
+    plt.plot(data['BB_upper'], label='Bollinger Upper Band', color='red')
+    plt.plot(data['BB_middle'], label='Bollinger Middle Band', color='green')
+    plt.plot(data['BB_lower'], label='Bollinger Lower Band', color='red')
+    plt.title('KLCI with Technical Indicators')
+    plt.legend()
+    plt.show()
 
-# Step 6: Main Function to Run Pipeline
+# Step 7: Main Function to Run Pipeline
 def main():
     # 1. Fetch and Prepare Data
     klci_data = fetch_stock_data(SYMBOL, START_DATE, END_DATE)
@@ -103,6 +116,10 @@ def main():
         execute_trade("sell", symbol=SYMBOL, qty=1)
     else:
         print("No clear trade signal from prediction.")
+    
+    # 5. Plot KLCI Data with Indicators
+    plot_klci(klci_data)
+
 
 # Run the main function
 if __name__ == "__main__":