Update app.py

app.py

@@ -7,55 +7,68 @@ import pandas as pd
 API_KEY = "QR8F9B7T6R2SWTAT"
 
 def fetch_alpha_vantage_data(api_key):
-    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=MANH&interval=5min&apikey={api_key}'
+    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=IBM&interval=5min&apikey={api_key}'
     response = requests.get(url)
     alpha_vantage_data = response.json()
     return alpha_vantage_data
 
+def calculate_ichimoku_cloud(data):
+    short_window = 9
+    long_window = 26
+    span_b_window = 52
+    displacement = 26
+
+    data['tenkan_sen'] = (data['high'].rolling(window=short_window).max() + data['low'].rolling(window=short_window).min()) / 2
+    data['kijun_sen'] = (data['high'].rolling(window=long_window).max() + data['low'].rolling(window=long_window).min()) / 2
+    data['senkou_span_a'] = ((data['tenkan_sen'] + data['kijun_sen']) / 2).shift(displacement)
+    data['senkou_span_b'] = ((data['high'].rolling(window=span_b_window).max() + data['low'].rolling(window=span_b_window).min()) / 2).shift(displacement)
+
+    return data
+
 def calculate_indicators(data):
-    # Convert all columns to numeric
     data = data.apply(pd.to_numeric, errors='coerce')
 
-    # Example: Simple condition for doji and inside
     data['Doji'] = abs(data['Close'] - data['open']) <= 0.01 * (data['high'] - data['low'])
     data['Inside'] = (data['high'] < data['high'].shift(1)) & (data['low'] > data['low'].shift(1))
+
+    data['MA5'] = data['Close'].rolling(window=5).mean()
+    data['MA20'] = data['Close'].rolling(window=20).mean()
+
+    data['26EMA'] = data['Close'].ewm(span=26).mean()
+    data['12EMA'] = data['Close'].ewm(span=12).mean()
+    data['MACD'] = data['12EMA'] - data['26EMA']
+
+    # Calculate Ichimoku Cloud
+    data = calculate_ichimoku_cloud(data)
+
     return data
 
 def main():
     st.title("Stock Trend Predictor")
 
-    # Use the hard-coded API key
     api_key = API_KEY
 
-    # Fetch Alpha Vantage data
     alpha_vantage_data = fetch_alpha_vantage_data(api_key)
 
-    # Extract relevant data from Alpha Vantage response
     alpha_vantage_time_series = alpha_vantage_data.get('Time Series (5min)', {})
     df = pd.DataFrame(alpha_vantage_time_series).T
     df.index = pd.to_datetime(df.index)
     df = df.dropna(axis=0)
 
-    # Rename columns
     df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'Close', '5. volume': 'volume'})
 
-    # Calculate indicators
     df = calculate_indicators(df)
 
-    # Create predictor
     my_market_predictor = Pandas_Market_Predictor(df)
 
-    # Predict Trend
-    indicators = ["Doji", "Inside"]
+    indicators = ["Doji", "Inside", "MA5", "MA20", "MACD", "tenkan_sen", "kijun_sen", "senkou_span_a", "senkou_span_b"]
     trend = my_market_predictor.Trend_Detection(indicators, 10)
 
-    # Display results
     st.subheader("Predicted Trend:")
     st.write("Buy Trend :", trend['BUY'])
     st.write("Sell Trend :", trend['SELL'])
     st.write(f"Standard Deviation Percentage: {my_market_predictor.PERCENT_STD}%")
 
-    # Delete the DataFrame to release memory
     del df
 
 if __name__ == "__main__":