Update app.py

app.py

@@ -13,14 +13,18 @@ def fetch_alpha_vantage_data(api_key):
     return alpha_vantage_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))
 
+    # Calculate Moving Averages
     data['MA5'] = data['Close'].rolling(window=5).mean()
     data['MA20'] = data['Close'].rolling(window=20).mean()
 
+    # Calculate MACD
     data['26EMA'] = data['Close'].ewm(span=26).mean()
     data['12EMA'] = data['Close'].ewm(span=12).mean()
     data['MACD'] = data['12EMA'] - data['26EMA']
@@ -30,33 +34,38 @@ def calculate_indicators(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)
 
-    # Remove Ichimoku Cloud columns
-    ichimoku_columns = ["tenkan_sen", "kijun_sen", "senkou_span_a", "senkou_span_b"]
-    df = df.drop(columns=ichimoku_columns)
-
+    # Predict Trend
     indicators = ["Doji", "Inside", "MA5", "MA20", "MACD"]
     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__":