Update app.py

app.py

@@ -49,82 +49,3 @@ if __name__ == "__main__":
 
 
  
-
-# Hard-coded API key for demonstration purposes
-API_KEY = "QR8F9B7T6R2SWTAT"
-
-def fetch_alpha_vantage_data(api_key, symbol):
-    
-    url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
-    response = requests.get(url)
-    alpha_vantage_data = response.json()
-    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))
-    return data
-
-def display_signals(signal_type, signals):
-    st.subheader(f"{signal_type} Signals:")
-    st.write(signals)
-
-def main():
-    st.title("Stock Trend Predictor")
-
-    # Input for stock symbol
-    symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")
-
-    # Fetch Alpha Vantage data
-    alpha_vantage_data = fetch_alpha_vantage_data(API_KEY, symbol)
-
-    # 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)
-
-    # Display stock trading signals
-    strategic_signals = StrategicSignals(symbol=symbol)
-
-    # Display loading message during processing
-    with st.spinner("Predicting signals using Strategic Indicators..."):
-        # Display signals
-        st.subheader(":orange[Strategic Indicators Trend Prediction]")
-        display_signals("Bollinger Bands", strategic_signals.get_bollinger_bands_signals())
-        display_signals("Breakout", strategic_signals.get_breakout_signals())
-        display_signals("Crossover", strategic_signals.get_crossover_signals())
-        display_signals("MACD", strategic_signals.get_macd_signals())
-        display_signals("RSI", strategic_signals.get_rsi_signals())
-
-    # Create predictor
-    my_market_predictor = Pandas_Market_Predictor(df)
-
-    # Predict Trend
-    indicators = ["Doji", "Inside"]
-
-    # Display loading message during prediction
-    with st.spinner("Predicting trend using AI ...."):
-        # Predict trend
-        trend = my_market_predictor.Trend_Detection(indicators, 10)
-
-    # Display results
-    st.subheader(":orange[AI Trend Prediction]")
-    st.write("Buy Trend :", trend['BUY'])
-    st.write("Sell Trend :", trend['SELL'])
-
-    # Delete the DataFrame to release memory
-    del df
-
-if __name__ == "__main__":
-    main()