Update app.py

app.py

@@ -1,30 +1,67 @@
-# Import necessary libraries
 import streamlit as st
+from thronetrader import StrategicSignals
 import requests
-import pandas as pd
-from datetime import datetime
 from Pandas_Market_Predictor import Pandas_Market_Predictor
+import pandas as pd
+
 
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
 
 def fetch_alpha_vantage_data(api_key, symbol):
-    try:
-        url = f'https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol={symbol}&interval=5min&apikey={api_key}'
-        response = requests.get(url)
-        response.raise_for_status()  # Raise an error for bad responses
-        alpha_vantage_data = response.json()
-        return alpha_vantage_data
-    except requests.RequestException as e:
-        st.error(f"Error fetching data: {e}")
-        return None
+    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 main():
+    st.title("Stock Trend Predictor")
+
+    # User input for stock symbol
+    symbol = st.text_input("Enter Stock Symbol (e.g., IBM):")
+
+    if not symbol:
+        st.warning("Please enter a valid stock symbol.")
+        st.stop()
+
+    # Use the hard-coded API key
+    api_key = API_KEY
+
+    # 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)
+
+    # Display the raw data
+    st.subheader("Raw Data:")
+    st.write(df)
+
+if __name__ == "__main__":
+    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['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
 
@@ -32,63 +69,58 @@ def display_signals(signal_type, signals):
     st.subheader(f"{signal_type} Signals:")
     st.write(signals)
 
-def predict_trend(data):
+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(data)
+    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)
-    return trend
-
-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)
+    # Display results
+    st.subheader(":orange[AI Trend Prediction]")
+    st.write("Buy Trend :", trend['BUY'])
+    st.write("Sell Trend :", trend['SELL'])
 
-    if alpha_vantage_data:
-        # 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())
-
-        # Predict trend using AI
-        trend = predict_trend(df)
-
-        # 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
+    # Delete the DataFrame to release memory
+    del df
 
 if __name__ == "__main__":
     main()