Update app.py

app.py

@@ -1,60 +1,11 @@
 import streamlit as st
-from thronetrader import StrategicSignals
-import requests
-from Pandas_Market_Predictor import Pandas_Market_Predictor
+import yfinance as yf
 import pandas as pd
+from Pandas_Market_Predictor import Pandas_Market_Predictor
 
-
-# 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 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 fetch_yfinance_data(symbol):
+    data = yf.download(symbol, start="2022-01-01", end="2022-12-31", interval="5m")
+    return data
 
 def calculate_indicators(data):
     # Convert all columns to numeric
@@ -65,62 +16,33 @@ def calculate_indicators(data):
     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")
+    st.title("AI 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'})
+    # Fetch yfinance data
+    stock_data = fetch_yfinance_data(symbol)
 
     # 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())
+    stock_data = calculate_indicators(stock_data)
 
     # Create predictor
-    my_market_predictor = Pandas_Market_Predictor(df)
+    my_market_predictor = Pandas_Market_Predictor(stock_data)
 
     # 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)
+    trend = my_market_predictor.Trend_Detection(indicators, 10)
 
     # Display results
-    st.subheader(":orange[AI Trend Prediction]")
+    st.subheader("Predicted Trend:")
     st.write("Buy Trend :", trend['BUY'])
     st.write("Sell Trend :", trend['SELL'])
+    st.write("Hold Trend :", trend['HOLD'])
 
     # Delete the DataFrame to release memory
-    del df
+    del stock_data
 
 if __name__ == "__main__":
     main()