app.py

import streamlit as st
from thronetrader import StrategicSignals
import requests
import pandas as pd


# 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()

def main():
    st.title("Strategic Trading Signals")

    # Input for stock symbol
    symbol = st.text_input("Enter stock symbol (e.g., AAPL):", "AAPL")

    # Display strategic trading signals
    strategic_signals = StrategicSignals(symbol=symbol)

    st.subheader("Bollinger Bands Signals:")
    bollinger_bands_signals = strategic_signals.get_bollinger_bands_signals()
    st.write(bollinger_bands_signals)

    st.subheader("Breakout Signals:")
    breakout_signals = strategic_signals.get_breakout_signals()
    st.write(breakout_signals)

    st.subheader("Crossover Signals:")
    crossover_signals = strategic_signals.get_crossover_signals()
    st.write(crossover_signals)

    st.subheader("MACD Signals:")
    macd_signals = strategic_signals.get_macd_signals()
    st.write(macd_signals)

    st.subheader("RSI Signals:")
    rsi_signals = strategic_signals.get_rsi_signals()
    st.write(rsi_signals)

if __name__ == "__main__":
    main()



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 main():
    st.title("AI Stock Trend Predictor")

    # User input for stock symbol
    symbol = st.text_input("Enter Stock Symbol (e.g., IBM):",key="symbol")

    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)

    # 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"]
    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'])
    
    # Delete the DataFrame to release memory
    del df

if __name__ == "__main__":
    main()