app.py

import streamlit as st
import yfinance as yf
import pandas as pd
import requests
from thronetrader import StrategicSignals
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 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()