app.py

import streamlit as st
import yfinance as yf
import pandas as pd
from Pandas_Market_Predictor import Pandas_Market_Predictor

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
    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")

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

    # Fetch yfinance data
    stock_data = fetch_yfinance_data(symbol)

    # Calculate indicators
    stock_data = calculate_indicators(stock_data)

    # Create predictor
    my_market_predictor = Pandas_Market_Predictor(stock_data)

    # 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'])
    st.write("Hold Trend :", trend['HOLD'])

    # Delete the DataFrame to release memory
    del stock_data

if __name__ == "__main__":
    main()