app.py

import streamlit as st
import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

def fetch_data(ticker, start_date, end_date):
    data = yf.download(ticker, start=start_date, end=end_date)
    return data

def calculate_indicators(data):
    # High and low for the breakout signals
    data['20 Day High'] = data['High'].rolling(window=20).max()
    data['20 Day Low'] = data['Low'].rolling(window=20).min()
    data['55 Day High'] = data['High'].rolling(window=55).max()
    data['55 Day Low'] = data['Low'].rolling(window=55).min()
    
    return data

def identify_signals(data):
    # Buy signals are generated when the price exceeds the 20-day high
    data['Buy Signal'] = (data['Close'] > data['20 Day High'].shift(1))
    # Sell signals are generated when the price drops below the 20-day low
    data['Sell Signal'] = (data['Close'] < data['20 Day Low'].shift(1))
    
    signals = []
    for index, row in data.iterrows():
        if row['Buy Signal']:
            signals.append({'Date': index, 'Signal Type': 'Buy', 'Price': row['Close']})
        if row['Sell Signal']:
            signals.append({'Date': index, 'Signal Type': 'Sell', 'Price': row['Close']})
    
    return data, pd.DataFrame(signals)

def plot_data(data):
    plt.figure(figsize=(12, 6))
    plt.plot(data['Close'], label='Close Price')
    
    buy_signals = data[data['Buy Signal']]
    sell_signals = data[data['Sell Signal']]
    plt.scatter(buy_signals.index, buy_signals['Close'], marker='^', color='green', s=100, label='Buy Signal')
    plt.scatter(sell_signals.index, sell_signals['Close'], marker='v', color='red', s=100, label='Sell Signal')
    
    plt.title('Stock Price and Turtle Trading Signals')
    plt.xlabel('Date')
    plt.ylabel('Price')
    plt.legend()
    plt.grid(True)
    plt.show()

def main():
    st.title("Turtle Trading Strategy Visualization")
    ticker = st.text_input("Enter the ticker symbol, e.g., 'AAPL'")
    start_date = st.date_input("Select the start date")
    end_date = st.date_input("Select the end date")
    
    if st.button("Analyze"):
        data = fetch_data(ticker, start_date, end_date)
        data = calculate_indicators(data)
        data, signals = identify_signals(data)
        plot_data(data)
        st.pyplot(plt)
        if not signals.empty:
            st.write("Trading Signals:")
            st.dataframe(signals)
        else:
            st.write("No trading signals found for the selected period.")

if __name__ == "__main__":
    main()