app.py

import streamlit as st
import yfinance as yf
import pandas as pd
import plotly.graph_objects as go

# Function to fetch data from Yahoo Finance
def fetch_data(ticker, start_date, end_date):
    data = yf.download(ticker, start=start_date, end=end_date)
    return data

# Calculate indicators based on user-defined window sizes
def calculate_indicators(data, window_short, window_long):
    data['High Short'] = data['High'].rolling(window=window_short).max()
    data['Low Short'] = data['Low'].rolling(window=window_short).min()
    data['High Long'] = data['High'].rolling(window=window_long).max()
    data['Low Long'] = data['Low'].rolling(window=window_long).min()
    return data

# Identify buy and sell signals based on breakout strategy
def identify_signals(data):
    data['Buy Signal'] = (data['Close'] > data['High Short'].shift(1))
    data['Sell Signal'] = (data['Close'] < data['Low Short'].shift(1))
    return data

# Collect and display signals
def collect_signals(data):
    signals = pd.DataFrame()
    signals['Date'] = data[data['Buy Signal'] | data['Sell Signal']].index
    signals['Price'] = data[data['Buy Signal'] | data['Sell Signal']]['Close']
    signals['Signal'] = 'Buy'
    signals.loc[data['Sell Signal'], 'Signal'] = 'Sell'
    return signals

# Calculate returns and metrics for backtesting
def backtest_signals(data):
    data['Position'] = 0
    data['Position'] = data['Buy Signal'].replace(True, 1).cumsum()
    data['Position'] = data['Position'] - data['Sell Signal'].replace(True, 1).cumsum()
    data['Position'] = data['Position'].clip(lower=0, upper=1)

    data['Market Returns'] = data['Close'].pct_change()
    data['Strategy Returns'] = data['Market Returns'] * data['Position'].shift(1)

    data['Cumulative Market Returns'] = (1 + data['Market Returns']).cumprod() - 1
    data['Cumulative Strategy Returns'] = (1 + data['Strategy Returns']).cumprod() - 1
    
    return data, data['Cumulative Market Returns'].iloc[-1], data['Cumulative Strategy Returns'].iloc[-1]

# Plotting function using Plotly for interactive charts
def plot_data(data):
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=data.index, y=data['Close'], name='Close Price', line=dict(color='blue')))
    fig.add_trace(go.Scatter(x=data.index, y=data['High Short'], name='High Short', line=dict(dash='dot')))
    fig.add_trace(go.Scatter(x=data.index, y=data['Low Short'], name='Low Short', line=dict(dash='dot')))
    buys = data[data['Buy Signal']]
    sells = data[data['Sell Signal']]
    fig.add_trace(go.Scatter(x=buys.index, y=buys['Close'], mode='markers', name='Buy Signal', marker_symbol='triangle-up', marker_color='green', marker_size=10))
    fig.add_trace(go.Scatter(x=sells.index, y=sells['Close'], mode='markers', name='Sell Signal', marker_symbol='triangle-down', marker_color='red', marker_size=10))
    fig.update_layout(title='Stock Price and Trading Signals', xaxis_title='Date', yaxis_title='Price', template='plotly_dark')
    return fig

# Main application function
def main():
    st.title("Enhanced Turtle Trading Strategy with Backtesting and Signal Table")

    # Sidebar for user inputs
    with st.sidebar:
        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")
        window_short = st.number_input("Short term window", min_value=5, max_value=60, value=20)
        window_long = st.number_input("Long term window", min_value=5, max_value=120, value=55)

    if st.button("Analyze"):
        data = fetch_data(ticker, start_date, end_date)
        if not data.empty:
            data = calculate_indicators(data, window_short, window_long)
            data = identify_signals(data)
            signals = collect_signals(data)
            data, market_return, strategy_return = backtest_signals(data)
            fig = plot_data(data)
            st.plotly_chart(fig, use_container_width=True)
            st.subheader("Trading Signals")
            st.dataframe(signals)
            st.subheader("Backtesting Results")
            st.write(f"Market Return: {market_return * 100:.2f}%")
            st.write(f"Strategy Return: {strategy_return * 100:.2f}%")
        else:
            st.error("No data found for the selected ticker and date range.")

if __name__ == "__main__":
    main()