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import streamlit as st |
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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import yfinance as yf |
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@st.cache_data |
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def load_data(ticker): |
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return yf.download(ticker, start="2000-01-01", end="2023-01-01") |
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def calculate_performance_metrics(data, initial_capital): |
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cagr = (data['Portfolio Value'].iloc[-1] / initial_capital) ** (1 / ((data.index[-1] - data.index[0]).days / 365.25)) - 1 |
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sharpe_ratio = data['Portfolio Value'].pct_change().mean() / data['Portfolio Value'].pct_change().std() * np.sqrt(252) |
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return cagr, sharpe_ratio |
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def plot_signals(data, ticker, short_window, long_window): |
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fig, ax = plt.subplots(figsize=(10, 5)) |
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ax.plot(data.index, data['Close'], label='Close Price', alpha=0.5) |
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ax.plot(data.index, data['Short_MA'], label=f'Short MA ({short_window})', alpha=0.75) |
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ax.plot(data.index, data['Long_MA'], label=f'Long MA ({long_window})', alpha=0.75) |
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ax.plot(data[data['Position'] == 1].index, data['Short_MA'][data['Position'] == 1], '^', markersize=10, color='g', lw=0, label='Buy Signal') |
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ax.plot(data[data['Position'] == -1].index, data['Short_MA'][data['Position'] == -1], 'v', markersize=10, color='r', lw=0, label='Sell Signal') |
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ax.set_title(f"{ticker} Price and Trading Signals") |
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ax.set_xlabel("Date") |
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ax.set_ylabel("Price") |
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ax.legend() |
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st.pyplot(fig) |
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st.title("Algorithmic Trading Strategy Backtesting") |
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st.markdown("This app allows you to backtest an algorithmic trading strategy using historical stock data.") |
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ticker = st.text_input("Enter the ticker symbol", "AAPL") |
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data = load_data(ticker) |
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short_window = st.number_input("Short moving average window", 1, 50, 20) |
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long_window = st.number_input("Long moving average window", 1, 200, 50) |
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initial_capital = st.number_input("Initial Capital", 1000, 1000000, 100000) |
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data['Short_MA'] = data['Close'].rolling(window=short_window).mean() |
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data['Long_MA'] = data['Close'].rolling(window=long_window).mean() |
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data.dropna(inplace=True) |
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data['Signal'] = 0 |
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data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0) |
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data['Position'] = data['Signal'].diff() |
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data['Portfolio Value'] = initial_capital |
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data['Portfolio Value'][short_window:] = initial_capital * (1 + data['Signal'][short_window:].shift(1) * data['Close'].pct_change()[short_window:]).cumprod() |
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cagr, sharpe_ratio = calculate_performance_metrics(data, initial_capital) |
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st.write(f"CAGR: {cagr:.2%}") |
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st.write(f"Sharpe Ratio: {sharpe_ratio:.2f}") |
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fig, ax = plt.subplots(figsize=(10, 5)) |
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ax.plot(data.index, data['Portfolio Value'], label='Portfolio Value') |
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ax.set_title(f"Backtested Performance of {ticker} Strategy") |
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ax.set_xlabel("Date") |
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ax.set_ylabel("Portfolio Value") |
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ax.legend() |
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st.pyplot(fig) |
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plot_signals(data, ticker, short_window, long_window) |
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st.subheader("Advanced Performance Metrics") |
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rolling_max = data['Portfolio Value'].cummax() |
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daily_drawdown = data['Portfolio Value'] / rolling_max - 1.0 |
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max_drawdown = daily_drawdown.cummin().iloc[-1] |
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st.write(f"Maximum Drawdown: {max_drawdown:.2%}") |
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st.subheader("Trade Statistics") |
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num_trades = data['Position'].value_counts().sum() |
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num_winning_trades = data['Position'][data['Position'] == 1].count() |
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num_losing_trades = data['Position'][data['Position'] == -1].count() |
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win_rate = num_winning_trades / num_trades |
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loss_rate = num_losing_trades / num_trades |
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st.write(f"Total Trades: {num_trades}") |
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st.write(f"Winning Trades: {num_winning_trades} ({win_rate:.2%})") |
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st.write(f"Losing Trades: {num_losing_trades} ({loss_rate:.2%})") |
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st.subheader("Upload Your Own Data") |
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uploaded_file = st.file_uploader("Choose a file", type="csv") |
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if uploaded_file is not None: |
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user_data = pd.read_csv(uploaded_file) |
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st.write("Uploaded data:") |
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st.write(user_data.head()) |
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