AlgoTradingBacktest

Interactive Streamlit app for backtesting simple algorithmic trading strategies on historical price data.

The app lets you:

• Pull OHLCV data from Yahoo Finance
• Configure parameters for several technical strategies
• Run backtests vs. a buy-and-hold benchmark
• Inspect equity curves, trades, and summary performance metrics

⚠️ Disclaimer
This project is for educational and research purposes only.
It is not financial advice and should not be used to make live trading decisions.

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Features

• 📈 Download market data with [yfinance] from Yahoo Finance
• 🧮 Technical indicators with [pandas-ta]
• ⚙️ Configurable strategy parameters via Streamlit sidebar
• 🔁 Vectorized backtesting engine for long / flat strategies
• 📊 Performance analytics, for example:
  • Total return
  • Annualized (CAGR) return
  • Volatility
  • Sharpe ratio
  • Max drawdown
  • Win rate
• 🔍 Trade list: entry / exit dates, returns, and holding periods

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Implemented Strategies (Overview)

The app is structured to support multiple rule-based strategies, including:

• EMA Crossover

  • Go long when a fast EMA crosses above a slow EMA
  • Exit when the fast EMA crosses back below the slow EMA

• Bollinger Band Strategy

  • Uses moving average ± N standard deviations
  • Typical rules: buy near lower band, sell/exit near middle or upper band

• Z-Score Mean Reversion

  • Normalize deviations from a moving average using Z-scores
  • Enter when |Z| is above a threshold and revert back toward the mean

• Pairs Trading (Spread & Z-Score)

  • Build a spread between two correlated tickers (e.g., SPY vs. IVV)
  • Compute Z-score of the spread over a lookback window
  • Enter long/short legs when Z exceeds entry threshold and exit on reversion

The exact strategies and parameters are defined in app.py and can be customized there.

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Tech Stack

• Python
• Streamlit – UI framework
• yfinance – data download
• pandas / NumPy – data wrangling
• pandas-ta – technical indicators
• matplotlib / Plotly (if used in app.py) – charts

All Python dependencies are listed in requirements.txt.

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Installation

1. Clone the repository

git clone https://github.com/ROCCYK/AlgoTradingBacktest.git
cd AlgoTradingBacktest

Live Demo / Web App

You can try a live deploy of the backtest app here:

https://algotradingbacktest.streamlit.app/

Use this link to launch the app in your browser without cloning the repo.