Backtesting Framework for Trading Strategies

A lightweight and modular Python framework for backtesting trading strategies using historical stock market data. This project includes components for data loading, strategy execution, portfolio management, and result visualization.

Features

• Plug-and-play trading strategies (e.g., Moving Average Crossover)
• Modular design for extensibility
• Portfolio and cash management
• Uses Yahoo Finance for historical data
• Command-line interface for quick testing

Project Structure

.
├── core
│   ├── backtester.py          # Main backtester logic
│   ├── data_feed.py           # Iterates over data
│   ├── data_loader.py         # Loads data from CSV or DataFrame
│   ├── display.py             # Plotting utilities
│   ├── portfolio.py           # Portfolio and trade execution logic
│   └── strategy.py            # Base strategy class
├── strategies
│   └── simple_MA.py           # Example strategy: Simple Moving Average
├── main.py                    # Entry point
└── README.md                  # This file

Requirements

• Python 3.8+
• pandas
• matplotlib
• yfinance
• ccxt
• pyarrow

Install dependencies with:

poetry install

Usage

Run the backtest from the command line:

python main.py --ticker AAPL --period 60d

• --ticker: Stock ticker (e.g., AAPL, MSFT, TSLA)
• --period: Time range (e.g., 60d, 1y, 5y)

Example Output

The script will:

1. Download historical price data from Yahoo Finance.
2. Apply the SimpleMovingAverageStrategy.
3. Execute trades via the portfolio manager.
4. Display a price chart.

Implemented Strategy

Simple Moving Average Crossover:

• Buy when short MA crosses above long MA.
• Sell when short MA crosses below long MA.
• Holds otherwise.

You can define your own strategies by subclassing Strategy.

License

This project is open-source and free to use under the MIT License.