Algorithmic Trading Starter

A simple framework for algorithmic trading with backtesting and live trading capabilities. This project provides a modular architecture for developing, testing, and deploying trading strategies with minimal setup.

Features

• Modular strategy implementation with an extensible framework
• Backtesting with historical data and performance metrics
• Live trading integration with Alpaca API (both paper and real trading)
• Performance analysis and visualization tools
• Multiple built-in strategy examples (SMA, RSA)
• Asynchronous data streaming for real-time market data

Installation

1. Clone the repository:

git clone https://github.com/Eik-Lab/algorithmic-trading-starter.git
cd algorithmic-trading-starter

2. Install dependencies:

pip install -r requirements.txt

3. Set up your Alpaca API credentials (for live trading):

export ALPACA_API_KEY="your_api_key"
export ALPACA_API_SECRET="your_api_secret"

Usage

Backtesting

Run a backtest with default parameters (RSA strategy on SPY):

python src/main.py --mode backtest

Customize your backtest:

python src/main.py --mode backtest --strategy sma --window-size 50 --symbol AAPL --days 500 --capital 100000

Live Trading

Run live trading with paper account:

python src/main.py --mode live --strategy rsa --window-size 20

Available command-line arguments:

• --mode: Trading mode ('backtest' or 'live')
• --strategy: Strategy to use ('rsa' or 'sma')
• --window-size: Window size for the strategy
• --symbol: Symbol to trade (default: 'SPY')
• --days: Number of days for backtest
• --capital: Initial capital amount

Project Structure

algorithmic-trading-starter/
├── src/
│   ├── main.py              # Main entry point
│   ├── trader.py            # Core trader implementation
│   ├── strategy.py          # Base strategy class
│   ├── backtest.py          # Backtesting engine
│   ├── live_trading.py      # Live trading with Alpaca
│   └── strategies/          # Strategy implementations
│       ├── rsa.py           # Relative Strength Analysis
│       └── sma.py           # Simple Moving Average
├── requirements.txt         # Dependencies
└── README.md                # Documentation

Implementing Custom Strategies

Create a new strategy by extending the base Strategy class:

from strategy import Strategy, Position

class MyCustomStrategy(Strategy):
    def __init__(self, name="custom_strategy", window_size=20):
        super().__init__(name)
        self.window_size = window_size
        # Initialize your strategy parameters
        
    def update(self, data):
        # Implement your strategy logic here
        # Use self.enter_long(), self.enter_short(), or self.exit_position()
        # to generate trading signals
        pass

Performance Metrics

The backtesting engine provides the following performance metrics:

• Total return
• Win rate
• Profit factor
• Maximum drawdown
• Sharpe ratio
• Number of trades

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.