A collection of tools for researching and running intraday trading strategies. It includes a modular trading system, backtester, risk management utilities and several example strategies.
- Multiple strategies: Moving average crossover, RSI mean reversion, Bollinger Bands and MACD (new).
- Backtesting engine with metrics such as total return, Sharpe ratio, drawdown and more.
- Risk management: position sizing, stop loss/take profit and daily loss limits.
- Live trading support via Interactive Brokers API with optional paper trading.
- Factor model utilities using toraniko for quantitative analysis.
Install the required packages:
pip install -r requirements.txtRun a simple moving average cross backtest:
from config import TradingConfig
from intraday_strategy import IntradayTradingSystem
system = IntradayTradingSystem(TradingConfig())
system.set_strategy('ma_cross')
results = system.run_backtest('SPY', '20230101', '20231231')
print(results.metrics)For live paper trading (requires a running TWS/Gateway instance):
system.connect_to_ibkr()
system.set_strategy('ma_cross')
system.run_live_trading('SPY', duration_minutes=60)
print(system.get_performance_metrics())The backtester records a suite of metrics including:
- Profit and loss (PnL)
- Win rate and profit factor
- Sharpe and Sortino ratios
- Maximum drawdown and volatility
- Trade expectancy and Calmar ratio
Results are returned as a dictionary for further analysis or comparison.
During live (paper) trading all executed trades are logged and the system keeps running totals of realised and unrealised PnL. Performance metrics can be retrieved via get_performance_metrics() and may be used to automatically adjust strategy parameters.
Additional examples can be found under code/example_usage.py and docs/examples/. To verify the installation run:
pytest -qAlgTrading/
├── code/ # Trading system modules
├── docs/ # Books and example notebooks
├── tests/ # Basic import tests
└── requirements.txt
This project is for research purposes. Use at your own risk when trading live.