README
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance.
Key Features
- A super-fast backtesting engine built in NumPy and accelerated with Numba.
- The ability to create and execute trading rules and models across multiple instruments with ease.
- Access to historical data from Alpaca, Yahoo Finance, AKShare, or from your own data provider.
- The option to train and backtest models using Walkforward Analysis, which simulates how the strategy would perform during actual trading.
- More reliable trading metrics that use randomized bootstrapping to provide more accurate results.
- Caching of downloaded data, indicators, and models to speed up your development process.
- Parallelized computations that enable faster performance.
With PyBroker, you'll have all the tools you need to create winning trading