PyBroker is a framework for creating algorithmic trading strategies, including those that utilize machine learning.
Unlike other algotrading frameworks, PyBroker is specifically designed for developing trading 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.
Some of the key features of PyBroker include:
A super-fast backtesting engine built using 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 and Yahoo Finance, 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.
Support for strategies that use ranking and flexible position sizing.
Caching of downloaded data, indicators, and models to speed up your development process.
Parallelized computations that enable faster performance.
Additionally, PyBroker was written to be Pythonic and very easy to use.
PyBroker is a framework for creating algorithmic trading strategies, including those that utilize machine learning.
Unlike other algotrading frameworks, PyBroker is specifically designed for developing trading 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.
Some of the key features of PyBroker include:
A super-fast backtesting engine built using 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 and Yahoo Finance, 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.
Support for strategies that use ranking and flexible position sizing.
Caching of downloaded data, indicators, and models to speed up your development process.
Parallelized computations that enable faster performance.
Additionally, PyBroker was written to be Pythonic and very easy to use.