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.
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 strategies backed by data and machine learning. Start using PyBroker today and take your trading to the next level!
PyBroker supports Python 3.9+ on Windows, Mac, and Linux. You can install
pip install -U lib-pybroker
Or you can clone the Git repository with:
git clone https://github.com/edtechre/pybroker
A Quick Example
Get a glimpse of what backtesting with PyBroker looks like with these code snippets:
from pybroker import Strategy, YFinance, highest def exec_fn(ctx): # Get the rolling 10 day high. high_10d = ctx.indicator('high_10d') # Buy on a new 10 day high. if not ctx.long_pos() and high_10d[-1] > high_10d[-2]: ctx.buy_shares = 100 # Hold the position for 5 days. ctx.hold_bars = 5 # Set a stop loss of 2%. ctx.stop_loss_pct = 2 strategy = Strategy(YFinance(), start_date='1/1/2022', end_date='7/1/2022') strategy.add_execution( exec_fn, ['AAPL', 'MSFT'], indicators=highest('high_10d', 'close', period=10)) # Run the backtest after 20 days have passed. result = strategy.backtest(warmup=20)
import pybroker from pybroker import Alpaca, Strategy def train_fn(train_data, test_data, ticker): # Train the model using indicators stored in train_data. ... return trained_model # Register the model and its training function with PyBroker. my_model = pybroker.model('my_model', train_fn, indicators=[...]) def exec_fn(ctx): preds = ctx.preds('my_model') # Open a long position given my_model's latest prediction. if not ctx.long_pos() and preds[-1] > buy_threshold: ctx.buy_shares = 100 # Close the long position given my_model's latest prediction. elif ctx.long_pos() and preds[-1] < sell_threshold: ctx.sell_all_shares() alpaca = Alpaca(api_key=..., api_secret=...) strategy = Strategy(alpaca, start_date='1/1/2022', end_date='7/1/2022') strategy.add_execution(exec_fn, ['AAPL', 'MSFT'], models=my_model) # Run Walkforward Analysis on 1 minute data using 5 windows with 50/50 train/test data. result = strategy.walkforward(timeframe='1m', windows=5, train_size=0.5)
To learn how to use PyBroker, see the notebooks under the User Guide:
The notebooks above are also available on Github.
- Configuration Options
- pybroker package
- pybroker.cache module
- pybroker.common module
- pybroker.config module
- pybroker.context module
- pybroker.data module
- pybroker.eval module
- pybroker.indicator module
- pybroker.log module
- pybroker.model module
- pybroker.portfolio module
- pybroker.scope module
- pybroker.slippage module
- pybroker.strategy module
- pybroker.vect module
- pybroker package
The following is a list of essential books that provide background information on quantitative finance and algorithmic trading:
Lingjie Ma, Quantitative Investing: From Theory to Industry
Timothy Masters, Testing and Tuning Market Trading Systems: Algorithms in C++
Stefan Jansen, Machine Learning for Algorithmic Trading, 2nd Edition
Ernest P. Chan, Machine Trading: Deploying Computer Algorithms to Conquer the Markets
Perry J. Kaufman, Trading Systems and Methods, 6th Edition