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AlphaPy is a machine learning framework for both speculators and
data scientists. It is written in Python mainly with the scikit-learn
and pandas libraries, as well as many other helpful
packages for feature engineering and visualization.
π AlphaPy Pro is Now Available!
AlphaPy Pro - the professional edition of AlphaPy - is now publicly available! Featuring modern Python 3.12+ support, enhanced performance, and enterprise-grade capabilities.
- Repository: https://github.com/ScottfreeLLC/alphapy-pro
- Documentation: https://scottfreellc.github.io/alphapy-pro/
- Installation:
pip install alphapy-pro
Here are just some of the things you can do with AlphaPy (legacy):
- Run machine learning models using
scikit-learn,Keras,xgboost,LightGBM, andCatBoost. - Generate blended or stacked ensembles.
- Create models for analyzing the markets with MarketFlow.
- Predict sporting events with SportFlow.
- Develop trading systems and analyze portfolios using MarketFlow
and Quantopian's
pyfolio.
AlphaPy Pro is the next generation of AlphaPy with enhanced features and modern capabilities:
- Modern Python 3.12+ support with UV package management
- Enhanced MarketFlow with advanced financial ML features
- MetaLabeling Support for sophisticated financial modeling
- NLP Features for sentiment analysis and text processing
- Automated CI/CD with GitHub Actions and PyPI publishing
- Comprehensive Documentation with tutorials and examples
Quick Start with AlphaPy Pro:
pip install alphapy-pro
Links:
- GitHub Repository: https://github.com/ScottfreeLLC/alphapy-pro
- Documentation: https://scottfreellc.github.io/alphapy-pro/
- PyPI Package: https://pypi.org/project/alphapy-pro/
Note: Active development has moved to AlphaPy Pro. This repository (AlphaPy) remains available for users who rely on the original version.
http://alphapy.readthedocs.io/en/latest/
You should already have pip, Python, and optionally XGBoost, LightGBM, and CatBoost installed on your system (see below). Run the following command to install AlphaPy:
pip install -U alphapy
Pyfolio is automatically installed by AlphaPy, but if you encounter the following error when trying to create a tear sheet:
AttributeError: 'numpy.int64' object has no attribute 'to_pydatetime'
Install pyfolio with this command:
pip install git+https://github.com/quantopian/pyfolio
For Mac and Windows users, XGBoost will not install automatically
with pip. For instructions to install XGBoost on your specific
platform, go to http://xgboost.readthedocs.io/en/latest/build.html.
For instructions to install LightGBM on your specific platform, go to https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html.
For instructions to install CatBoost on your specific platform, go to https://catboost.ai/docs/concepts/python-installation.html.
You can find an implementation of MarketFlow here:
https://www.scottfreellc.com/gamept
The official channel for support is to open an issue on Github.
http://github.com/ScottfreeLLC/AlphaPy/issues
Follow us on Twitter:
https://twitter.com/_AlphaPy_?lang=en
If you like the software, please donate:
http://alphapy.readthedocs.io/en/latest/introduction/support.html#donations