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. Here are just some of the things you can do with AlphaPy:

• Run machine learning models using scikit-learn, Keras, xgboost, LightGBM, and CatBoost.
• 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.

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.

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