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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. 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.
AlphaPy Model Pipeline



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+


For Mac and Windows users, XGBoost will not install automatically with pip. For instructions to install XGBoost on your specific platform, go to


For instructions to install LightGBM on your specific platform, go to


For instructions to install CatBoost on your specific platform, go to


MarketFlow Model MarketFlow System




The official channel for support is to open an issue on Github.

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Python (1,147,256
Machine Learning (31,898
Deep Learning (23,829
Tensorflow (12,840
Data Science (9,253
Cryptocurrency (5,902
Keras (5,781
Portfolio (4,720
Pandas (3,959
Classification (3,163
Scikit Learn (2,001
Trading (1,508
Regression (1,354
Stock (1,332
Time Series Analysis (593
Sports (468
Trading Strategies (365
Predictive Analytics (169
Trading Platform (156
Iex (68
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