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ScottfreeLLC/AlphaPy

Python machine learning framework built on scikit-learn and pandas, with modules for market trading systems and sports prediction.

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Repolode describes open-source projects and does not give investment advice. Trading and investing can lose money: assess the risk yourself and follow the laws where you live before using this project.

What is it?

AlphaPy is a Python machine learning framework aimed at traders and data scientists. It is built mainly on scikit-learn and pandas, with additional packages for feature engineering and visualization. It can train models from scikit-learn, Keras, XGBoost, LightGBM, and CatBoost, and generate blended or stacked ensembles.

The project includes two domain pipelines. MarketFlow supports building models for market analysis and developing trading systems, with portfolio analysis via Quantopian's pyfolio. SportFlow is aimed at predicting sporting events.

The author notes that active development has moved to AlphaPy Pro, a separate commercial-oriented edition with Python 3.12+ support. This repository remains for users of the original version.

Key features

  • 01Runs scikit-learn, Keras, XGBoost, LightGBM, and CatBoost models in one pipeline.
  • 02Builds blended or stacked ensemble models.
  • 03MarketFlow module for market analysis and trading system development.
  • 04SportFlow module for predicting sporting events.
  • 05Portfolio analysis through integration with pyfolio.

Use cases

  • Building predictive models for stock or cryptocurrency price movements.
  • Developing and backtesting systematic trading strategies.
  • Forecasting sports game outcomes with machine learning.
  • Prototyping tabular classification and regression models with scikit-learn-based tooling.

Quick start

  1. 1

    Install AlphaPy with pip

    pip install -U alphapy
  2. 2

    Optionally install pyfolio from GitHub if tear sheets fail

    pip install git+https://github.com/quantopian/pyfolio
  3. 3

    Install XGBoost, LightGBM or CatBoost following their platform guides if needed (Mac and Windows users)

Things to know

  • Active development has moved to AlphaPy Pro; this legacy repository is maintained only for existing users.
  • XGBoost does not install automatically with pip on Mac and Windows, so extra setup is required.
  • Pyfolio may need to be installed from GitHub to fix tear sheet errors.

In collections

Data science courses & tools

Topics

#machine-learning#trading#scikit-learn#time-series-analysis#sports-analytics#ensemble-learning#backtesting#classification#cryptocurrency#data-science

FAQ

What is AlphaPy?
Python machine learning framework built on scikit-learn and pandas, with modules for market trading systems and sports prediction. AlphaPy is a Python machine learning framework aimed at traders and data scientists. It is built mainly on scikit-learn and pandas, with additional packages for feature engineering and visualization. It can train models from scikit-learn, Keras, XGBoost, LightGBM, and CatBoost, and generate blended or stacked ensembles.
Is AlphaPy free to use? What license does it have?
AlphaPy is open source under the Apache-2.0 license. Check the license terms before using it in commercial products.
Is AlphaPy still maintained?
Updated within 6 months. The last commit to AlphaPy was on Oct 5, 2026.
What can I use AlphaPy for?
Building predictive models for stock or cryptocurrency price movements. Developing and backtesting systematic trading strategies. Forecasting sports game outcomes with machine learning. Prototyping tabular classification and regression models with scikit-learn-based tooling.
How do I install AlphaPy?
Install AlphaPy with pip: pip install -U alphapy Optionally install pyfolio from GitHub if tear sheets fail: pip install git+https://github.com/quantopian/pyfolio Install XGBoost, LightGBM or CatBoost following their platform guides if needed (Mac and Windows users)
Where can I get AlphaPy?
The source code and installation instructions are on GitHub: https://github.com/ScottfreeLLC/AlphaPy

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