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It is written in Python mainly with the ``scikit-learn``\nand ``pandas`` libraries, as well as many other helpful\npackages for feature engineering and visualization. Here are just\nsome of the things you can do with AlphaPy:\n\n* Run machine learning models using ``scikit-learn``, ``Keras``, ``xgboost``, ``LightGBM``, and ``CatBoost``.\n* Generate blended or stacked ensembles.\n* Create models for analyzing the markets with *MarketFlow*.\n* Predict sporting events with *SportFlow*.\n* Develop trading systems and analyze portfolios using *MarketFlow*\n  and Quantopian's ``pyfolio``.\n\n.. image:: https://github.com/Alpha314/AlphaPy/blob/master/images/model_pipeline.png\n    :width: 100%\n    :alt: AlphaPy Model Pipeline\n    :align: center\n\nAlphaPy Pro: Coming Soon\n------------------------\n\nhttps://www.scottfreellc.com/alphapy-pro\n\nDocumentation\n-------------\n\nhttp://alphapy.readthedocs.io/en/latest/\n\nInstallation\n------------\n\nYou should already have pip, Python, and optionally XGBoost, LightGBM, and\nCatBoost installed on your system (see below). Run the following command to install\nAlphaPy::\n\n    pip install -U alphapy\n\nPyfolio\n~~~~~~~\n\nPyfolio is automatically installed by AlphaPy, but if you encounter\nthe following error when trying to create a tear sheet:\n\n    *AttributeError: 'numpy.int64' object has no attribute 'to_pydatetime'*\n\nInstall pyfolio with this command:\n\n    pip install git+https://github.com/quantopian/pyfolio\n\nXGBoost\n~~~~~~~\n\nFor Mac and Windows users, XGBoost will *not* install automatically\nwith ``pip``. For instructions to install XGBoost on your specific\nplatform, go to http://xgboost.readthedocs.io/en/latest/build.html.\n\nLightGBM\n~~~~~~~~\n\nFor instructions to install LightGBM on your specific\nplatform, go to https://lightgbm.readthedocs.io/en/latest/Installation-Guide.html.\n\nCatBoost\n~~~~~~~~\n\nFor instructions to install CatBoost on your specific\nplatform, go to https://catboost.ai/docs/concepts/python-installation.html.\n\nMarketFlow\n----------\n\n.. image:: https://github.com/Alpha314/AlphaPy/blob/master/images/market_pipeline.png\n    :width: 100%\n    :alt: MarketFlow Model\n    :align: center\n\n.. image:: https://github.com/Alpha314/AlphaPy/blob/master/images/system_pipeline.png\n    :width: 100%\n    :alt: MarketFlow System\n    :align: center\n\nSportFlow\n---------\n\n.. image:: https://github.com/Alpha314/AlphaPy/blob/master/images/sports_pipeline.png\n    :width: 100%\n    :alt: SportFlow\n    :align: center\n\nGamePT\n------\n\nYou can find an implementation of MarketFlow here:\n\nhttps://www.scottfreellc.com/gamept\n\nSupport\n-------\n\nThe official channel for support is to open an issue on Github.\n\nhttp://github.com/ScottfreeLLC/AlphaPy/issues\n\nFollow us on Twitter:\n\nhttps://twitter.com/_AlphaPy_?lang=en\n\nDonations\n---------\n\nIf you like the software, please donate:\n\nhttp://alphapy.readthedocs.io/en/latest/introduction/support.html#donations\n\n\n.. |badge_pypi| image:: https://badge.fury.io/py/alphapy.svg\n.. |badge_docs| image:: https://readthedocs.org/projects/alphapy/badge/?version=latest\n.. |badge_downloads| image:: https://static.pepy.tech/badge/alphapy\n","funding_links":["https://github.com/sponsors/ScottfreeLLC"],"categories":["其他_机器学习与深度学习","Artificial Intelligence","Python","超参数优化和AutoML","Libraries"],"sub_categories":["Machine Learning","Trading \u0026 Backtesting","交易与回测"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FScottfreeLLC%2FAlphaPy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FScottfreeLLC%2FAlphaPy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FScottfreeLLC%2FAlphaPy/lists"}