{"id":13460928,"url":"https://github.com/quantopian/alphalens","last_synced_at":"2025-10-07T22:42:55.016Z","repository":{"id":39493888,"uuid":"60379729","full_name":"quantopian/alphalens","owner":"quantopian","description":"Performance analysis of predictive (alpha) stock factors","archived":false,"fork":false,"pushed_at":"2024-02-12T06:44:22.000Z","size":90642,"stargazers_count":3686,"open_issues_count":49,"forks_count":1220,"subscribers_count":168,"default_branch":"master","last_synced_at":"2025-05-11T12:39:03.063Z","etag":null,"topics":["algorithmic-trading","finance","jupyter","numpy","pandas","python"],"latest_commit_sha":null,"homepage":"http://quantopian.github.io/alphalens","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/quantopian.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-06-03T21:49:15.000Z","updated_at":"2025-05-10T14:06:03.000Z","dependencies_parsed_at":"2022-07-18T08:17:20.679Z","dependency_job_id":"c6295c03-a396-4860-b89d-63cbdceb3f61","html_url":"https://github.com/quantopian/alphalens","commit_stats":{"total_commits":451,"total_committers":27,"mean_commits":"16.703703703703702","dds":0.6873614190687362,"last_synced_commit":"77084f1e4c2c0be407e032d444fb19e4be4b0f37"},"previous_names":[],"tags_count":13,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quantopian%2Falphalens","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quantopian%2Falphalens/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quantopian%2Falphalens/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/quantopian%2Falphalens/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/quantopian","download_url":"https://codeload.github.com/quantopian/alphalens/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254049296,"owners_count":22006034,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["algorithmic-trading","finance","jupyter","numpy","pandas","python"],"created_at":"2024-07-31T10:00:50.956Z","updated_at":"2025-10-07T22:42:49.989Z","avatar_url":"https://github.com/quantopian.png","language":"Jupyter Notebook","readme":".. image:: https://media.quantopian.com/logos/open_source/alphalens-logo-03.png\n    :align: center\n\nAlphalens\n=========\n.. image:: https://github.com/quantopian/alphalens/workflows/CI/badge.svg\n    :alt: GitHub Actions status\n    :target: https://github.com/quantopian/alphalens/actions?query=workflow%3ACI+branch%3Amaster\n\nAlphalens is a Python Library for performance analysis of predictive\n(alpha) stock factors. Alphalens works great with the\n`Zipline \u003chttps://www.zipline.io/\u003e`__ open source backtesting library, and\n`Pyfolio \u003chttps://github.com/quantopian/pyfolio\u003e`__ which provides\nperformance and risk analysis of financial portfolios. You can try Alphalens\nat  `Quantopian \u003chttps://www.quantopian.com\u003e`_ -- a free,\ncommunity-centered, hosted platform for researching and testing alpha ideas. \nQuantopian also offers a `fully managed service for professionals \u003chttps://factset.quantopian.com\u003e`_ \nthat includes Zipline, Alphalens, Pyfolio, FactSet data, and more.\n\nThe main function of Alphalens is to surface the most relevant statistics\nand plots about an alpha factor, including:\n\n-  Returns Analysis\n-  Information Coefficient Analysis\n-  Turnover Analysis\n-  Grouped Analysis\n\nGetting started\n---------------\n\nWith a signal and pricing data creating a factor \"tear sheet\" is a two step process:\n\n.. code:: python\n\n    import alphalens\n    \n    # Ingest and format data\n    factor_data = alphalens.utils.get_clean_factor_and_forward_returns(my_factor, \n                                                                       pricing, \n                                                                       quantiles=5,\n                                                                       groupby=ticker_sector,\n                                                                       groupby_labels=sector_names)\n\n    # Run analysis\n    alphalens.tears.create_full_tear_sheet(factor_data)\n\n\nLearn more\n----------\n\nCheck out the `example notebooks \u003chttps://github.com/quantopian/alphalens/tree/master/alphalens/examples\u003e`__ for more on how to read and use\nthe factor tear sheet.  A good starting point could be `this \u003chttps://github.com/quantopian/alphalens/tree/master/alphalens/examples/alphalens_tutorial_on_quantopian.ipynb\u003e`__\n\nInstallation\n------------\n\nInstall with pip:\n\n::\n\n    pip install alphalens\n\nInstall with conda: \n\n::\n\n    conda install -c conda-forge alphalens\n\nInstall from the master branch of Alphalens repository (development code):\n\n::\n\n    pip install git+https://github.com/quantopian/alphalens\n\nAlphalens depends on:\n\n-  `matplotlib \u003chttps://github.com/matplotlib/matplotlib\u003e`__\n-  `numpy \u003chttps://github.com/numpy/numpy\u003e`__\n-  `pandas \u003chttps://github.com/pandas-dev/pandas\u003e`__\n-  `scipy \u003chttps://github.com/scipy/scipy\u003e`__\n-  `seaborn \u003chttps://github.com/mwaskom/seaborn\u003e`__\n-  `statsmodels \u003chttps://github.com/statsmodels/statsmodels\u003e`__\n\nUsage\n-----\n\nA good way to get started is to run the examples in a `Jupyter\nnotebook \u003chttps://jupyter.org/\u003e`__.\n\nTo get set up with an example, you can:\n\nRun a Jupyter notebook server via:\n\n.. code:: bash\n\n    jupyter notebook\n\nFrom the notebook list page(usually found at\n``http://localhost:8888/``), navigate over to the examples directory,\nand open any file with a .ipynb extension.\n\nExecute the code in a notebook cell by clicking on it and hitting\nShift+Enter.\n\nQuestions?\n----------\n\nIf you find a bug, feel free to open an issue on our `github\ntracker \u003chttps://github.com/quantopian/alphalens/issues\u003e`__.\n\nContribute\n----------\n\nIf you want to contribute, a great place to start would be the\n`help-wanted\nissues \u003chttps://github.com/quantopian/alphalens/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22\u003e`__.\n\nCredits\n-------\n\n-  `Andrew Campbell \u003chttps://github.com/a-campbell\u003e`__\n-  `James Christopher \u003chttps://github.com/jameschristopher\u003e`__\n-  `Thomas Wiecki \u003chttps://github.com/twiecki\u003e`__\n-  `Jonathan Larkin \u003chttps://github.com/marketneutral\u003e`__\n-  Jessica Stauth (jstauth@quantopian.com)\n-  `Taso Petridis \u003chttps://github.com/tasopetridis\u003e`_\n\nFor a full list of contributors see the `contributors page. \u003chttps://github.com/quantopian/alphalens/graphs/contributors\u003e`_\n\nExample Tear Sheet\n------------------\n\nExample factor courtesy of `ExtractAlpha \u003chttps://extractalpha.com/\u003e`_\n\n.. image:: https://github.com/quantopian/alphalens/raw/master/alphalens/examples/table_tear.png\n.. image:: https://github.com/quantopian/alphalens/raw/master/alphalens/examples/returns_tear.png\n.. image:: https://github.com/quantopian/alphalens/raw/master/alphalens/examples/ic_tear.png\n.. image:: https://github.com/quantopian/alphalens/raw/master/alphalens/examples/sector_tear.png\n    :alt:\n","funding_links":[],"categories":["Research Tools","Jupyter Notebook","Statistics","金融数据处理","Python","金融股票"],"sub_categories":["Arbitrage","NLP","Factor Analysis","网络服务_其他","因子分析","Automated Machine Learning"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantopian%2Falphalens","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquantopian%2Falphalens","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquantopian%2Falphalens/lists"}