| Name | alphalens-reloaded JSON |
| Version |
0.4.5
JSON |
| download |
| home_page | None |
| Summary | Performance analysis of predictive (alpha) stock factors |
| upload_time | 2024-09-26 09:39:39 |
| maintainer | Stefan Jansen |
| docs_url | None |
| author | Quantopian Inc |
| requires_python | >=3.9 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. 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If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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<p align="center">
<a href="https://alphalens.ml4trading.io">
<img src="https://i.imgur.com/uf8PmQO.png" width="35%">
</a>
</p>
[](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/conda_package.yml)
[](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/unit_tests.yml)
[](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/build_wheels.yml)
[](https://coveralls.io/github/stefan-jansen/alphalens-reloaded?branch=main)
Alphalens is a Python library for performance analysis of predictive
(alpha) stock factors. Alphalens works great with the
[Zipline](https://www.zipline.ml4trading.io/) open source backtesting library, and [Pyfolio](https://github.com/quantopian/pyfolio) which provides performance and risk analysis of financial portfolios.
The main function of Alphalens is to surface the most relevant statistics and plots about an alpha factor, including:
- Returns Analysis
- Information Coefficient Analysis
- Turnover Analysis
- Grouped Analysis
# Getting started
With a signal and pricing data creating a factor \"tear sheet\" is a two step process:
```python
import alphalens
# Ingest and format data
factor_data = alphalens.utils.get_clean_factor_and_forward_returns(my_factor,
pricing,
quantiles=5,
groupby=ticker_sector,
groupby_labels=sector_names)
# Run analysis
alphalens.tears.create_full_tear_sheet(factor_data)
```
# Learn more
Check out the [example notebooks](https://github.com/stefan-jansen/alphalens-reloaded/tree/master/alphalens/examples)
for more on how to read and use the factor tear sheet.
# Installation
Install with pip:
pip install alphalens-reloaded
Install with conda:
conda install -c ml4t alphalens-reloaded
Install from the master branch of Alphalens repository (development code):
pip install git+https://github.com/stefan-jansen/alphalens-reloaded
Alphalens depends on:
- [matplotlib](https://github.com/matplotlib/matplotlib)
- [numpy](https://github.com/numpy/numpy)
- [pandas](https://github.com/pandas-dev/pandas)
- [scipy](https://github.com/scipy/scipy)
- [seaborn](https://github.com/mwaskom/seaborn)
- [statsmodels](https://github.com/statsmodels/statsmodels)
> Note that Numpy>=2.0 requires pandas>=2.2.2. If you are using an older version of pandas, you may need to upgrade
> accordingly, otherwise you may encounter compatibility issues.
# Usage
A good way to get started is to run the examples in a [Jupyter notebook](https://jupyter.org/).
To get set up with an example, you can:
Run a Jupyter notebook server via:
```bash
jupyter notebook
```
From the notebook list page(usually found at `http://localhost:8888/`), navigate over to the examples directory, and open any file with a .ipynb extension.
Execute the code in a notebook cell by clicking on it and hitting Shift+Enter.
# Questions?
If you find a bug, feel free to open an issue on our [github tracker](https://github.com/stefan-jansen/alphalens-reloaded/issues).
# Contribute
If you want to contribute, a great place to start would be the
[help-wanted issues](https://github.com/stefan-jansen/alphalens-reloaded/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22).
# Credits
- [Andrew Campbell](https://github.com/a-campbell)
- [James Christopher](https://github.com/jameschristopher)
- [Thomas Wiecki](https://github.com/twiecki)
- [Jonathan Larkin](https://github.com/marketneutral)
- Jessica Stauth (<jstauth@quantopian.com>)
- [Taso Petridis](https://github.com/tasopetridis)
For a full list of contributors see the [contributors page.](https://github.com/stefan-jansen/alphalens-reloaded/graphs/contributors)
# Example Tear Sheets
Example factor courtesy of [ExtractAlpha](https://extractalpha.com/)
## Peformance Metrics Tables
## Returns Tear Sheet
## Information Coefficient Tear Sheet
## Sector Tear Sheet
Raw data
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"description": "<p align=\"center\">\n<a href=\"https://alphalens.ml4trading.io\">\n<img src=\"https://i.imgur.com/uf8PmQO.png\" width=\"35%\">\n</a>\n</p>\n\n\n[](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/conda_package.yml)\n[](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/unit_tests.yml)\n[](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/build_wheels.yml)\n[](https://coveralls.io/github/stefan-jansen/alphalens-reloaded?branch=main)\n\n\n\n\n\nAlphalens is a Python library for performance analysis of predictive\n(alpha) stock factors. Alphalens works great with the\n[Zipline](https://www.zipline.ml4trading.io/) open source backtesting library, and [Pyfolio](https://github.com/quantopian/pyfolio) which provides performance and risk analysis of financial portfolios.\n\nThe main function of Alphalens is to surface the most relevant statistics and plots about an alpha factor, including:\n\n- Returns Analysis\n- Information Coefficient Analysis\n- Turnover Analysis\n- Grouped Analysis\n\n# Getting started\n\nWith a signal and pricing data creating a factor \\\"tear sheet\\\" is a two step process:\n\n```python\nimport alphalens\n\n# Ingest and format data\nfactor_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\nalphalens.tears.create_full_tear_sheet(factor_data)\n```\n\n# Learn more\n\nCheck out the [example notebooks](https://github.com/stefan-jansen/alphalens-reloaded/tree/master/alphalens/examples)\nfor more on how to read and use the factor tear sheet.\n\n# Installation\n\nInstall with pip:\n\n pip install alphalens-reloaded\n\nInstall with conda:\n\n conda install -c ml4t alphalens-reloaded\n\nInstall from the master branch of Alphalens repository (development code):\n\n pip install git+https://github.com/stefan-jansen/alphalens-reloaded\n\nAlphalens depends on:\n\n- [matplotlib](https://github.com/matplotlib/matplotlib)\n- [numpy](https://github.com/numpy/numpy)\n- [pandas](https://github.com/pandas-dev/pandas)\n- [scipy](https://github.com/scipy/scipy)\n- [seaborn](https://github.com/mwaskom/seaborn)\n- [statsmodels](https://github.com/statsmodels/statsmodels)\n\n> Note that Numpy>=2.0 requires pandas>=2.2.2. If you are using an older version of pandas, you may need to upgrade\n> accordingly, otherwise you may encounter compatibility issues.\n\n# Usage\n\nA good way to get started is to run the examples in a [Jupyter notebook](https://jupyter.org/).\n\nTo get set up with an example, you can:\n\nRun a Jupyter notebook server via:\n\n```bash\njupyter notebook\n```\n\nFrom the notebook list page(usually found at `http://localhost:8888/`), navigate over to the examples directory, and open any file with a .ipynb extension.\n\nExecute the code in a notebook cell by clicking on it and hitting Shift+Enter.\n\n# Questions?\n\nIf you find a bug, feel free to open an issue on our [github tracker](https://github.com/stefan-jansen/alphalens-reloaded/issues).\n\n# Contribute\n\nIf you want to contribute, a great place to start would be the\n[help-wanted issues](https://github.com/stefan-jansen/alphalens-reloaded/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22).\n\n# Credits\n\n- [Andrew Campbell](https://github.com/a-campbell)\n- [James Christopher](https://github.com/jameschristopher)\n- [Thomas Wiecki](https://github.com/twiecki)\n- [Jonathan Larkin](https://github.com/marketneutral)\n- Jessica Stauth (<jstauth@quantopian.com>)\n- [Taso Petridis](https://github.com/tasopetridis)\n\nFor a full list of contributors see the [contributors page.](https://github.com/stefan-jansen/alphalens-reloaded/graphs/contributors)\n\n# Example Tear Sheets\n\nExample factor courtesy of [ExtractAlpha](https://extractalpha.com/)\n\n## Peformance Metrics Tables\n\n\n\n## Returns Tear Sheet\n\n\n\n## Information Coefficient Tear Sheet\n\n\n\n## Sector Tear Sheet\n\n\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. 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For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"{}\" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright 2018 Quantopian, Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ",
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