| Name | alphalens-tej JSON |
| Version |
2.0.2
JSON |
| download |
| home_page | |
| Summary | Performance analysis of predictive (alpha) stock factors |
| upload_time | 2024-03-13 07:19:24 |
| maintainer | tej |
| docs_url | None |
| author | tej |
| requires_python | >=3.8 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<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)
# 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
{
"_id": null,
"home_page": "",
"name": "alphalens-tej",
"maintainer": "tej",
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "tej@tej.com.tw",
"keywords": "",
"author": "tej",
"author_email": "tej@tej.com.tw",
"download_url": "https://files.pythonhosted.org/packages/16/44/648c1522893ec3d6cb25ad11cf9d9a26ec2b04ef18a209efacb8ab28cf70/alphalens-tej-2.0.2.tar.gz",
"platform": null,
"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# 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-2.0",
"summary": "Performance analysis of predictive (alpha) stock factors",
"version": "2.0.2",
"project_urls": {
"homepage": "https://tquant.tejwin.com/",
"repository": "https://github.com/tejtw/alphalens-tej"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "ece57eada2225f1d17fc062a994d3297cc0edead2886786141d3aa103f006d3c",
"md5": "9d960b62ce1c8c011762789dfe0bce4b",
"sha256": "ce4c2b75687a8b95c5c2bbf619d24e8d5f193f9b511bd23e728f47af49072ddb"
},
"downloads": -1,
"filename": "alphalens_tej-2.0.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "9d960b62ce1c8c011762789dfe0bce4b",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 42184,
"upload_time": "2024-03-13T07:19:22",
"upload_time_iso_8601": "2024-03-13T07:19:22.936912Z",
"url": "https://files.pythonhosted.org/packages/ec/e5/7eada2225f1d17fc062a994d3297cc0edead2886786141d3aa103f006d3c/alphalens_tej-2.0.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "1644648c1522893ec3d6cb25ad11cf9d9a26ec2b04ef18a209efacb8ab28cf70",
"md5": "c67b14d19a9d21d7161268e4eaa82356",
"sha256": "7cce4d393d51e070267a2565182f321d33455a4b80408d5dfc1d726d461af053"
},
"downloads": -1,
"filename": "alphalens-tej-2.0.2.tar.gz",
"has_sig": false,
"md5_digest": "c67b14d19a9d21d7161268e4eaa82356",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 7170977,
"upload_time": "2024-03-13T07:19:24",
"upload_time_iso_8601": "2024-03-13T07:19:24.270822Z",
"url": "https://files.pythonhosted.org/packages/16/44/648c1522893ec3d6cb25ad11cf9d9a26ec2b04ef18a209efacb8ab28cf70/alphalens-tej-2.0.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-03-13 07:19:24",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "tejtw",
"github_project": "alphalens-tej",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "alphalens-tej"
}
