diff --git a/README.md b/README.md index 94ee1e66..e8f762f3 100755 --- a/README.md +++ b/README.md @@ -1,46 +1,28 @@ -

- -

+## Welcome to PyPortfolioOpt - -

- - python   - - platforms   - - pypi   - - MIT license   - - build   - - downloads   - - binder   -

+ - +PyPortfolioOpt is a library implementing portfolio optimization methods, including +classical mean-variance optimization, Black-Litterman allocation, or shrinkage and Hierarchical Risk Parity. +PyPortfolioOpt is inspired by scikit-learn; it is **extensive** yet easily **extensible**, for casual investors, or professionals looking for an easy prototyping tool. Whether you are a fundamentals-oriented investor who has identified a +handful of undervalued picks, or an algorithmic trader who has a basket of +strategies, PyPortfolioOpt can help you combine your alpha sources in a risk-efficient way. -PyPortfolioOpt is a library that implements portfolio optimization methods, including -classical mean-variance optimization techniques and Black-Litterman allocation, as well as more -recent developments in the field like shrinkage and Hierarchical Risk Parity. -It is **extensive** yet easily **extensible**, and can be useful for either a casual investors, or a professional looking for an easy prototyping tool. Whether you are a fundamentals-oriented investor who has identified a -handful of undervalued picks, or an algorithmic trader who has a basket of -strategies, PyPortfolioOpt can help you combine your alpha sources -in a risk-efficient way. + -**PyPortfolioOpt has been [published](https://joss.theoj.org/papers/10.21105/joss.03066) in the Journal of Open Source Software 🎉** +| | **[Documentation](https://pyportfolioopt.readthedocs.io/en/latest/)** · **[Tutorials](https://github.com/pyportfolio/pyportfolioopt/tree/main/cookbook)** · **[Release Notes](https://github.com/PyPortfolio/PyPortfolioOpt/releases)** | +|---|---| +| **Open Source** | [![MIT](https://img.shields.io/github/license/pyportfolio/pyportfolioopt)](https://github.com/pyportfolio/pyportfolioopt/blob/master/LICENSE) [![GC.OS Sponsored](https://img.shields.io/badge/GC.OS-Sponsored%20Project-orange.svg?style=flat&colorA=0eac92&colorB=2077b4)](https://gc-os-ai.github.io/) | | +| **Tutorials** | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pyportfolio/pyportfolioopt/main/?filepath=cookbook) | +| **Community** | [![!discord](https://img.shields.io/static/v1?logo=discord&label=discord&message=chat&color=lightgreen)](https://discord.gg/7uKdHfdcJG) [![!slack](https://img.shields.io/static/v1?logo=linkedin&label=LinkedIn&message=news&color=lightblue)](https://www.linkedin.com/company/pyportfolioopt/) | +| **CI/CD** | [![github-actions](https://img.shields.io/github/actions/workflow/status/pyportfolio/pyportfolioopt/main.yml?logo=github)](https://github.com/pyportfolio/pyportfolioopt/actions/workflows/release.yml) [![readthedocs](https://img.shields.io/readthedocs/pyportfolioopt?logo=readthedocs)](https://pyportfolioopt.readthedocs.io/en/latest/?badge=latest) | +| **Code** | [![!pypi](https://img.shields.io/pypi/v/pyportfolioopt?color=orange)](https://pypi.org/project/pyportfolioopt/) [![!python-versions](https://img.shields.io/pypi/pyversions/pyportfolioopt)](https://www.python.org/) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) | +| **Downloads** | ![PyPI - Downloads](https://img.shields.io/pypi/dw/pyportfolioopt) ![PyPI - Downloads](https://img.shields.io/pypi/dm/pyportfolioopt) [![Downloads](https://static.pepy.tech/personalized-badge/pyportfolioopt?period=total&units=international_system&left_color=grey&right_color=blue&left_text=cumulative%20(pypi))](https://pepy.tech/project/pyportfolioopt) | +| **Citation** | [JOSS article](https://joss.theoj.org/papers/10.21105/joss.03066) | -PyPortfolioOpt is now being maintained by [Tuan Tran](https://github.com/88d52bdba0366127fffca9dfa93895). + + Head over to the **[documentation on ReadTheDocs](https://pyportfolioopt.readthedocs.io/en/latest/)** to get an in-depth look at the project, or check out the [cookbook](https://github.com/pyportfolio/pyportfolioopt/tree/main/cookbook) to see some examples showing the full process from downloading data to building a portfolio. @@ -352,7 +334,7 @@ SBUX: 0.0695 ### Black-Litterman allocation -As of v0.5.0, we now support Black-Litterman asset allocation, which allows you to combine +Pyportfolioopt supports Black-Litterman asset allocation, which allows you to combine a prior estimate of returns (e.g the market-implied returns) with your own views to form a posterior estimate. This results in much better estimates of expected returns than just using the mean historical return. Check out the [docs](https://pyportfolioopt.readthedocs.io/en/latest/BlackLitterman.html) for a discussion of the theory, as well as advice @@ -483,7 +465,7 @@ BibTex:: Contributions are _most welcome_. Have a look at the [Contribution Guide](https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/CONTRIBUTING.md) for more. -I'd like to thank all of the people who have contributed to PyPortfolioOpt since its release in 2018. +We'd like to thank all of the people who have contributed to PyPortfolioOpt since its release in 2018. Special shout-outs to: - Tuan Tran