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[DOC] improved README - important links at the top #682

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60 changes: 21 additions & 39 deletions README.md
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<p align="center">
<img width=60% src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/logo_v1.png?raw=true">
</p>
## Welcome to PyPortfolioOpt

<!-- buttons -->
<p align="center">
<a href="https://www.python.org">
<img src="https://img.shields.io/pypi/pyversions/PyPortfolioOpt.svg"
alt="python"></a> &nbsp;
<a href="https://www.python.org">
<img src="https://img.shields.io/badge/Platforms-linux--64,win--64,osx--64-orange.svg?style=flat-square"
alt="platforms"></a> &nbsp;
<a href="https://pypi.org/project/PyPortfolioOpt/">
<img src="https://img.shields.io/badge/pypi-v1.5.6-brightgreen.svg"
alt="pypi"></a> &nbsp;
<a href="https://opensource.org/licenses/MIT">
<img src="https://img.shields.io/badge/license-MIT-brightgreen.svg"
alt="MIT license"></a> &nbsp;
<a href="https://github.com/pyportfolio/pyportfolioopt/actions">
<img src="https://github.com/pyportfolio/pyportfolioopt/actions/workflows/main.yml/badge.svg?branch=main"
alt="build"></a> &nbsp;
<a href="https://pepy.tech/project/pyportfolioopt">
<img src="https://pepy.tech/badge/pyportfolioopt"
alt="downloads"></a> &nbsp;
<a href="https://mybinder.org/v2/gh/pyportfolio/pyportfolioopt/main/?filepath=cookbook">
<img src="https://mybinder.org/badge_logo.svg"
alt="binder"></a> &nbsp;
</p>
<a href="https://pyportfolioopt.readthedocs.io/en/latest/"><img src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/logo_v1.png?raw=true" width="275" align="right" /></a>

<!-- content -->
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.
<!-- buttons -->

**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&#160;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).

<!-- content -->

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.

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### 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
Expand Down Expand Up @@ -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
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