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

Improvements to findability through README:

* important links and buttons are moved to the top
* new buttons (added from links)
* formatting updates

Author: fkiraly

README.md

@@ -1,46 +1,28 @@
-<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.
 
@@ -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