{"repo":"PyPortfolio/PyPortfolioOpt","free":true,"listed":false,"github":"https://github.com/PyPortfolio/PyPortfolioOpt","clone":"git clone https://github.com/PyPortfolio/PyPortfolioOpt.git","description":"Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity","language":"Jupyter Notebook","stars":5966,"topics":["finance","portfolio-optimization","portfolio-management","quantitative-finance","algorithmic-trading","investing","efficient-frontier","covariance","python","investment"],"license":"MIT","category":"trading","readme_excerpt":"Welcome to PyPortfolioOpt 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. Documentation · Tutorials · Release Notes --- --- Open Source Community CI/CD Code Downloads Citation JOSS article Head over to the documentation on ReadTheDocs to get an in-depth look at the project, or check out the cookbook to see some examples showing the full process from downloading data to building a portfolio. Table of contents - Table of contents - Getting started - For development - A quick example - An overview of classical portfolio optimization methods - Features - Expected returns - Risk models (covariance) - Objective functions - Adding constraints or different objectives - Black-Litterman allocation - Other optimizers - Advantages over existing implementations - Project principles and design decisions - Testing - Citing PyPortfolioOpt - Contributing - Getting in touch 🚀 Installation Using pip From source Clone the repository, navigate to the folder, ","default_branch":null,"files":null,"tree":[],"storefront":"/r/PyPortfolio","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/PyPortfolio/PyPortfolioOpt/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}