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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
| Date | Stars |
|---|---|
| 2026-07-24 | 5889 |
| 2026-07-25 | 5890 |
| 2026-07-28 | 5890 |
| 2026-07-30 | 5890 |
| 2026-08-06 | 5890 |
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growth rate 0.00%/day
## Welcome to PyPortfolioOpt <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> 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. <!-- buttons --> | | **[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** | [](https://github.com/pyportfolio/pyportfolioopt/blob/main/LICENSE) [](https://gc-os-ai.github.io/) | | | **Community** | [](https://discord.gg/7uKdHfdcJG) [](https://www.linkedin.com/company/pyportfolioopt/) | | **CI/CD** | [](https://github.com/pyportfolio/pyportfolioopt/actions/workflows/main.yml) [](https://pyportfolioopt.readthedocs.io/en/latest/?badge=latest) | | **Code** | [](https://pypi.org/project/pyportfolioopt/) [](https://www.python.org/) [](https://github.com/psf/black) | | **Downloads** |   [](https://pepy.tech/project/pyportfolioopt) | | **Citation** | [JOSS article](https://joss.theoj.org/papers/10.21105/joss.03066) | <!-- 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. <center> <img src="https://github.com/PyPortfolio/PyPortfolioOpt/blob/main/media/conceptual_flowchart_v2.png?raw=true" style="width:70%;"/> </center> ## Table of contents - [Table of contents](#table-of-contents) - [Getting started](#getting-started) - [For development](#for-development) - [A quick example](#a-quick-example) - [An overview of classical portfolio optimization methods](#an-overview-of-classical-portfolio-optimization-methods) - [Features](#features) - [Expected returns](#expected-returns) - [Risk models (covariance)](#risk-models-covariance) - [Objective functions](#objective-functions) - [Adding constraints or different objectives](#adding-constraints-or-different-objectives) - [Black-Litterman allocation](#black-litterman-allocation) - [Other optimizers](#other-optimizers) - [Advantages over existing implementations](#advantages-over-existing-imp
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6ae5ee8030c1a816, topic:finance, topic:quantitative-finance, desc:financial