PyPortfolioOpt
Unverified Other strategy on Multi by PyPortfolio. BotFinder score 18 out of 100.
Financial portfolio optimization in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
Source: github
BotFinder analysis pending.
PyPortfolioOpt
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 class
⚠ No verified equity curve — no track-record source connected.
Drawdown profile
Data unavailable — contact the owner.
Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
No score history is stored yet — only the current score is shown.
Reviews & comments
No reviews collected from the source yet.
⚠ No live verification account connected — ask for proof before buying.
Alerts on changes: coming soon
Prop-firm compatibility not provided.
Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)