dcajasn/Riskfolio-Lib ? reverse-engineered prompt

Build me a Python library for portfolio optimization that works nicely with pandas data and uses convex optimization under the hood.

I want it to help me take a table of asset returns and generate practical portfolios like minimum risk, maximum return, maximum utility, risk adjusted return, risk parity, hierarchical risk parity, and clustered portfolio methods. It should also support common risk measures like standard deviation, CVaR, drawdown based risk, Sortino style downside risk, and related frontier calculations.

Please include a clean, easy to use API, a few simple examples, and enough documentation so someone who is not a quant can still try it. I’d like plotting helpers for things like efficient frontiers and allocation charts, plus tests so the main methods are reliable. If something depends on current library behavior or best practices, look up the latest docs online if needed.

Make it feel polished and ready for people to install and use right away.