Portfolio optimization pitfalls

Quant Science positions itself as an applied quant-education shop led by founders Matt and Jason and publishes hands‑on Python tutorials, a newsletter and GitHub repos with vectorbt/zipline examples used for portfolio construction. (quantscience.io) A long-form Quant Science tutorial, “Portfolio Optimization with Riskfolio‑Lib,” (Apr 13, 2024) walks through nine core functions and includes full Python code for alternative objectives beyond vanilla mean‑variance. (quantscience.io) A separate QS piece, “How to Use Correlation to Construct Investment Portfolios in Python,” shows a worked example where the optimizer allocates over 50% to GLD to reduce correlation with a technology-heavy sleeve, illustrating their emphasis on correlation‑aware allocations. (quantscience.io) Technical failures that make the naive efficient frontier brittle are well documented: sample covariance matrices can be singular or poorly estimated, and expected‑return inputs produce extreme, unstable weights — issues highlighted in interview‑level MPT guides and quant Q&A threads. (datainterview.com) Accepted fixes that QS and the broader quant literature promote include covariance shrinkage or alternative risk models (PyPortfolioOpt supports non‑covariance risk models), Bayesian/Black‑Litterman adjustments for expected returns, and shifting optimization from raw assets to low‑correlation strategies or factor sleeves. (pyportfolioopt.readthedocs.io) Concrete checklist actions appearing across QS tutorials and academic/practitioner sources are: implement shrinkage or robust risk models, run walk‑forward or k‑fold portfolio backtests, cluster and de‑duplicate strategies by correlation before allocating, and codify constraints in Riskfolio‑Lib or PyPortfolioOpt to produce operational efficient frontiers. (quantscience.io)