Enhanced Portfolio Optimization with PSO and Bayesian Shrinkage

[AssetMatrix500]

I've been a longtime user of OpenBB Terminal for investment research and absolutely love its mission to democratize investment tools. While exploring portfolio construction approaches, I noticed an opportunity to extend the ecosystem with specialized optimization techniques.

I've developed a complementary package that implements Particle Swarm Optimization (PSO) with Bayesian shrinkage for portfolio construction:

https://github.com/AssetMatrix500/Portfolio-Optimization_Enhanced

Key features:

Metaheuristic optimization - PSO handles complex, non-convex constraints that traditional QP solvers sometimes struggle with
Dynamic Bayesian shrinkage - Significantly reduces estimation error in both returns and covariance matrices
Factor-based covariance models - More stable than sample covariance, especially during regime changes
Comprehensive visualization - Detailed efficient frontier analysis and drawdown comparisons

The approach shows particular strength in managing the portfolio weight stability between rebalancing periods - something I found challenging with traditional approaches.
I built this with the same democratization ethos as OpenBB - making institutional-grade techniques accessible to individual investors without requiring advanced mathematics.

Has anyone else experimented with metaheuristic approaches to portfolio construction?

I'm curious if others have found similar stability advantages compared to traditional optimizers when dealing with complex constraint sets.