PyPortOptimization: A portfolio optimization pipeline leveraging multiple expected return methods, risk models, and post-optimization allocation techniques

This paper presents PyPortOptimization, an automated portfolio optimization library that incorporates multiple methods for expected returns, risk return modeling, and portfolio optimization. The library offers a flexible and scalable solution for constructing optimized portfolios by supporting various risk-return matrices, covariance and correlation matrices, and optimization methods. Users can customize the pipeline at every step, from data acquisition to post-processing of portfolio weights, using their own methods or selecting from predefined options. Built-in Monte Carlo simulations help assess portfolio robustness, while performance metrics such as return, risk, and Sharpe ratio are calculated to evaluate optimization results.•The study compares various configured methods for each step of the portfolio optimization pipeline, including expected returns, risk-modeling and optimization techniques.•Custom Designed Allocator outperformed. For example, the Proportional Allocator's sharpe ratio of out-performed the expected average.•A caching system was implemented to optimize execution time.