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

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2025-02-07 DOI:10.1016/j.mex.2025.103211

Rushikesh Nakhate , Harikrishnan Ramachandran , Amay Mahajan

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引用次数: 0

Abstract

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.

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The study compares various configured methods for each step of the portfolio optimization pipeline, including expected returns, risk-modeling and optimization techniques.
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Custom Designed Allocator outperformed. For example, the Proportional Allocator's sharpe ratio of out-performed the expected average.
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A caching system was implemented to optimize execution time.

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