McPortfolio Optimization Server

Example Use Cases

• 1

  Perform flexible portfolio optimization with custom constraints and objectives.

• 2

  Apply classical and modern portfolio optimization methods via natural language.

• 3

  Convert theoretical portfolio weights into practical share allocations.

Description

McPortfolio offers a suite of 9 specialized portfolio optimization tools through a Model Context Protocol server, enabling users to perform classic and modern portfolio optimizations such as mean-variance, hierarchical risk parity, and Black-Litterman models without coding. It integrates PyPortfolioOpt features with LLMs to facilitate flexible, customizable portfolio construction and analysis. The server supports both general and specialized optimization scenarios, including constraints and discrete allocation for practical trading.