McPortfolio
Provides 9 specialized tools for LLM-driven portfolio optimization using natural language, covering strategies from mean-variance to machine learning approaches.
About
Empower Large Language Models (LLMs) to perform sophisticated portfolio optimization through natural language. McPortfolio acts as an MCP server, offering 9 specialized tools that translate user requests into actions using the PyPortfolioOpt library and CVXPY solver. This allows for diverse investment strategies, from classic Markowitz mean-variance optimization and efficient frontier analysis to modern techniques like Hierarchical Risk Parity and the Black-Litterman model, all driven by intuitive language commands without requiring direct coding from the end-user.
Key Features
- Leverages PyPortfolioOpt for financial calculations and CVXPY for convex optimization.
- Utilize natural language to define optimization problems, constraints, and objectives without coding.
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- Includes utilities for stock data retrieval and converting portfolio weights to discrete share allocations.
- Covers diverse strategies including Efficient Frontier, Black-Litterman, and Hierarchical Risk Parity.
- Access 9 specialized tools for LLM-driven portfolio optimization, from mean-variance to machine learning methods.
Use Cases
- Optimize investment portfolios by specifying objectives (e.g., maximize Sharpe ratio, minimize volatility) and constraints through natural language.
- Construct portfolios using various methodologies such as classic Mean-Variance, modern Hierarchical Risk Parity, or view-integrated Black-Litterman.
- Convert theoretical optimized asset weights into practical, tradable share allocations based on total portfolio value.
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