Empyrical is a Python library for performance and risk analysis of financial portfolios, providing common financial risk metrics like Sharpe ratio, max drawdown, and alpha/beta calculations.

What risk metrics does this server provide?

It provides access to empyrical-based analytics including portfolio performance metrics, risk-adjusted return measures, drawdown analysis, and other quantitative risk indicators.

Do I need my own portfolio data to use this?

Yes. The server provides the analytical tools — users typically need to supply portfolio return data or position information for the analytics to process.

Empyrical MCP | CLI Trader

Empyrical MCP is an MCP server that provides portfolio analytics and risk metrics based on the empyrical Python library. It brings quantitative portfolio analysis capabilities to MCP-compatible agents, enabling them to compute risk-adjusted returns, drawdown metrics, and other performance indicators that are standard in professional portfolio management.

The server exposes tools for computing common financial risk and performance metrics. This includes measures like Sharpe ratio, Sortino ratio, maximum drawdown, alpha, beta, and other analytics that portfolio managers and quantitative analysts rely on. By making these calculations available through MCP tools, agents can perform portfolio-level risk assessment as part of their analytical workflows without requiring the user to write custom quantitative code.

Setup requires a Python environment with the empyrical library and standard MCP server configuration. Users register the server in their MCP client and provide portfolio data for analysis. The server is focused on computation and analytics — it calculates metrics from supplied data rather than retrieving market data itself. For a complete workflow, it pairs well with data retrieval MCP servers that provide the price and return data needed as inputs. This combination enables agents to handle the full pipeline from data gathering through quantitative analysis.