What is RiskModels?
RiskModels provides clean dividend-adjusted total returns, factor risk decomposition, return attribution, and ETF-executable hedge ratios for US equities. It is delivered over REST, a typed Python SDK, and a built-in MCP (Model Context Protocol) server. The dataset covers ~16,000 US stocks historically, with a monthly headline universe of the largest ~3,000 by market cap.
How to use RiskModels?
Install the Python SDK with pip install riskmodels-py, then create a RiskModelsClient and call methods like client.analyze(). For AI agent integration, connect to the hosted MCP server via SSE at https://riskmodels.app/api/mcp/sse or run the local MCP server with stdio transport for tools like riskmodels_decompose and riskmodels_hedge_position. A CLI tool is also available via npm.
Key features of RiskModels
- Daily factor decompositions for market, sector, and subsector
- Dollar-denominated ETF hedge ratios (L1/L2/L3) for liquid ETFs
- Historical split- and dividend-adjusted returns from 2006-01-04
- Built-in MCP server for AI agent and LLM integration
- Python SDK with agent-native helpers and LLM-ready context formatting
- OAuth2 authentication and per-request billing
Use cases of RiskModels
- Decompose portfolio risk into market, sector, and subsector components
- Obtain executable hedge ratios for individual US equity positions
- Perform return attribution for a universe of ~3,000 US stocks
- Integrate factor risk analysis directly into AI agent workflows via MCP
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