riskprism

Decompose US equity portfolio risk into its factor spectrum.

Explorer: https://risk-prism-production.up.railway.app · Agent model card: /model.md

An open-source, Barra-style fundamental factor risk model built to be usable by AI agents out of the box: a Python library, an MCP server, and weekly-published model artifacts covering most liquid US common stocks.

• 9 style factors (size, value, growth, momentum, beta, volatility, liquidity, quality, leverage — value, quality and leverage are multi-descriptor composites, volatility is beta-orthogonalized residual volatility) + 30 industries (Fama-French scheme) + a market factor
• Free, redistributable data chain: fundamentals and SIC codes from SEC EDGAR (public domain), prices from pluggable providers
• Hybrid distribution: precomputed artifacts (exposures, factor covariance, specific risk) are published on a weekly schedule, and the full pipeline is open so anyone can reproduce or extend them

Disclaimer: research software, provided as-is. Nothing here is investment advice.

Hosted API (no key, no signup)

The live deployment serves a JSON API over the newest weekly build — interactive docs at /api/docs:

curl -s -X POST https://risk-prism-production.up.railway.app/api/v1/portfolio-risk \
  -H 'content-type: application/json' \
  -d '{"weights": {"AAPL": 0.4, "MSFT": 0.4, "XOM": 0.2}}'

Endpoints: GET /api/v1/meta · GET /api/v1/factors · GET /api/v1/assets/{ticker} · GET /api/v1/coverage?tickers=… · POST /api/v1/portfolio-risk · POST /api/v1/stress-test · GET /api/v1/registry (catalog of published builds). Same surface as the MCP server; self-host it with pip install ".[api]" && riskprism-api (artifacts auto-download from the latest release at boot). Details in docs/API.md.

For AI agents (MCP)

Hosted — no install, serving the newest weekly build:

{
  "mcpServers": {
    "riskprism": {
      "type": "http",
      "url": "https://risk-prism-production.up.railway.app/mcp"
    }
  }
}

Or local, from the installed package and downloaded artifacts:

{
  "mcpServers": {
    "riskprism": {
      "command": "riskprism-mcp",
      "env": { "RISKPRISM_ARTIFACTS": "/path/to/artifacts" }
    }
  }
}

Tools exposed: get_model_info, get_portfolio_risk, get_factor_exposures, stress_test, check_coverage, list_model_versions. Weights are portfolio weights (shorts negative); volatilities are annualized decimals.

Get the latest model (no key, no signup)

pip install riskprism
mkdir -p artifacts && curl -L https://github.com/wanxinwanxin/risk-prism/releases/latest/download/riskprism-artifacts.tar.gz | tar xz -C artifacts

For humans (Python)

from riskprism import RiskModel

model = RiskModel.load("artifacts")
report = model.portfolio_risk({"AAPL": 0.4, "MSFT": 0.3, "XOM": 0.3})
print(report["total_vol"], report["factor_var_contributions"])

model.stress_test({"AAPL": 1.0}, {"market": -0.10, "momentum": -0.05})

Build the model yourself

pip install -e ".[dev]"
export RISKPRISM_EDGAR_UA="your-project (you@example.com)"   # SEC fair-access policy
riskprism-build --max-names 3000 --out artifacts             # yahoo prices, no key needed
riskprism-build --prior artifacts_prev --out artifacts       # append new weeks to a prior build
riskprism-build --provider tiingo ...                        # licensed data, needs TIINGO_API_KEY

The weekly GitHub Action runs exactly this and publishes the artifact directory; see .github/workflows/build-model.yml.

The explorer

A zero-backend static site (served on Railway, re-rendered by each weekly build) for exploring the model: cumulative factor returns, factor vol and correlations, a client-side portfolio risk sandbox with stress-test sliders, per-stock factor profiles, and a visual methodology walkthrough. All math runs in the browser on the embedded artifacts.

Agents get a plain-markdown mirror of every build at /model.md (indexed by /llms.txt): model card, factor definitions, correlations, and the full coverage list — no DOM parsing required.

Render everything locally:

riskprism-site --artifacts artifacts --out site   # index.html + model.md + llms.txt

Model summary

Full methodology in docs/METHODOLOGY.md; design decisions and their rationale in docs/DECISIONS.md.

License

MIT for code. Published model artifacts are derived data built from SEC EDGAR (public domain) and third-party price providers — see docs/DECISIONS.md for the data-licensing discussion.