Baseline & Premium · First Agentic Risk API · MCP-ready

An Agentic Approach to Managing Equity Risk.

Baseline vs Premium

Baseline features ($0.001–$0.005/call) power everyday risk checks and time series. Premium capabilities unlock deeper L3 decomposition, portfolio-level risk indexing, PDF snapshots, and batch analytics — perfect for agents and power users.

From market, sector, and subsector attribution to automated hedging logic — calibrate your whole stack through one institutional-grade API.

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Read the Docs

Try a stock deep dive

AAPLMSFTNVDAAMZNGOOGMETATSLA

$0 upfront · $20 free credits · Pay per tiered call · No subscription · No seat fees

Built on

  • Python
  • Node
  • MCP
  • Docker
Live Demo

See the API in Action

Stylized demos (not live recordings) — REST and SDK shapes match the API; CLI 'agent' and some MCP tool names illustrate product workflows. Python SDK: pip install riskmodels-py on PyPI ([xarray] optional for cubes). CLI: npm install -g riskmodels-cli on npm. Use riskmodels --help and MCP tools/list for what your install exposes. sdk/ · cli/.

riskmodels agent decompose — L3 portfolio attributionportfolio: positions.json
$
[INFO] Loading portfolio: 8 holdings detected...
[INFO] Calling POST /batch/analyze (ERM3-L3-v30)...
[INFO] L3 attribution complete. Building response...
 
{
  "avg_l3_mkt_hr": 1.14,
  "dominant_factor": "market",
  "holdings": {
    "NVDA": { "l3_mkt_hr": 0.97, "l3_sec_hr": 0.14, "sector_etf": "SOXX" },
    "MSFT": { "l3_mkt_hr": 0.88, "l3_sec_hr": 0.11, "sector_etf": "XLK" },
    "AAPL": { "l3_mkt_hr": 0.85, "l3_sec_hr": 0.12, "sector_etf": "XLK" }
  },
  "_metadata": { "model_version": "ERM3-L3-v30", "universe_size": 2987 }
}

Developer-First

OpenAPI 3.0 spec, TypeScript/Python/cURL examples. Clean REST API with full type safety.

API Spec →Quickstart →PyPI (riskmodels-py) →SDK source →

Agentic Delegation

Pass your portfolio and a task — the agent returns factor exposures, drift alerts, and hedge suggestions. No query logic required.

Agent Guide →Get Key →

Institutional Grade

~3,000 tickers, 15+ years history, daily updates. Powered by ERM3 regression engine.

Methodology →Docs →

Stock Deep Dive

Institutional-Grade Risk Snapshots

One-page PDF combining L3 factor decomposition, residual alpha quality, and subsector peer comparison — generated for any stock in seconds.

NVDA Stock Deep Dive snapshot
Open NVDA Interactive Dashboard →
View NVDA DashboardDownload PDF

What Makes It Agentic

Traditional APIs give you data. You do the work. RiskModels does the work for you.

Traditional APIs

You own every step

  • →You construct the query payload
  • →You call the endpoint
  • →You parse the response
  • →You interpret hedge ratios and explained risk
  • →You compute drift vs benchmark
  • →You decide what hedge to use
  • →You implement the trade

You = the risk engine. API = a data pipe.

VS

RiskModels Agentic

You own the outcome

  • ✓You delegate the job in natural language or from your stack

    MCP /api/mcp/sse (tools/call) · OAuth2 agent keys · REST from automation

  • ✓ERM3 factor decomposition & hedge ratios across holdings

    POST /batch/analyze (full_metrics · hedge_ratios) · GET /metrics/{ticker} · GET /l3-decomposition

  • ✓Drift vs targets lives in your policy layer

    L1/L2/L3 snapshot via GET /metrics; L3 return history via GET /ticker-returns — you apply thresholds & alerts

  • ✓Factor exposure & explained risk surfaced in structured JSON

    L1/L2/L3 ER & HR in batch responses; lineage in _metadata

  • ✓Portfolio hedge notionals from the same factor model

    POST /api/batch/analyze (hedge_ratios) · POST /api/estimate before spend

  • ✓Machine-readable output for OMS, sheets, or copilots

    JSON · optional Parquet/CSV on batch & returns routes

You = the decision-maker. API = the risk engine.

Try the Agentic API
Hedge ratios first — patterns you orchestrate

The Foundation for Risk Agents

Hedge recommendations are our deepest, most turnkey surface. The rest is structured data you connect to guards, monitors, and autonomous workflows.

Hedge Recommendations

Core capability

CORE ENDPOINT

L1/L2/L3 hedge ratios, sector/subsector ETFs, and explained risk — ready to map to notionals without rebuilding the model.

View Pattern Docs →

Pre-Trade Risk

Implementation pattern

Provide the data layer for automated factor-impact guardrails — marginal hedge-ratio and explained-risk deltas (market, sector, subsector) your rules engine evaluates before execution.

View Pattern Docs →

Drift Monitoring

Implementation pattern

Calculate sigma-band drift against targets from L1/L2/L3 snapshot fields (`GET /metrics`) and L3 return history (`GET /ticker-returns`) — feed results into your monitoring stack or custom alert logic.

View Pattern Docs →

Rebalance Triggers

Agentic pattern

Detect when factor tilts breach policy using decomposition and exposure series — the API surfaces calculated trade directions implied by the structure; you own rebalance timing.

View Pattern Docs →

Plugs into your stack

REST, batch, Parquet/CSV exports, and MCP — you wire JSON into OMS, Slack, or agents; we do not sit in your execution path.

Enterprise Analytics. Not Enterprise Pricing.

The methodology is the same. The contract length is not.

Feature

MSCI Barra

$500K+/yr

Northfield

$200K+/yr

Recommended

RiskModels

$10K–$25K/yr

Multi-factor risk models
Equity factor coverage
16,495 tickers
Agentic task delegation
API-first access
Same-day provisioning
Open-source methodology
Real-time / intraday
Coming soon
Usage-based pricing
Availability
Negotiated only
Enterprise only
Get Started

RiskModels is built for teams that want institutional-grade risk analytics without the 6-month sales cycle.

Full pricing details →
$0 upfront · Baseline & Premium · $20 credits · Usage-based · $5 low-balance email

Try it free in 30 seconds

Use the public demo key below—no signup. Full universe access uses the same Baseline & Premium per-call pricing (card on file; no upfront charge).

1Your public demo API keyMAG7 access
rm_demo_mag7_dffc2f0239425513\n

Read-only · MAG7 tickers only · Rate limited

2Run this in your terminalreturns MAG7 ticker list
bash
curl "https://riskmodels.app/api/tickers?mag7=true" -H "Authorization: Bearer rm_demo_mag7_dffc2f0239425513\n"
3With a full key — live risk metrics$0.005 / call
GET /api/metrics/META → response
{
  "ticker": "META",
  "metrics": {
    "vol_23d":    0.392,
    "l3_mkt_hr":  1.284,   // short $1.28 SPY per $1 META
    "l3_sec_hr":  0.371,   // short $0.37 XLC per $1 META
    "l3_sub_hr":  0.198,   // short $0.20 subsector ETF
    "l3_mkt_er":  0.431,   // 43% variance from market
    "l3_sec_er":  0.089,   // 9% from sector
    "l3_sub_er":  0.043,   // 4% from subsector
    "l3_res_er":  0.437    // 44% idiosyncratic (alpha)
  }
}

Hedge ratios, decompositions, batch analysis, 15yr history.

Get full access →
RiskModels API — Precision Equity Risk Intelligence