RiskModels API

Your portfolio already has a benchmark. You just haven’t seen it.

Decompose every position into market, sector, subsector, and residual (stock-specific) risk — with model-derived ETF hedge ratios from one API call.

Numbers-first answers from live ERM3 data · hedge ratios in dollars of ETF per $1 of stock

Get API Key View Docs See the Worked Example

Add to Claude or Cursor · no terminal, no API key

https://riskmodels.app/api/mcp/sse

Settings → Connectors → Add custom connector → paste → Connect. Sign in once and approve; the tools load automatically. Setup guide →

After Connect, paste this (pulls live data)

Call riskmodels_compare with tickers ["AAPL","NVDA"]. Then call riskmodels_decompose on AAPL and on NVDA. Quote residual explained-risk and the L3 hedge ratios from the tool results. Do not answer from training data. Do not call riskmodels_list_endpoints unless I ask which REST routes exist.

Read-only · Keys server-sideERM3 (Equity Risk Model v3)Low-latencyAgent-callable

A risk map, not a factor zoo.

See that benchmark in four layers.

A factor zoo adds labels. A risk map shows relationships: how a position’s modeled risk divides, which exposures connect to ETF references, and what remains stock-specific.

RiskModels organizes every position across four layers. The three systematic layers include model-derived ETF hedge ratios. The same structure carries from measurement into research, testing, and trading workflows.

The map makes the terrain legible. You decide what to keep, what to hedge, and what to test.

Benchmark → bet

Risk hierarchy

01
Market
Broad benchmark risk
02
Sector
Incremental sector exposure
03
Subsector
Granular industry tilt
04
Residual
Stock-specific risk left visible

Four layers. ETF hedge ratios. One API call.

Worked example — MSFT

Per $1 long MSFT, ERM3 returns three hedge ratios you can trade.

L1 · market

short $0.03 SPY

+20% explained risk

L2 · tech sector

short $0.05 XLK

+4% explained risk

L3 · systems software

short $0.62 IGV

+18% explained risk · dominant

What’s left — the bet

MSFT’s stock-specific return. Not hedgeable with ETFs.

57% residual

Example output from the ERM3 API as of 2026-05-26 · single call, no extrapolation · today’s MSFT hedge

Run on your portfolio See the methodology

Built for three workflows

Pick the path that matches your job.

For developers & agents

Call risk decomposition from your app, notebook, or agent workflow. JSON in, hedge ratios out.

View API Docs

For portfolio managers

See what each position is really betting on — and what ETF trades hedge it. NVDA single-stock and AGTHX fund-fit snapshots, rendered from one API call.

See sample snapshots

For RIAs, family offices & allocators

Holdings-level risk without an enterprise platform. Separate benchmark exposure from stock selection across funds, managers, and disclosed 13F books.

How we compare to Barra & Axioma

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: python3 -m pip install "riskmodels-py>=0.3.4" on PyPI ([xarray] optional for dataset helpers; [viz] for Kaleido static PNG). CLI: npm install -g riskmodels@latest 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 }

}

The research behind the model

Published method. Open evidence.

The factor definitions, hierarchical orthogonalization, L-star, and hedge-ratio construction are published so the four-layer map can be examined rather than taken on faith.

Published methodology. Open research examples. Reproducible decomposition logic. Real citations (Frisch-Waugh-Lovell, Cremers-Petajisto, Harvey-Liu-Zhu, Grinold-Kahn).

Part 1 · One Position, Four Bets

AAPL vs NVDA, XOM vs KMI, MAG7 DNA.

Read on riskmodels.org

Part 2 · Risk Structure in 13F Filings

Buffett, Ackman, Lone Pine, Tiger Global, Baupost.

Read on riskmodels.org

Methodology · ERM3 Engine Design

Factor definitions, hierarchical orthogonalization, L-star, and hedge-ratio construction.

Read the methodology

See full institutional snapshots — what ERM3 renders end-to-end

Evaluating alternatives?

Evaluating against Barra or Axioma?

See where RiskModels fits — API-native decomposition with tradable ETF hedge ratios, for teams that need holdings-level risk without an enterprise platform.

Compare

Pricing, API-first.

Designed for experimentation and agent workflows.

Pay per call
No seats
No enterprise lock-in
Start free — $20 in credits

See full pricing

Transparent methodology.

Additive decomposition

Four layers, no hidden factors. `market_er + sector_er + subsector_er + residual_er ≈ 1`.

Same model drives return + risk

Hedge ratios are the dollar weights from the same ERM3 regression that produces explained risk.

Fully documented

OpenAPI spec, semantic field reference, and methodology notes are public.

Methodology API reference Python SDK

Install once. Ask what you own.

Put RiskModels inside Claude, Cursor, Codex, or VS Code and turn portfolios into chart-ready risk explanations.

Get started Get API key

RiskModels API

Your portfolio already has a benchmark. You just haven’t seen it.

Decompose every position into market, sector, subsector, and residual (stock-specific) risk — with model-derived ETF hedge ratios from one API call.

Numbers-first answers from live ERM3 data · hedge ratios in dollars of ETF per $1 of stock

Get API Key View Docs See the Worked Example

Add to Claude or Cursor · no terminal, no API key

https://riskmodels.app/api/mcp/sse

Settings → Connectors → Add custom connector → paste → Connect. Sign in once and approve; the tools load automatically. Setup guide →

After Connect, paste this (pulls live data)

Read-only · Keys server-sideERM3 (Equity Risk Model v3)Low-latencyAgent-callable

A risk map, not a factor zoo.

See that benchmark in four layers.

A factor zoo adds labels. A risk map shows relationships: how a position’s modeled risk divides, which exposures connect to ETF references, and what remains stock-specific.

The map makes the terrain legible. You decide what to keep, what to hedge, and what to test.

Benchmark → bet

Risk hierarchy

01
Market
Broad benchmark risk
02
Sector
Incremental sector exposure
03
Subsector
Granular industry tilt
04
Residual
Stock-specific risk left visible

Four layers. ETF hedge ratios. One API call.

Worked example — MSFT

Per $1 long MSFT, ERM3 returns three hedge ratios you can trade.

L1 · market

short $0.03 SPY

+20% explained risk

L2 · tech sector

short $0.05 XLK

+4% explained risk

L3 · systems software

short $0.62 IGV

+18% explained risk · dominant

What’s left — the bet

MSFT’s stock-specific return. Not hedgeable with ETFs.

57% residual

Example output from the ERM3 API as of 2026-05-26 · single call, no extrapolation · today’s MSFT hedge

Run on your portfolio See the methodology

Built for three workflows

Pick the path that matches your job.

For developers & agents

Call risk decomposition from your app, notebook, or agent workflow. JSON in, hedge ratios out.

View API Docs

For portfolio managers

See what each position is really betting on — and what ETF trades hedge it. NVDA single-stock and AGTHX fund-fit snapshots, rendered from one API call.

See sample snapshots

For RIAs, family offices & allocators

Holdings-level risk without an enterprise platform. Separate benchmark exposure from stock selection across funds, managers, and disclosed 13F books.

How we compare to Barra & Axioma

Live Demo

See the API in Action

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 }

}

The research behind the model

Published method. Open evidence.

The factor definitions, hierarchical orthogonalization, L-star, and hedge-ratio construction are published so the four-layer map can be examined rather than taken on faith.

Published methodology. Open research examples. Reproducible decomposition logic. Real citations (Frisch-Waugh-Lovell, Cremers-Petajisto, Harvey-Liu-Zhu, Grinold-Kahn).

Part 1 · One Position, Four Bets

AAPL vs NVDA, XOM vs KMI, MAG7 DNA.

Read on riskmodels.org

Part 2 · Risk Structure in 13F Filings

Buffett, Ackman, Lone Pine, Tiger Global, Baupost.

Read on riskmodels.org

Methodology · ERM3 Engine Design

Factor definitions, hierarchical orthogonalization, L-star, and hedge-ratio construction.

Read the methodology

See full institutional snapshots — what ERM3 renders end-to-end

Evaluating alternatives?

Evaluating against Barra or Axioma?

See where RiskModels fits — API-native decomposition with tradable ETF hedge ratios, for teams that need holdings-level risk without an enterprise platform.

Compare

Pricing, API-first.

Designed for experimentation and agent workflows.

Pay per call
No seats
No enterprise lock-in
Start free — $20 in credits

See full pricing

Transparent methodology.

Additive decomposition

Four layers, no hidden factors. `market_er + sector_er + subsector_er + residual_er ≈ 1`.

Same model drives return + risk

Hedge ratios are the dollar weights from the same ERM3 regression that produces explained risk.

Fully documented

OpenAPI spec, semantic field reference, and methodology notes are public.

Methodology API reference Python SDK

Install once. Ask what you own.

Put RiskModels inside Claude, Cursor, Codex, or VS Code and turn portfolios into chart-ready risk explanations.

Get started Get API key

Your portfolio already has a benchmark. You just haven’t seen it.

See that benchmark in four layers.

Market

Sector

Subsector

Residual

Per $1 long MSFT, ERM3 returns three hedge ratios you can trade.

Pick the path that matches your job.

See the API in Action

Published method. Open evidence.

Evaluating against Barra or Axioma?

Pricing, API-first.

Transparent methodology.

Additive decomposition

Same model drives return + risk

Fully documented

Install once. Ask what you own.

Your portfolio already has a benchmark. You just haven’t seen it.

See that benchmark in four layers.

Market

Sector

Subsector

Residual

Per $1 long MSFT, ERM3 returns three hedge ratios you can trade.

Pick the path that matches your job.

See the API in Action

Published method. Open evidence.

Evaluating against Barra or Axioma?

Pricing, API-first.

Transparent methodology.

Additive decomposition

Same model drives return + risk

Fully documented

Install once. Ask what you own.

Your portfolio already has a benchmark. You just haven’t seen it.

See that benchmark in four layers.

Market

Sector

Subsector

Residual

Per $1 long MSFT, ERM3 returns three hedge ratios you can trade.

Pick the path that matches your job.

See the API in Action

Published method. Open evidence.

Evaluating against Barra or Axioma?

Pricing, API-first.

Transparent methodology.

Additive decomposition

Same model drives return + risk

Fully documented

Install once. Ask what you own.

Your portfolio already has a benchmark. You just haven’t seen it.

See that benchmark in four layers.

Market

Sector

Subsector

Residual

Per $1 long MSFT, ERM3 returns three hedge ratios you can trade.

Pick the path that matches your job.

See the API in Action

Published method. Open evidence.

Evaluating against Barra or Axioma?

Pricing, API-first.

Transparent methodology.

Additive decomposition

Same model drives return + risk

Fully documented

Install once. Ask what you own.