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Next filing · Form N-PORT · Q1 2026 · Due today
Next filing · Form N-PORT · Q1 2026 · Due todayFactor Research · Part 2 published: risk structure in 13F filings across five allocator stylesAPI Update · AOM portfolio chains — single snapshot call for multi-step analyze flowsAPI Update · POST /api/snapshot — canonical JSON portfolio snapshotPart 1 · One Position, Four BetsPart 2 · Risk Structure in 13F FilingsNext filing · Form N-PORT · Q1 2026 · Due todayFactor Research · Part 2 published: risk structure in 13F filings across five allocator stylesAPI Update · AOM portfolio chains — single snapshot call for multi-step analyze flowsAPI Update · POST /api/snapshot — canonical JSON portfolio snapshotPart 1 · One Position, Four BetsPart 2 · Risk Structure in 13F Filings
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Independent reviews

Outside write-ups of the RiskModels ecosystem — the public methodology on riskmodels.org and the agent-native API on riskmodels.app. Reproduced with attribution; we add new ones as they arrive.

Independent reviewChatGPT · OpenAI language model · May 2026

A quantitative truth layer for AI-native finance

RiskModels has a credible claim as the quantitative truth layer behind financial agents.

RiskModels.app is a rare financial API product that feels built for the next interface layer, not the last one. Its strongest idea is simple but commercially important: an LLM should not be inventing portfolio-risk commentary from text memory; it should be calling a structured, time-stamped risk model. The API exposes equity risk as a four-layer decomposition — market, sector, subsector, and residual stock-specific risk — with explained-risk contributions, ETF hedge ratios, and agent-ready outputs that can be consumed directly by applications, notebooks, or MCP-connected assistants.

The product is most compelling where it turns quantitative infrastructure into usable workflow. Developers can call canonical endpoints such as /api/snapshot, /api/decompose, metrics, portfolio analysis, rankings, and PDF/PNG snapshot routes, while agents can discover and use the system through OpenAPI, MCP, SDKs, and CLI tooling. That matters because the product is not merely returning raw factor data; it is packaging risk decomposition, hedge ratios, portfolio snapshots, and narrative-ready context into objects that can drive portfolio assistants, research tools, advisor dashboards, or internal risk copilots.

Bottom line

The main product judgment is that RiskModels.app is not trying to be another chart site or generic market-data wrapper. It is closer to a risk-model infrastructure layer for AI-native finance software: opinionated, structured, and built around executable decomposition rather than descriptive commentary. The current opportunity is to keep tightening the public surface around one canonical workflow — submit a ticker or portfolio, receive decomposed risk, hedge logic, and interpretable context — because that is the wedge. If the developer experience stays clean and the data coverage and reliability continue to hold up, RiskModels.app has a credible claim as the quantitative truth layer behind financial agents.

Independent reviewGrok · xAI language model · May 2026

The most transparent equity risk decomposition platform in 2026

In a world full of risk models that overpromise and under-explain, RiskModels is refreshingly honest, technically excellent, and immediately actionable.

RiskModels (riskmodels.app + riskmodels.org) is a sharply focused, developer-first equity risk intelligence platform that does one thing exceptionally well: it decomposes any US equity position — stock, fund, or portfolio — into four clean, tradable layers (market, sector, subsector, and residual) and instantly delivers dollar-neutral ETF hedge ratios in a single API call.

Built on the proprietary ERM3 (Equity Risk Model v3), RiskModels turns the vague “factor zoo” critique into something practical and reproducible. Instead of abstract factors, it shows you exactly what benchmark your position is actually trading against and how much of its risk is hedgeable right now with liquid ETFs. The residual slice — often dismissed as noise — is treated as a legitimate, tradable bet you can monitor and size deliberately.

What stands out

  • Extreme transparency

    The full methodology — hierarchical orthogonalization, the L-star regression rule, marginal-ER cascade logic — lives publicly on riskmodels.org with full derivations, edge-case handling, and reproducible examples. No black-box claims.

  • Speed & usability

    Sub-120 ms responses, read-only keys, and server-side processing. A Python SDK (pip install riskmodels-py), an npm CLI, REST endpoints, and MCP/agent manifests make it instantly usable by both humans and AI agents (Claude, Cursor, and others).

  • Pricing that respects builders

    Pure pay-per-call with no seat licenses or enterprise lock-in. Starts with $20 in free credits — enough to test real portfolios.

  • Credibility layer

    riskmodels.org functions as the independent research hub: deep 13F analyses (the “One Position, Four Bets” series), single-stock and fund snapshots (NVDA, AGTHX), and institutional-grade PNG reports. The .app site stays lean and product-focused while linking back to the research.

  • Real-world workflows

    Built for portfolio managers running live hedges, allocators doing 13F diligence, and quants or AI agents monitoring drift and building attribution pipelines.

Who it’s for

Portfolio managers, hedge fund analysts, allocators reviewing 13F filings, and any developer or AI agent that needs production-grade equity risk data without the usual vendor friction. If you’re tired of “beta proxies that are directionally true but economically incomplete,” this is the antidote.

Bottom line

In a world full of risk models that overpromise and under-explain, RiskModels is refreshingly honest, technically excellent, and immediately actionable. The combination of public methodology on .org and a lightning-fast, agent-native API on .app creates one of the cleanest risk intelligence offerings available today. Highly recommended for any serious equity investor or builder who wants numbers-first answers instead of marketing slides.

Reviews are reproduced with attribution to their source and lightly copy-edited for typos only. They reflect the author’s independent opinion, not investment advice.

See the methodology →Read the research →

Subscribe to the Quarterly Attribution Review.

Research notes on risk decomposition, fund attribution, 13F filings, and benchmark structure — a few times a quarter.

By registering, you agree to receive technical factor research and API deployment logs. RM-Registry-2026. Privacy Policy.

RiskModels.org

A research surface for hierarchical orthogonal decomposition, variance attribution, and allocator-grade risk measurement. Operational APIs and developer workflows live at riskmodels.app.

Subscribe to the Quarterly Attribution Review.

Research notes on risk decomposition, fund attribution, 13F filings, and benchmark structure — a few times a quarter.

By registering, you agree to receive technical factor research and API deployment logs. RM-Registry-2026. Privacy Policy.

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