Industry peer-β panel (/api/industry-panel)

GET /api/industry-panel returns the industry × level cross-section from the ds_erm3_industry zarr — Vasicek peer-β statistics at one trading day (latest by default). One row per (EODHD 4-digit industry code, cascade level) with:

Units: β values are dimensionless regression coefficients (not hedge ratios). n_companies is an integer; weights are unitless log-mcap sums.

Why this endpoint exists

For macro and sector-rotation work, the right question is rarely "what is one stock's β?" It is "what is the typical β for this slice of the market, and how dispersed is it inside the slice?" That's the Vasicek shrinkage prior turned inside out: instead of using the peer mean to adjust a single stock's β (which is what the engine does internally), we expose the peer mean and variance themselves as a data product.

Use cases:

• Sector rotation — rank subsector industries by month-over-month change in beta_mean at level='subsector' to surface industries quietly rotating beta.
• Dispersion screens — high beta_variance at level='subsector' flags industries where the label hides real structural variation (idiosyncratic alpha hiding in plain sight).
• Macro hedge sizing — for an allocator with sector exposure, the cap-weighted industry beta_mean is the right notional to assume per dollar of sector exposure when sizing macro overlays.
• Quality control — n_companies per industry per teo is your sample-size guard. Industries with n_companies < 5 should be treated as too thin to interpret.

Request

GET /api/industry-panel accepts:

Response shape

{
  "teo": "2026-05-26",
  "market_factor_etf": "SPY",
  "industries": [
    {
      "fs_industry_code": 3210,
      "level": "subsector",
      "beta_mean": 1.243,
      "beta_variance": 0.187,
      "n_companies": 32,
      "total_log_mcap_weight": 712.4
    },
    ...
  ],
  "_metadata": { "data_source": "zarr", ... }
}

Example

# Top 5 most-dispersed industries at subsector level, today
curl -sS "https://riskmodels.app/api/industry-panel?level=subsector&min_peers=20" \
  -H "Authorization: Bearer $RISKMODELS_API_KEY" \
  | jq '.industries | sort_by(.beta_variance) | reverse | .[:5]'

from riskmodels import RiskModelsClient
client = RiskModelsClient.from_env()

df = client.get_industry_panel(level="subsector", min_peers=20, as_dataframe=True)
df.nlargest(5, "beta_variance")[["fs_industry_code", "beta_mean", "beta_variance", "n_companies"]]

Pricing & cadence

0.02/request (billing_code: industry_panel_v1). Updated daily by the ERM3 pipeline; latest-available teo lags the close by the same window as ds_erm3_betas_adjusted. Use _metadata.data_source + _metadata.range on the JSON envelope to confirm what you got — see Response metadata.

Where it sits in the stack

The industry panel is derived from the per-(stock, teo) Vasicek shrinkage that lives inside ds_erm3_betas_adjusted. The pipeline aggregates the per-stock β to the industry × level grid as a side product; this endpoint serves that aggregate. It does not expose per-stock β — for that use GET /metrics/{ticker} or GET /api/lstar (per-ticker hedge ratios) and POST /decompose (the four-bet breakdown).

Related

• ERM3 Engine Design — the broader cascade methodology.
• Methodology — Huber–Vasicek estimation on riskmodels.org.
• POST /api/rankings/screen — when you want a stock-level rank cross-section instead of an industry-level one.
• OpenAPI IndustryPanelResponse — full schema.