A concrete blueprint for turning the price history Delvantic already collects into per-stock macro betas — coefficients that say "for a +1 standard-deviation move in rates / the dollar / oil / the market, this name has historically moved ±X%." The general theory (what a macro beta is, how to estimate it, why it is unstable) lives in the sibling nodes of Macro Factor Sensitivity & Elasticity; this node is the engineering and data-discipline layer for computing those sensitivities from the Delvantic universe and surfacing them as a read-only overlay, never as a write-back into the e2e scoring pipeline. The core tension throughout: macro betas are genuinely useful but statistically fragile — small windows are noisy, large windows are stale, and the macro factors themselves are correlated — so most of the work is in not fooling yourself, not in the regression.

What data already exists vs. what's missing

Delvantic's regime engine already ingests the raw market series it needs into stocks_daily_index_snapshot — ^GSPC (S&P 500), ^VIX, ^RUT (Russell 2000) — and the universe carries per-ticker daily closes. That covers the equity-market beta and a crude size/breadth factor (RUT−GSPC) for free. A full macro-beta engine needs additional factor proxies that are not yet collected:

• Rates: a Treasury-yield series (e.g. 10y ^TNX) or the IEF/TLT bond ETFs as a tradable rate proxy.
• Dollar: DXY or the UUP ETF.
• Oil / commodities: USO/WTI, optionally a broad DBC.
• Credit / risk-appetite: a high-yield spread proxy (HYG vs IEF).

These are a handful of additional daily symbols on the same collection path the index snapshot already uses — the cost is plumbing, not new infrastructure.

How it's calculated (the regression)

A macro beta is the slope from regressing the stock's returns on contemporaneous factor returns (multivariate OLS — the multi-factor generalization of CAPM, in the spirit of Chen-Roll-Ross's Economic Forces and the Stock Market, 1986):

r_i,t = α + β_mkt·r_mkt,t + β_rate·Δrate_t + β_usd·r_usd,t + β_oil·r_oil,t + ε_t

Key build decisions, each sourced to standard practice:

• Use returns, not levels. Prices and yields are non-stationary; regressing levels produces spurious betas. Use log returns for prices and changes for yields.
• Standardize the factors (z-score each factor's return series). Then β reads as "% stock move per 1-SD factor move," which is comparable across factors and across stocks — the practical form referenced in the applying sensitivities sibling.
• Window length is the central tradeoff. Tidy Finance's standard convention is a 60-month window with ≥48 observations for monthly data, or a ~5-year / ≥1,000-day window for daily CAPM betas. The BIS Dollar Beta and Stock Returns paper uses a rolling one-year window for a single macro factor. Shorter windows track regime changes but are noisier; longer windows are smoother but stale. A defensible Delvantic default: 252 trading days (1y), min ~150 obs, with an optional 504-day "stable" beta shown alongside.
• Renormalize on thin history — mirror the existing MarketRegime discipline: if a ticker or factor lacks enough overlapping observations, mark that beta inactive rather than reporting a number computed from a handful of points.

How it's used in practice

The output is a small table per ticker — {factor → beta, t-stat, R², window, n_obs} — written to a new read-only table (e.g. stocks_macro_betas), recomputed nightly and never frozen, exactly like stocks_market_regime. Downstream uses:

• Scenario / what-if overlay: combine the beta vector with a macro scenario ("rates +50bp, dollar +2%") to estimate first-order price impact per name — the canonical application from the sibling node.
• Risk-clustering: rank the universe by β_rate or β_oil to find hidden concentration (e.g. a "value" basket that is secretly a short-duration rates bet).
• Augustus input (timing/sizing only): per the macro-overlay design, a stock's macro betas feed the General Sentiment lens and the Play's sizing/timing — they must not alter the Quality or Value lens scores. A high-β_rate name into a hawkish tape is a reason to size down or wait, not to re-rate the business.

Adoption, debate & evidence

Multi-factor macro regression is the textbook backbone of institutional risk systems (BARRA, Axioma, BIRR) — see the macro factor models sibling. What is contested is reliability at the single-stock level, not the method: the literature is consistent that betas are time-varying and noisy (NeuralBeta, the rolling-WLS literature, and the broad-cross-section beta studies all exist precisely because OLS rolling betas forecast poorly). Two empirically grounded cautions:

• Macro factors are mutually correlated (rates, the dollar, oil, and equities co-move), so multivariate coefficients suffer multicollinearity — individual β's are unstable even when the model's overall fit is fine. Inspecting the factor correlation matrix, and optionally orthogonalizing factors, is part of doing this honestly.
• R² is typically low for single names against macro factors; idiosyncratic risk dominates. A macro beta is a tilt, not an explanation.

There is no credible claim that these betas predict returns; they describe historical sensitivity. Treat any "this stock will move X" output as a conditional estimate with wide error bars.

Strengths & limitations

• Strengths: cheap (reuses the existing collection + regime plumbing), transparent (a regression you can audit, not a black box), and genuinely fills Cairn's self-named macro_blindspot weakness with a quantified, per-name read.
• Limitations: betas drift across regimes (the why macro betas are unstable sibling is the required companion read); short windows overfit recent shocks; long windows miss them. Outliers (a single earnings gap) can distort a 1-year daily beta materially.
• #1 misuse: treating a point estimate as precise. Always carry the t-stat / standard error and the window length; a β_oil = 0.8 with a t-stat of 0.4 is noise. The second-worst misuse is letting a transient macro beta leak into the immutable e2e scoring — by design it stays an additive, human-gated overlay.

Sources

• Tidy Finance — Beta Estimation with Python / R (rolling-window conventions: 60-month/48-obs monthly, 5-year/≥1,000-day daily): https://www.tidy-finance.org/python/beta-estimation.html
• BIS Working Paper No. 1000 — Dollar beta and stock returns (one-year rolling macro-beta window): https://www.bis.org/publ/work1000.pdf
• Chen, N.-F., Roll, R. & Ross, S. A. (1986) — Economic Forces and the Stock Market, Journal of Business 59(3), 383–403 (the foundational multi-factor macro regression framework; no open-access PDF — see the JSTOR record or the publisher): https://www.journals.uchicago.edu/doi/10.1086/296344
• NeuralBeta: Estimating Beta using Deep Learning (documents OLS rolling-beta noise / time-variation, motivates window/weight tradeoffs): https://arxiv.org/html/2408.01387v2
• QuantInsti — Linear Regression in Algorithmic Trading (multivariate OLS for factor/hedge-ratio estimation): https://blog.quantinsti.com/linear-regression/
• Delvantic internal: classes/stocks/services/marketregime.class.php, migration 222 (stocks_market_regime), stocks_daily_index_snapshot — the existing data + engine pattern this node extends.