Macro Risk Overlay
A regime classifier trained on rates, vol, credit-spread, and macro-data series — paired with a recommended position-sizing and hedging overlay that the portfolio construction team consumes alongside the existing risk framework.
- Engagement
- 12–16 week engagement · quarterly model review
- Built for
- CIOs · CROs · Portfolio construction leads
Position sizing and hedging are usually intuition-anchored at the desk level — fine in steady regimes, dangerous when the regime breaks. Funds want a system that classifies the regime, scales exposures accordingly, and surfaces the moments when the regime is shifting before the P&L tells them.
What this is
A portfolio-level overlay engagement. Two paired deliverables:
- Macro regime classifier. A model that classifies the current macro environment into a small number of regimes — inflationary vs. disinflationary, risk-on vs. risk-off, vol-expanding vs. vol-contracting, or whatever taxonomy the fund's strategy mix calls for. Probability-weighted, not hard-classified — the overlay needs uncertainty to size positions sanely.
- Position-sizing and hedging overlay. Translates regime probabilities into strategy-level exposure scaling, hedge sizing recommendations, and transition alerts. Consumed by portfolio construction alongside the existing risk framework — not replacing it, layered on it.
How it's built
Backbone: a regularized classifier (Bayesian or LightGBM-with-monotonic-constraints, depending on interpretability requirements) trained on rates, vol, credit-spread, and macro-data series. Walk-forward validation with explicit attention to the small-sample problem. The overlay layer translates classifier output into recommendation through a fund-specific rule set scoped during the engagement.
Integration: the overlay surfaces through your existing risk dashboards. No new system for the desk to check.
What you get
- The regime classifier model — trained, documented, version-controlled.
- The overlay rule set — sizing and hedging recommendations tied to regime confidence.
- Transition alerts wired into your existing risk infra.
- Quarterly model review — the regime taxonomy retrained on rolling data, the rule set re-examined against the prior quarter's P&L.
- Documentation written for the CIO and CRO, not the engineers.
Engagement is shape, not list.
Length and price are functions of the data and the destination. The shape below is the typical engagement.
- Length
- 12–16 week engagement · quarterly model review
- Lead
- Bogdan
- Cadence
- Async, weekly
- Bar
- Production
Scoped during the discovery call against the actual data and the operation it integrates with.
Principal engineer. Architecture and most code ships through one keyboard.
Written updates between, calls when the decision needs the room.
Async correctness, capacity under burst, observability at every boundary.
Products this composes with.
Same suite, or vertical-specialized versions in another.
- Same suite · Hedge Fund Suite
Alternative Data Signal Engine
A production pipeline that ingests one or more unconventional datasets, normalizes against a fund-internal schema, and serves processed factor scores, back-tested signals, and event-time alerts to the research stack.
- Same suite · Hedge Fund Suite
Prediction-Market Alpha Layer
A clean feed of prediction-market probabilities mapped to your existing macro and event-driven framework — Fed move probabilities, geopolitical risk markers, election-implied probabilities, joined to the equity sector exposures and macro positions they should influence.
What buyers ask about this one.
How is this different from Bayesline / ALEX?
Bayesline ships a packaged regime + risk-overlay product. We build a custom one — trained on your data, calibrated to your strategy mix, surfaced through your existing risk infra. If the packaged version fits your fund, buy it. If your strategy mix doesn't map cleanly onto a packaged product (multi-strat with idiosyncratic books, prop-trading-flavored macro, or anything where the canonical regime taxonomy doesn't apply), this is the engagement for the bespoke version.
What macro variables drive the regime classification?
Default features: short rate, long rate, term spread, vol (VIX + sector vol), credit spreads (IG + HY), USD index, gold/copper ratio, breakeven inflation. Hyperparameters and feature selection scoped to the strategy. The classifier is regularized — interpretability matters when the CIO has to explain why exposures got cut.
What does the overlay actually recommend?
Three things. (1) Strategy-level exposure scaling — how much capital each strategy gets in the current regime. (2) Hedge recommendations — vol, credit, FX overlay sizing tied to regime confidence. (3) Transition alerts — surfaced when the regime classifier crosses confidence thresholds, with the underlying feature deltas driving the transition.
How do you avoid overfitting given how few regime breaks there are in history?
Walk-forward validation with explicit attention to the small-sample problem. Multiple regime taxonomies stress-tested rather than picking the one that backtested best. CIO-side acceptance criteria explicitly include 'does this generalize past the training window' rather than just hit-rate on holdout. Where the data is thin, we say so out loud.
Pricing?
Scoped against the data, the strategy mix, and the integration depth. Discovery call covers all three.
If the deliverable matches the gap, the next step is one call.
We'll scope length and price against your data and the operation it integrates with. No retainer, no fishing.
Bogdan and team · async-first · OP—2026