Focus Engine: how we map 14 macro events to geographic footprints

Translating a macro event into actionable numbers is a problem of geometry: you have to decide who it touches, how much it touches them, and in which direction. The Focus Engine — the engine that powers both the /focus module and Risk Map — solves the problem in three layers: geographic footprint, composite scoring and regime classification.

Layer 1 — ISO-3 footprint

Each event has a static footprint: a set of ISO-3 country codes with a 0–1 weight representing structural exposure. The footprint for "China real estate" is {CHN: 1.0, HKG: 0.6, AUS: 0.4, JPN: 0.2}: China is the source, Hong Kong is the financial proxy, Australia exports the mining that supports construction, and Japan has regional exposure.

The footprint is curated manually, not inferred. Every entry is documented with its rationale: imports/exports, energy dependence, banking exposure, geographic proximity. No historical correlations masquerading as causation.

Layer 2 — Composite scoring

The 0–100 score of an event is the linear combination of three indicators, normalized on a rolling 5Y z-score:

1. 1.A relevant World Bank macro indicator (e.g. YoY growth, headline inflation, debt/GDP, current account) over the set of footprint countries, weighted.
2. 2.Performance of the proxy ETF associated with the event (e.g. KWEB for China internet, TLT for US rate shocks), z-scored over 1Y.
3. 3.Intensity trend — 1M change in the raw score, providing the "momentum" the panel displays as a delta.

Weights are 0.4 / 0.4 / 0.2: macro fundamentals and market proxy are equally weighted, momentum is the spice. The renormalized sum yields a 0–100 score where 50 is the historical median and the extremes (≥75 or ≤25) trigger the "extreme regime" flag.

Layer 3 — Regime classification

Each event is labelled with a market regime that the panel displays as a colored tag. The regime is not the score: it is its operational direction.

• ·Risk-off: the score rises and the safe-haven proxy ETF (TLT, GLD, UUP) outperforms. Examples: rate shocks, geopolitics, hawkish central banks.
• ·Risk-on: the score rises and broad equity (SPY, QQQ) outperforms. Examples: AI capex, China recovery, accommodative cuts.
• ·Easing: the score declines after reaching a peak — the market is "metabolizing" the event.
• ·Neutral: score in the median range, no dominant direction.

Integration with the portfolio

PortfolioFocusTab cross-references holdings with the event list and computes, for each one, the portfolio's net exposure: the sum of the weights of holdings whose country / sector / sub-asset falls within the event's footprint. The result is a matrix that says "your 60/40 has 38% exposure to the US Rates event, 11% to China real estate".

It is not a forecast, it is an audit. When an event enters extreme regime, you immediately know how much of your book is involved and where the most exposed names are — drill-down by event, sorted by market-cap × exposure.