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Stagflation Risk

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Auto-generated. This article is rebuilt from app/signals/config/signal_definitions.json by scripts/build_signals_kb.py. Edit the registry entry and re-run the script β€” do not edit this file directly.

Stagflation Risk

What it is

Stagflation Risk β€” registry key stagflation.

Classifier metric. See the bands table below for the band-by-band reading.

Data-pyramid tier

T3 β€” Lens. This metric sits at the lens layer of the platform’s six-tier data pyramid (T0 raw inputs β†’ T5 actionable read). The tier reflects how far the value is from a raw measurement β€” not how strongly it is validated. Abstraction and validation are separate axes: a higher tier is not a claim of stronger evidence.

Horizon & validation

No validated skill horizon is on file for this metric β€” read it as context / a data carrier, not a validated edge. Stamps are added only when a gated research verdict lands.

Source

Source module: inflation
Data source: computed

Derived metric β€” produced inside the platform (app/sources/inflation.py or equivalent) rather than fetched as a raw upstream value. See the How it's computed section below for the formula.

How it’s computed

Composite 0-100 stagflation risk: weighted sum of (breakeven_5y, 40%; breakeven_10y, 20%; breakeven_5y5y, 20%; breakeven_inversion = be_5y βˆ’ be_10y, 20%) β€” each component min-max-scaled to its policy-relevant band. See app/signals/stagflation.py:compute_stagflation_breakdown.

Where it surfaces

Bands / thresholds

Classifier direction: lower_is_better.

Range Label Dot Implication Points
β€” Low risk favorable BULLISH 8
β€” Moderate neutral NEUTRAL 5
β€” Elevated cautionary BEARISH 2
β€” Severe adverse BEARISH 0

Health-score / alignment role

Release cadence

Computed from breakevens + macro fundamentals; refreshes with β€˜full’ profile cycles.

See also