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KB/signal

Normalized archetype score

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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 — do not edit this file directly.

Normalized archetype score

What it is

Normalized archetype score — registry key screener_score_norm.

Per-(ticker, archetype) percentile or z-score normalization of the raw archetype score within the archetype’s current-batch or trailing distribution. Range [0, 1]. 0.5 sentinel when only one candidate exists for an archetype (single-sample edge). Computed by rank.py normalize_within_archetype().

Data-pyramid tier

T1 — Signal. This metric sits at the signal layer of the platform’s six-tier data pyramid (T0 raw inputs → T5 actionable read). The tier reflects how far the value is from a raw measurement — 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 — read it as context / a data carrier, not a validated edge. Stamps are added only when a gated research verdict lands.

Source

Source module: screener
Data source: computed

Derived metric — produced inside the platform (app/sources/screener.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

Percentile mode (default): rank(score_i) / (N-1) where rank is 0-indexed ascending position. Z-score mode: (score - mean) / std clamped to [-3,3] then mapped to [0,1] via (z+3)/6. Single-sample sentinel = 0.5. See app/screener/rank.py.

Where it surfaces

Health-score / alignment role

Data carrier — no implication, no health-score contribution.

Persisted for downstream consumers (sparklines, base-rate matcher, calibration substrate) but does not classify into BULLISH / NEUTRAL / BEARISH and does not contribute to the 0-100 health score.

Release cadence