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N1 distribution top triggers

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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.

N1 distribution top triggers

What it is

N1 distribution top triggers β€” registry key n1_distribution_triggers.

Human-readable list of fired conditions from the N1 scorer (e.g. [β€˜near_52w_high:-2.0%’, β€˜rsi_divergence:50’, β€˜momentum_rollover:-1.0%’, β€˜sma_breach:sma20’, β€˜confirmation:pass’]). Serialized as a JSON array string in the candidates table. Mirrors the A* triggers convention.

Data-pyramid tier

T1 β€” Signal. This metric sits at the signal 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: screener
Data source: computed

Derived metric β€” 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

List of triggered condition strings assembled in evaluate_distribution_top. Each string describes one fired predicate with its observed value. See app/screener/archetypes.py.

Where it surfaces

Health-score / alignment role

Data carrier β€” 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