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A6 vol mispricing score (historical half)

What it is

A6 vol mispricing score (historical half) — registry key a6_vol_score.

Partial dislocation score for A6 vol mispricing: hist_earnings_move = recency-weighted mean of abs(gap_pct) over last N earnings events. The full vol_edge = hist_earnings_move / implied_move requires Phase 3 options data. A score > 0 means the historical move was computable.

Data-pyramid tier

T3 — Lens. This metric sits at the lens 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

hist_earnings_move = recency-weighted mean of abs(gap_pct) over the last N screener_earnings rows. Weight = 0.7^i (i=0 most recent), normalised. vol_edge = hist_earnings_move / implied_move — implied_move pending Phase 3.

Where it surfaces

• API field: screener.candidates.a6.score on GET /api/v1/signals/latest

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

• Publishes: daily