The owner's modularity/monitoring ask: rate each pipeline component so what's earning (and
what's cutting) is visible instead of inferred. Everything derives from the existing event
stream — no pipeline changes.
- research/attribution.py: compute_attribution(days) -> per-day per-stage metrics:
DISCOVERY (symbols found + precision = fraction that ever go ready), SIGNALS (ready split
EARLY-vs-LATE against the confirmation window — the only +EV ones), GATES (risk_rule
histogram: what each gate cut), EXECUTION (submissions vs cancels = fill efficiency),
OUTCOMES (realized P&L, win rate, per-close detail). CLI: python -m research.attribution.
All queries filter the native timestamp column first (payload casts on the 676k-row events
table time out).
- api/main.py: GET /api/attribution?days=N.
- dashboard.html: "Component Attribution" panel, lazy-loads on open, per-day table with
gate-histogram hover.
- momentum_cli board header: live pulse line — today's early/late signal split + top gate
cuts (two tiny date-scoped queries).
Verified live: CLI scorecard over 7 days, endpoint returns real ratings (e.g. 2026-07-02:
discovery 45%, early 38%, fill 39%, win 50%, P&L +$29), board renders the pulse line.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
