audit-pick-flow

name	audit-pick-flow
description	Use when tracing how a pick on findtorontoevents.ca/audit got there — what source emitted it, which gates it passed or was rejected by, why it is or is not in Smart Picks / High Conviction / Money Ready, or why a class shows the win-rate it does. Also for "which DB tables matter", per-asset-class pick-funnel case studies, and onboarding someone to the audit pipeline.

Audit Pick Flow

Overview

Every pick on /audit travels a fixed path: source emitter → JSON → at_raw_picks → gate stack → at_consensus_picks → outcome resolver → dashboard category. This skill traces any single pick through that path, runs per-asset-class funnel case studies, and tells you which MySQL tables to care about.

Core principle: a pick's position on /audit is fully explained by which gate decisions it received. If you can list the gates, you can explain the pick.

When to Use

• "Why is BTCUSDT not in Smart Picks?" / "Why did this pick get rejected?"
• "What is the FOREX pick funnel for the past week?"
• "Which ejaguiar1_stocks tables do I need to understand?"
• Onboarding someone to the /audit pipeline.
• Building a per-asset-class case study (opened + closed picks).

Not for: editing gate logic (that's quality_gates.py directly), or sports/events picks (different pipeline).

The Pipeline (7 stages)

1 EMIT      *_scanner.py / *_strategies.py / copy_trader_intel / coinglass  -> per-source JSON
2 INGEST    collect_all_picks() merges JSON -> at_raw_picks (the core ledger)
3 ACTIVE    quality_gates.passes_active_gate()  -> dashboard-visible or at_filter_log reject
4 SMART     quality_gates.passes_smart_gate() (= active gate + smart floors) + calculate_smart_score()
5 HC        tools/dashboard_hc_rules.passes_high_conviction_pick() — Gates 1-9 (JS twin: hc_filter.js)
6 CONSENSUS multi-source agreement -> at_consensus_picks
7 OUTCOME   outcome_resolver.py / forward_validator.py -> status + pnl_pct

The three /audit surfaces are nested filters on the same picks, not separate lists: Money Ready ⊂ High Conviction ⊂ Smart Picks ⊂ Active. A pick falls out at the first gate it fails.

See references/pipeline-map.md for every gate with file:line, env kill-switch, and order.

Quick Reference — trace a pick

# full flow of one pick (live MySQL, read-only)
python .claude/skills/audit-pick-flow/scripts/trace_pick.py --symbol BTCUSDT
python .claude/skills/audit-pick-flow/scripts/trace_pick.py --pick-id 145879

# per-asset-class funnel for a date range (the case-study query)
python .claude/skills/audit-pick-flow/scripts/pick_flow_funnel.py --days 7

Credentials: env AUDIT_DB_HOST / AUDIT_DB_USER / AUDIT_DB_PASS, or pass --pw. Host mysql.50webs.com, db ejaguiar1_stocks.

Quick Reference — the gate stack

#	Gate	Where	Rejects
Active	passes_active_gate	quality_gates.py:5774	safety STOP, magnitude-insane, blocked symbols/sources, kill-gate, quarantine, FOREX-LONG, low elite_score
Smart	passes_smart_gate	quality_gates.py:7545	active gate + per-class score/WR floors, forward_validated required, CRYPTO LONG-only
Score	calculate_smart_score	quality_gates.py:8399	0-100 ranking (not a gate)
HC	passes_high_conviction_pick	tools/dashboard_hc_rules.py:368	Gates 1-9: score floors, trust-tier blacklist, per-class FWD-WR, regime, walk-forward FAILING, ≥3-signal consensus
Money Ready	money_ready_verdict	alpha_engine/money_ready_verdict.py	per-CLASS verdict: n≥50, WR≥0.55, PF≥1.5, DSR≥0.95, PBO≤0.55

Every gate is env-var kill-switchable and fail-open. Gate config: config/hc_gate_params.json, config/hf_conviction_tiers.json.

Which DB tables to care about

Full table guide: references/db-tables.md. The short list — only 8 tables in ejaguiar1_stocks are on the pick-flow path; the other 315 are sports/mutual-fund/experiment noise.

Table	Role
at_raw_picks	core ledger — every raw pick, lifecycle, close
at_filter_log	every gate REJECTION (no PASS rows)
at_consensus_picks	picks that survived multi-source agreement
at_signal_outcomes	resolved entry/exit/pnl
at_aggregation_runs	one row per cycle
trading_picks	dashboard-facing store with score fields
at_pick_audit_trail	new — full per-gate trace (opt-in writer)
at_pick_flow_daily	new — nightly per-class funnel rollup

ejaguiar1_backtests (6 tables) is a research archive — not on the live pick path.

The audit-trail acceleration tables

audit_integration/05_pick_audit_trail_schema.sql adds two tables so a pick-flow audit is one indexed query instead of a multi-table JSON join:

• at_pick_flow_daily — populated nightly by MySQL event ev_pick_flow_daily_rollup off at_raw_picks+at_filter_log. Works today, zero Python. pick_flow_funnel.py reads it.
• at_pick_audit_trail — full ordered PASS/REJECT trace per pick. Needs a Python writer.

Wiring Plan (for at_pick_audit_trail)

Target caller: audit_trail/quality_gates.py — wrap each gate decision inside passes_active_gate / passes_smart_gate with an opt-in _emit_trace(pick, gate, decision) helper, env-gated by PICK_AUDIT_TRAIL_ENABLED (default OFF, sidecar — per the repo Wire-Up Rule). Until that writer ships, trace_pick.py falls back to at_filter_log (reject rows only). Expected wire-up: follow-up PR after this skill lands.

UI element → pick-quality impact (the fast path)

Every button / tab / filter on /audit is a filter over the same pick set. To find what any UI element does to pick quality:

1. Identify the element — audit_dashboard/audit_frontend_manifest.json is the codified list of all 47 buttons, 17 tabs, 31 filters, 10 ?Guide concepts (each with element id, handler, WIRED/ORPHANED status). Auto-refreshed daily by tools/audit_frontend_manifest.py (.github/workflows/audit-frontend-manifest.yml).
2. Measure its pick-quality impact — tools/edge_finder.py applies the element's filter and reports per-asset-class WR / PF / avg-pnl vs the unfiltered baseline: python tools/edge_finder.py --surface smart_picks (or --filter "elite_score>=60"). --search finds the field-threshold combo that maximizes PF.
3. Trace one pick through it — scripts/trace_pick.py --symbol X shows which gates that pick passed/failed, i.e. which surface it qualifies for.
4. Full per-surface trace — once the at_pick_surface_eval writer is wired, every pick carries in_active / in_smart_picks / in_high_conviction / in_money_ready flags
   • the frozen gate-input snapshot, so HC-vs-Smart-vs-Money-Ready divergence is one query.

Known result (2026-05-18): Smart Picks genuinely lifts CRYPTO/EQUITY into profit on its filtered subset; the High Conviction button is not DB-verifiable (trust data missing); the Money Ready button is orphaned (dead). See reports/ for the per-button audit.

Where /audit lives on GitHub + how it deploys

• Repo: github.com/eltonaguiar/findtorontoevents_antigravity.ca.
• Source of /audit: audit_dashboard/ — edit template.html (NEVER index.html, which is generated). Filter JS: hc_filter.js, money_ready_filter.js.
• Build: audit_trail/dashboard_generator.py turns DB + JSON → index.html + dashboard_data.json. Gates: audit_trail/quality_gates.py.
• Deploy: .github/workflows/audit-dashboard.yml — hourly cron (:10), generate-and-deploy job runs the generator, commits the data, then FTP-deploys to 3 sites (findtorontoevents.ca / tdotevent.ca / torontoevent.net). Not GitHub Pages.
• An AI agent reviewing /audit starts at: audit_dashboard/template.html (UI) + audit_dashboard/audit_frontend_manifest.json (element index) → quality_gates.py (gate logic) → dashboard_generator.py (build) → audit-dashboard.yml (deploy).

Common Mistakes

• Citing a JSON aggregate as "what the page shows" — /audit tiles are live-computed in template.html JS; by_asset_class in dashboard_data.json is raw/pre-noise-filter. Use asset_class_health or pf_registry.json for verdict-grade numbers.
• Trusting pnl_pct blindly — non-crypto picks are often closed with pnl_pct=0.0 placeholder (resolver gap); closed_at is frequently NULL even for terminal-status rows. Confirm with trace_pick.py before concluding.
• Assuming Money Ready is a pick list — it is a per-asset-CLASS statistical verdict, not per-pick. The /audit "💰 Money Ready" button is currently orphaned (no render path applies its filter); the Money Ready tab works.
• Running dashboard generators locally — they overwrite live HTML. py_compile only.

Real-World Impact

Past-week (2026-05-11..18) live funnel: CRYPTO emitted ~8.7k picks/wk at ~35% WR / PF 0.17 on closed; non-crypto classes emit 10-540/wk but resolve to pnl_pct=0 placeholders. Staleness (5,396) and no-consensus (1,600+) are the dominant rejection reasons. Full numbers: reports/audit_pick_flow_case_study_2026_05_18.md.