| name | money-maker-ready |
| description | Use when user wants to audit findtorontoevents.ca/audit (+ /audit/hyrotrader, ejaguiar1_stocks, ejaguiar1_backtests) for production-grade real-money readiness. Per-asset-class statistical-edge hunt with cross-source correlation, external-data integration suggestions, dashboard UI/filter audit, and ranked Best-Possible-Action recommendations. Triggers — `/money-maker-ready`, "is /audit ready for real money?", "find edge per asset class", "money maker audit", "edge hunt", "what's our best statistical edge?", "should we go live with X asset class?". Aliases — mmready, money-maker, edge-hunt, real-money-audit, edge-by-class. |
Money-Maker-Ready Audit Skill
Goal: turn the 1-shot /audit real-money-readiness investigation into a repeatable, evolving evaluation. Project owner wants /audit ready for real-money use ("hedge-fund level picks per asset class, people spend their REAL money"). Scope now explicitly includes per-model + per-portfolio performance on /audit/ai_leaderboard.html + /audit/ai-tournament.html (Model Portfolios — Risk-Managed Books), not just per-class picks.
2026-06-10 REALITY UPDATE — the honest layer is LIVE (read before auditing)
The measurement layer is no longer the blocker — it's fixed and self-sustaining. Audit AGAINST it, don't re-discover it:
- Honest source of truth =
money_ready_verdict.json → classes.<CLASS>.intrabar_truth (n/wr/pf from the entry-anchored at_signal_outcomes.intrabar_* ledger). The verdict JSON is NESTED: {generated_at, classes:{CLASS:{... intrabar_truth:{n,wr,pf}}}, drift, summary}. Live on findtorontoevents.ca; both writer workflows (audit-dashboard.yml + money-ready-snapshot.yml) are env-complete.
- Entry-anchored resolver is the production DEFAULT (
RESOLVER_ENTRY_ANCHORED=1, rollback to 0). The old 23-24% WR inflation is closed. tools/reresolve_intrabar_signal_outcomes.py --apply --i-understand-this-mutates-production re-resolves; tools/build_intrabar_truth_by_class.py rebuilds the per-class JSON.
- Honest verdict today (2026-06-10): 0/9 classes money-ready. CRYPTO n=1154 32.4%/PF0.73 FAIL; EQUITY n=107 34.6%/PF0.47 FAIL (both crossed n≥100 and FAIL — small-n leads dissolve as n grows); COMMODITY n=90 / FOREX n=88 approaching n=100. PBO=0.822 (FAIL≥0.7) still correctly blocks promotion.
- The deficit is ENTRY SELECTION, not exit geometry (σ-geometry experiment proved vol-scaled exits trade WR for TIME_EXIT one-for-one). The forward edge-hunt lives in
tools/stamp_entry_conditions.py → entry_conditions_forward.json (read-only sidecar; conditions like crypto RSI50-70×US-session tracked but NOT promoted until n≥100 + R1/R2/R3 re-pass). When auditing, check this sidecar for live entry-condition candidates.
- DO-NOT-RELITIGATE (already refuted/done this period): stocks_rsi2_pullback promote, CRYPTO direction-flip, futures_momentum/forex_rsi2 shadow-track, luxalgo "best-in-system", MeanReversionBB, trust=7 edge, alpha_engine×CRYPTO 80.6%, the MiniMax-authored
kimi_ultimate_proven_edge (self-admitted SYNTHETIC). Tournament 73-91% WR + leaderboard WR = single-snapshot/MISPRICED_ENTRY artifacts, never sizing surfaces.
- DB components (verify before any metric): read via
tools/db_env.py → get_stocks_creds() (ejaguiar1_stocks — live picks/outcomes), get_backtests_creds() (ejaguiar1_backtests — bt_backtest_trades), get_backups_creds() (ejaguiar1_backups). Passwords resolve from env or /home/eaguiar2015/dbpasses.txt (gitignored — NEVER commit/echo). Any table-mutating op MUST back up to ejaguiar1_backups first (see tools/db_backup_to_backups.py; backup-table names must stay ≤64 chars). File incidents/enhancements via tools/audit_pick_funnel/cli_track.py (idempotent upsert) then render_incidents_page.py + FTP --only incidents.
Hard rule (enforced throughout)
Every performance claim must be labeled (asset_class | n | timeframe). Reject any claim missing any of these. Cite source path + line number from actual data files (audit_dashboard/data/dashboard_data.json::path.to.field).
MANDATORY DATA-INTEGRITY FILTERS (added 2026-06-09). Raw at_pick_outcomes WR/PF are inflated artifacts. Before computing ANY metric you MUST: (1) exclude resolver_version LIKE 'backfill%' + NULL resolved_at; (2) exclude BANNED_SOURCES; (3) apply the per-class sane-pnl guard (drops reverse-split / feed-bug rows like CADJPY +428%); (4) count EXPIRED as a non-win (WR = WON/(WON+LOST+EXPIRED+FLAT)); (5) treat any WR as provisional until intrabar-validated (tools/reresolve_intrabar.py — resolver doesn't replay intrabar; 26% of CRYPTO TP_HITs hit SL first); (6) require ≥3 months n≥30/mo (reject single-snapshot / resolver-version artifacts). Full recipe + thresholds: see /money-maker-readyv2 → "MANDATORY DATA-INTEGRITY FILTERS" and reports/2026-06-06-money-ready-screen-clean-cohort.md. Clean-cohort screen as of 2026-06-09: 0 confirmed money-ready survivors in any class.
n-citation discipline (added v1.1, 2026-05-15). The verdict-grade count is
asset_class_health.<CLASS>.n (= resolved_n = wins + losses after the
_is_valid_resolved_pick filter). It is a deterministic pure-function output —
proven reproducible (tools/_verify_n_reproducible.py, 3 identical SHA256 runs;
see reports/asset_class_verification_2026-05-15.md). Do NOT cite raw closed
(by_asset_class.<CLASS>.closed) as n — it is 1.5-3× larger (it includes
corrupted / blocked-history / pip-confused / pnl==0 rows). Reports that quote
raw closed as the verdict n are the source of every "n drift" complaint.
Always say which one you mean.
Plan-staleness rule. FOOLPROOF_ACTION_PLAN.md and the 90-day plans carry
snapshot numbers that go stale within days and have shipped self-contradictions
(BOND PF 0.66 vs 1.72). NEVER quote a plan's PF/WR/n as current — re-read
asset_class_health live and treat the plan only as intent/direction.
REJECT-without-reverify (2026-06-09): FOREX 14d 64%/PF 2.43, GBPUSD 58.8%, stocks_rsi2_pullback n=894, RENDERUSDT inverse_ml, AI tournament WR as trading edge, 32.6M backtest rows as live edge. Full list: reports/OBS_FINDING_JUNE8.MD.
SAVE THE SYSTEM: See /money-maker-readyv2 → SAVE THE SYSTEM. Honest answer today: 0 confirmed money-ready edges; fix measurement layer first.
Inputs (read, never invent)
| Source | What | Status to verify |
|---|
audit_dashboard/data/dashboard_data.json | Canonical post-resolver-v2 dashboard data, 18.8MB, hourly auto-refreshed | ::generated_at not stale (>2h = stale) |
docs/PERFORMANCE_CHARTER.md | Tier 1/2/3 thresholds, KPI definitions | v1.0 2026-04-28 = canonical |
TESTING_PROTOCOL.MD | Layered test stack L0-L9, rehab pipeline | re-check date |
docs/MUTATION_THREE_AXIS_PROTOCOL.md | Symbol/direction/timeframe split before kill | required reading before BLOCKED edits |
docs/STRATEGY_INVESTIGATION_BEFORE_KILL.md | Investigation ladder | required reading before BLOCKED edits |
audit_trail/quality_gates.py::BLOCKED_ASSET_STRATEGY_PAIRS | Current per-class strategy blocks | live |
alpha_engine/config.py::BLACKLISTED_STRATEGIES | Per-strategy blocks at pick-intake | live |
audit_dashboard/template.html | Real dashboard HTML — UI/filter buttons | DO NOT edit audit_dashboard/index.html (auto-generated) |
audit_dashboard/template.html (hyrotrader section) | hyrotrader-specific tabs/filters | locate hyrotrader mentions |
mysql.50webs.com::ejaguiar1_stocks | Live primary DB (322 tables) | creds in Windows env DB_NAME_STOCKS / DB_PASS_STOCKS (NOT live-monitor/api/.env) |
mysql.50webs.com::ejaguiar1_backtests | Backtest history DB (6 tables) | Windows env DB_NAME_BACKTESTS / DB_PASS_BACKTESTS |
docs/DB_SCHEMA_stocks_backtests_2026-05-15.md | Up-to-date DB schema doc | regenerate via tools/_db_schema_doc.py (reads env creds) |
FOOLPROOF_ACTION_PLAN.md | Master action plan (amended 2026-05-15) | intent only — verify numbers live |
reports/asset_class_action_items_2026-05-15.md | Per-class action items (graphify cross-ref) | latest verified action list |
reports/asset_class_verification_2026-05-15.md | Verification deep-dive + method/evidence | the proof layer |
reports/SUPREME_PLAN_90days.md + reports/asset_class_90day_plan_*_2026-05-15.md | 90-day plans per class | intent only — verify numbers live |
audit_dashboard/data/dashboard_data.json::walkforward.by_class | Walk-forward OOS per class | freshness check |
audit_dashboard/data/dashboard_data.json::fwd_vs_bt_divergence.rows | Backtest-overfit detector | live |
audit_dashboard/data/dashboard_data.json::hf_stats | Sharpe/MDD/Calmar per class | check freshness |
Output structure (deterministic)
Produce ONE markdown file at reports/money_maker_ready_<UTC>.md with these sections in this order:
# Money-Maker-Ready Audit — <UTC>
## 0. Freshness preflight
[fail-fast if dashboard_data.json::generated_at older than 2h]
## 1. Per-class baseline (verdict-grade)
[asset_class_health table; tier vs charter]
## 2. Walk-forward verification (per class, OOS)
[walkforward.by_class table; consistency, decay, oos_sharpe]
## 3. Cumulative-since-inception system winners (Tier-2-MDD-verified)
[systems where PF>=1.5, WR>=50, MDD<=20, n>=100; per (asset_class | n | timeframe)]
## 4. System draggers (negative PnL contribution)
[bottom 10 systems by pnl_pct; flag PF<0.5 OR -100 PnL% as kill candidates]
## 5. Backtest-overfit detector flags
[fwd_vs_bt_divergence.rows; group by system family; recommend surgical quarantine]
## 6. Drift state
[hf_stats::concept_drift; auto-pause-recommend if D > 0.10]
## 7. UI/Filter audit
[Smart Picks tab vs filter behavior; High-Conviction button audit]
## 8. External data integrations to consider for more edge
[ranked list with expected-impact estimate]
## 9. Top statistical edges per asset class
[per (CRYPTO/EQUITY/COMMODITY/ETF/FOREX/BOND): symbol+strategy+source combos that meet stat-sig + Tier-2 thresholds]
## 10. Best-Possible-Action ranked recommendations
[priority P0-P5 with expected impact, effort, risk]
## 11. Verifiable claims log
[reproducible commands + git SHAs]
Step-by-step workflow
Step 1 — Freshness preflight (FAIL-FAST)
python -c "
import json, time
from pathlib import Path
from datetime import datetime, timezone
p = Path('audit_dashboard/data/dashboard_data.json')
d = json.loads(p.read_text(encoding='utf-8'))
gen = datetime.fromisoformat(d['generated_at'].replace('Z','+00:00').rstrip('Z'))
age_h = (datetime.now(timezone.utc) - gen).total_seconds()/3600
if age_h > 2:
print(f'STALE: {age_h:.1f}h old; aborting per Money-Maker-Ready protocol')
exit(1)
print(f'OK: dashboard_data.json {age_h:.1f}h old')
"
If stale: surface to user, ask whether to proceed anyway or wait for next refresh cycle (hourly via chore(audit-dashboard): refresh payload [skip ci] cron).
Step 2 — Per-class baseline
Read dashboard_data.json::performance.asset_class_health. Per class:
| Class | n | WR | PF | MDD (if available) | sample_tier | Tier vs charter |
|---|
Tier mapping (per docs/PERFORMANCE_CHARTER.md):
- Tier 1: PF≥2.0, WR≥55%, MDD≤10%, n≥200
- Tier 2: PF≥1.5, WR≥50%, MDD≤20%, n≥100
- Tier 3: PF≥1.2, WR≥45%, MDD≤25%, n≥100
- Below: <1.2 PF OR <45% WR OR >25% MDD OR n<100
Step 3 — Walk-forward verification
Read dashboard_data.json::walkforward.by_class AND walkforward.generated_at. Per-class table: folds, oos_wr, oos_wr_std, oos_sharpe, decay, consistency, worst-best WR. Flag classes missing from by_class.
Verify: positive decay = strategy improves OOS (good); negative decay = backtest overfit. Consistency <80% = unstable.
Step 4 — Cumulative system winners
Filter dashboard_data.json::systems for systems where:
profit_factor >= 1.5win_rate >= 50max_drawdown <= 20closed_picks >= 100
Output table: name | asset_classes (multi) | closed_picks | win_rate | profit_factor | max_drawdown | last_signal | live_status (active/monitoring/dead).
Mark DEAD any system where last_signal_at > 30 days ago — flag INACTIVE.
Step 5 — System draggers
Filter for systems where total_pnl_pct < -50 OR profit_factor < 0.5. Sort by most negative PnL. Recommend per docs/STRATEGY_INVESTIGATION_BEFORE_KILL.md — investigation gate before BLOCK.
Step 6 — Backtest-overfit detector
Read dashboard_data.json::fwd_vs_bt_divergence.rows. Group by system. For each family with ≥3 flagged strategies, recommend SURGICAL quarantine (per-strategy add to BLOCKED_ASSET_STRATEGY_PAIRS), NOT system-wide block. Template: reports/baby_strats_overfit_quarantine_proposal_2026_05_10.md.
Step 7 — Drift state
Read dashboard_data.json::hf_stats.concept_drift. If drift_alert: True:
- Surface KS_D + critical
- Compute
D / critical ratio (>5 = severe)
- Recommend auto-pause sizing if D > 0.10
Standard response: pause new sizing, investigate distribution shift, re-train or re-validate before resume.
Step 8 — UI / Filter audit
Read audit_dashboard/template.html. Locate:
- "Smart Picks" tab → grep
id="tab-smart" or similar
- "High-Conviction" button → grep
data-filter="high_conviction" or similar
- Filter chips: "All", "Today", "Active", per-asset-class chips
Per filter, verify:
- JS filter logic matches user-facing label
- Filter excludes based on actual
dashboard_data.json field, not hardcoded
- "High-Conviction" defined consistently with
confidence_recalibrator if present (confidence may be inverted across classes)
Output: list of UI/filter mismatches between label and code behavior.
Step 9 — External data integrations to consider
| Library / data source | Asset class fit | Expected impact | Integration effort | Current gap |
|---|
| VectorBT (polakowo) | Crypto + HFT | 50-100x faster vectorized backtests | Low-Med | Slow backtest iteration |
| Riskfolio-Lib (dcajasn) | All (esp. Hyro/Prop) | CVaR/HRP risk budgeting | Low | Risk-cap audit gap |
| pandas-ta (twopirllc) | All | 130+ indicators | Very Low | Manual indicator code |
| Freqtrade (freqtrade) | Crypto live | Hyperopt + live execution | Med | Live exec layer |
| FinRL (AI4Finance) | Crypto adaptive | RL regime switching | High | Regime adaptation |
| QuantStats (ranaroussi) | Reporting | Pro perf reports | Very Low | Audit report polish |
| PyPortfolioOpt (robertmartin8) | Stocks | Black-Litterman, HRP | Low | Multi-asset portfolio opt |
| CoinGecko Pro API | Crypto | Mirror exchange data + on-chain | Low | Already in failover chain |
| Polymarket API | Cross-asset sentiment | Real-money prediction-market signal | Med | Already wired (prediction_market_consensus.py) |
| Kalshi API | Cross-asset (US-regulated) | Pairwise consensus with Polymarket | Med | Sidecar: pm_consensus_overlay.py |
| FRED data (St. Louis Fed) | Macro | Yield curves, VIX, DXY | Low | fred_data_fetcher.py exists, need FRED_API_KEY |
| Glassnode / Coinglass | On-chain | Whale flows, funding rates | Med | Some integration; widen |
Step 10 — Top statistical edges per asset class
For each class with sufficient data (n≥100), output TOP-N (N=5) (symbol, strategy, source_system) triples where:
n >= 8 (TESTING_PROTOCOL Layer 2.5 stat-sig gate)WR >= 52% (above coin-flip threshold)PF >= 1.5 (Tier 2 PF floor)
If DB credentials available, run live SQL. Otherwise pull from dashboard_data.json::cross_strategy_permutations or cross_system_permutations.
Per-class template:
### CRYPTO top 5 edges
| Rank | (symbol | strategy | source_system) | n | WR % | PF | Avg PnL % | timeframe |
Step 11 — Best-Possible-Action ranked recommendations
Final output P0-P5 ranked list:
| Priority | Action | Asset class impact | Effort (hr) | Risk | Reversibility | Expected lift |
Starter kit (re-based 2026-06-09 — measurement first; see SAVE THE SYSTEM in v2):
P0 — measurement (blocks everything)
- Intrabar re-resolution:
tools/reresolve_intrabar.py (dry-run → --apply after OHLCV backfill).
- Backfill
crypto_ohlcv ≥180d 1h bars (gating dependency; ~30d today).
- Backfill quarantine + sane-pnl guard ✅ (
build_pf_registry.py, picks_now_professional.py).
P1 — re-baseline & paper-pilot
- Clean+intrabar screen (
reports/OBS_FINDING_JUNE8.MD); forward paper-pilot ≥4wk only.
- Ban FOREX bleeders; wire
apply_emitter_discipline.
P2 — academic sleeves (after P0)
- TSMOM, residual momentum, carry — dormant modules; trailing-stop exits fix expiry trap.
P3 — legacy open
- PENNY/MEMECOIN gate; quan_engine cap desync; ETF emitter; drift auto-pause; FRED_API_KEY.
Never promote from: pick_summary_stats_14d alone, Obsidian T1 claims, AI tournament leaderboard, low_hanging_fruit_report.md.
Output destination + persistence
TS=$(date -u +%Y%m%dT%H%M%SZ)
OUT="reports/money_maker_ready_${TS}.md"
git add "$OUT"
git commit -m "audit(money-maker-ready): per-class edge audit ${TS}"
Hard constraints (refuse if violated)
- NEVER run
audit_trail/dashboard_generator.py locally (overwrites live HTML per CLAUDE.md)
- NEVER edit
audit_dashboard/index.html (auto-generated)
- NEVER auto-add to
BLOCKED_ASSET_STRATEGY_PAIRS or BLACKLISTED_STRATEGIES without explicit user approval
- NEVER claim performance numbers without
(asset_class | n | timeframe) triple
- NEVER trust per-engine swarm-fabricated file contents — verify with grep/ls before acting
- NEVER hardcode credentials in committed code (use
live-monitor/api/.env or env vars)
Cross-skill integration
Consumes outputs from:
check-gh-actions (verify CI pipeline health before trusting auto-refresh)homepage-perf-audit (sister skill for consumer-facing homepage)
Produces inputs for:
swarm-second-opinion (multi-engine review of audit findings)swarm-pr-review (when audit recommends a code change)
Companion subagents
When deep investigation needed:
- Spawn
cavecrew-investigator to locate specific code paths (e.g. "where is passes_smart_gate called?")
- Spawn
pr-reviewer if recommending PR-level change
- Spawn 4-5 engine swarm via
swarm-run.py for second opinion on Best-Possible-Action ranking
Failure modes the skill must handle
| Mode | Detection | Response |
|---|
| Dashboard data >2h stale | freshness preflight | abort, surface, ask wait/proceed |
| MySQL unreachable | timeout on DB query | fall back to dashboard_data.json aggregates + flag |
| Walkforward absent for class | empty by_class[CLASS] | mark "UNVERIFIED — walk-forward missing" not "WORKING" |
| Drift alert TRUE | concept_drift.drift_alert | emit P1 recommendation, do NOT proceed to "ready for real money" verdict |
Hermes peer in shared .git | files appear modified mid-run | snapshot working tree before/after, never auto-commit peer changes |
Versioning
Skill version 1.1 (2026-05-15). v1.0 shipped 2026-05-11 from
docs/proposals/money-maker-ready_skill_proposal_2026-05-10.md.
v1.1 changes: n-citation discipline + plan-staleness rule; DB creds moved to
Windows env (DB_PASS_STOCKS / DB_PASS_BACKTESTS); inputs now include the
amended FOOLPROOF_ACTION_PLAN.md, the 2026-05-15 action-items + verification
reports, and the 90-day plans; starter recommendations re-based on the verified
action stack; kill_gate / volume-cap / graphify rows added. After each run,
capture lessons learned + refine.
Acceptance criteria (v1.0)
- Skill runs end-to-end in <5 min on cold cache
- Output report has all 11 sections populated
- ≥1 fabrication-flag triggered (proves verifier works)
- Best-Possible-Action P0 list survives 4-engine swarm review
- UI/filter audit catches at least 1 mismatch (proves it's not a checklist)
Reference data sources
| Source | Path | Status |
|---|
| Verdict-grade per-class | audit_dashboard/data/dashboard_data.json::performance.asset_class_health | live |
| Walk-forward by class | audit_dashboard/data/dashboard_data.json::walkforward.by_class | live |
| Backtest-overfit | audit_dashboard/data/dashboard_data.json::fwd_vs_bt_divergence.rows | live |
| HF metrics (drift) | audit_dashboard/data/dashboard_data.json::hf_stats | check freshness |
| System list | audit_dashboard/data/dashboard_data.json::systems | live |
| Tier thresholds | docs/PERFORMANCE_CHARTER.md v1.0 | canonical 2026-04-28 |
| BLOCKED pairs | audit_trail/quality_gates.py BLOCKED_ASSET_STRATEGY_PAIRS (~:1933) | live — pairs only, no class-wide gate |
| BLOCKED triples | audit_trail/quality_gates.py BLOCKED_ASSET_STRATEGY_TRIPLES (~:2045) | live — direction axis; no FOREX rows yet |
| Blacklist | alpha_engine/config.py BLACKLISTED_STRATEGIES | live |
| Kill gate (M-055) | audit_trail/kill_gate.py | wired into commodity_kill_switch.py / fx_kill_switch.py / policy_backtest.py; NOT in passes_active_gate |
| Volume caps | alpha_engine/per_source_volume_cap.py + quarantine_manifest.json | desynced (5% vs 12%) — verify both |
| Live dashboard HTML | audit_dashboard/template.html | live |
| Codebase knowledge graph | graphify-out/graph.json via /graphify-ask | query for "which module feeds X" |
