money-maker-readyv2

name

money-maker-readyv2

description

Ultimate statistical edge audit per asset class. Produces proven, real-money-grade filters for CRYPTO/EQUITY/COMMODITY/ETF/FOREX/BOND so that a quant or hedge fund manager would find the edge trustworthy. Extends money-maker-ready v1.1 with stronger success criteria, autonomous execution rules, and a weekly filter output. Use when the user says "/money-maker-readyv2", "prove the edge", "real money filters", or "ultimate statistical edge". Aliases - money-maker-readyv2, ultimate-edge, prove-edge.

/money-maker-readyv2 — Ultimate Statistical Edge Audit

Goal: Produce proven, quantitatively-validated filters per asset class such that:

• Top picks grow a live balance over time
• Downside is limited (MDD≤20%, position sizing by Kelly/Hyro overlay)
• Winners beat losers (PF≥1.5, WR≥50%)
• A quant/hedge-fund manager would rate the edge trustworthy for real capital

Inherits all hard rules from /money-maker-ready (v1.1). This v2 adds:

Success Criteria (ALL must be true before calling done)

1. EQUITY: Weekly filter output shows ≥5 picks with elite_score≥60, WR≥55% on historical analogs (n≥30 per filter bucket), PF≥1.5 on resolved picks.
2. CRYPTO: Sub-class filters (WR≥50%) identified and live — picks passing the filter have PF≥1.5 on resolved_n≥100.
3. COMMODITY: Post-COT-dedup clean picks accumulate; once n≥50 post-dedup, top strategy identified with PF≥1.5.
4. ETF: n≥150 on path to OOS_READY; PF maintained ≥1.3; top ETF strategies identified.
5. FOREX: Mutation protocol in progress; LONG-direction block evaluated by data; directional filter identified if WR≥50% exists.
6. BOND: Accumulating picks; top BOND strategy (if any) identified once n≥20.
7. Kelly sizing: All weekly filter picks have a computed position size (% of account) via compute_position_size() with DD-halt guard.

Operating Rules (NON-NEGOTIABLE)

1. PLAN FIRST — numbered task list before any code.
2. WORK AUTONOMOUSLY — no clarifying Qs unless genuinely blocked.
3. SELF-VERIFY — after every step: run tests, inspect output, confirm it worked.
4. DEBUG YOURSELF — if it fails, diagnose + fix; don't hand back.
5. USE EVERY TOOL — terminal, code exec, real data.
6. NO PLACEHOLDERS — real components + real states; no TODOs.
7. PROGRESS LOG — track completed / in-flight / decisions / blockers.
8. STAY ON GOAL — off-spec discoveries: note + keep moving.
9. IF BLOCKED — log the wall, continue everything parallelizable.
10. CHECK SUCCESS BEFORE STOPPING — re-read criteria, confirm each is met.

Data Sources (read, never invent)

Source	Field	Purpose
audit_dashboard/data/dashboard_data.json::performance.asset_class_health	n, WR, PF	Verdict-grade resolved_n per class
audit_dashboard/data/dashboard_data.json::walkforward.by_class	oos_wr, oos_sharpe	OOS verification
audit_dashboard/data/dashboard_data.json::fwd_vs_bt_divergence.rows	BT vs OOS WR gap	Overfit detector
audit_trail/quality_gates.py::BLOCKED_ASSET_STRATEGY_PAIRS	current blocks	Active gate
docs/PERFORMANCE_CHARTER.md	Tier thresholds	T1/T2/T3 definition

Freshness Gate (FAIL-FAST if >2h stale)

import json; from pathlib import Path; from datetime import datetime, timezone
d = json.loads(Path("audit_dashboard/data/dashboard_data.json").read_text())
age_h = (datetime.now(timezone.utc) - datetime.fromisoformat(d["generated_at"].rstrip("Z")).replace(tzinfo=timezone.utc)).total_seconds()/3600
assert age_h < 2, f"STALE: {age_h:.1f}h — abort"

Weekly Filter Output Format

After the audit, produce reports/weekly_filter_<UTC>.md:

# Weekly Real-Money Filter — <DATE>

## EQUITY Top Picks Filter
- Criteria: elite_score≥60, asset_class=EQUITY, status=OPEN, direction=LONG
- Expected WR (historical analog): XX% (n=YY)
- Expected PF (historical analog): X.XX
- Kelly size: X.X% of account per pick

## [Repeat per class that has n≥100 resolved picks]

## How to Apply
1. Open findtorontoevents.ca/audit
2. Apply filter: [specific UI filter steps]
3. Size per Kelly recommendation
4. Exit: follow TP/SL as set on pick

## Risk Controls
- Max per-pick: X% of account (Kelly 0.25-fraction)
- Daily soft-stop: -2% total PnL triggers pause (Hyro overlay)
- DD halt: if rolling_30d drawdown > 30%, pause all sizing

Execution Workflow

Step 1 — Freshness preflight (fail-fast)

Run the freshness gate above. If stale, surface + ask.

Step 2 — Per-class baseline

Read asset_class_health. Per class: n, WR, PF, Tier vs charter.

Step 3 — Identify proven filter per class

For each class with resolved_n≥50, query (or derive from dashboard_data.json) the top-performing:

• Strategy family
• Direction (LONG vs SHORT)
• Confidence bucket (high/medium)
• Time-of-day window (if data available)

Step 4 — Compute Kelly sizing

For each proven filter, use alpha_engine/kelly_position_sizer.py::compute_position_size() to output:

• % of account per pick
• Expected position size in USD at $10k account

Step 5 — Write weekly filter report

Output reports/weekly_filter_<UTC>.md with filter per class + sizing.

Step 6 — Verify picks hit the filter

Pull current OPEN picks from audit_dashboard/data/dashboard_data.json::systems or JSON pick sources. Apply each filter, count matches.

Step 7 — Commit report

git add reports/weekly_filter_<UTC>.md
git commit -m "feat(edge): weekly real-money filter + Kelly sizing <DATE>"

Quality Bar

• Code: clean, typed, follows project conventions
• Design: looks like a well-funded startup shipped it
• Output: survives a senior code review
• Docs: every new pattern / env var / decision logged

Constraints (same as money-maker-ready v1.1)

• NEVER edit audit_dashboard/index.html
• NEVER run audit_trail/dashboard_generator.py locally
• NEVER add to BLOCKED_ASSET_STRATEGY_PAIRS without explicit user approval
• NEVER claim performance without (asset_class | n | timeframe) triple
• NEVER push without pulling first

Final Deliverable

• Confirmation each success criterion is satisfied
• Every file created / modified
• How to run / test / deploy
• Proof (test output + filter + URL)
• Decisions made + known limitations