Prepare source-only public release for develop.

Add cluster audit pipeline, united EA updates, brochure generators, and publication hygiene (gitignore, MT5 path desensitization, pre-upload scan). Remove tracked reports, models, and binary artifacts from the repo. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-02 15:03:43 +02:00
parent 3f75a08848
commit 605faf5310
1014 changed files with 83437 additions and 10413 deletions
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+"""
+Lot sizing guidance for United EA combined portfolio.
+
+Compares per-strategy risk at 123.set nominal lots and suggests relative weights.
+
+Usage:
+  python -m cluster_audit.lot_sizing
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from cluster_audit.united_registry import UNITED_STRATEGIES
+
+REPORTS = Path(__file__).parent / "reports" / "united_sequential"
+MANIFEST = REPORTS / "united_manifest.json"
+OUT = REPORTS / "lot_sizing.json"
+REF_BALANCE = 1000.0
+
+
+def main() -> None:
+    manifest = {}
+    if MANIFEST.exists():
+        manifest = json.loads(MANIFEST.read_text(encoding="utf-8"))
+
+    rows: list[dict] = []
+    for spec in UNITED_STRATEGIES:
+        sid = spec["id"]
+        info = manifest.get("strategies", {}).get(sid, {})
+        if not info.get("passed"):
+            continue
+        o_net = float(info.get("net_profit", 0))
+        trades = int(info.get("trades", 1))
+        dd = float(info.get("max_drawdown_pct", info.get("issues", [""])[0] if False else 5))
+        lot = float(spec["lot"])
+        per_trade = o_net / max(trades, 1)
+        # risk proxy: lot * avg loss magnitude; use net/trades as PnL per trade signal
+        rows.append({
+            "id": sid,
+            "symbol": spec["symbol"],
+            "lot_123set": lot,
+            "net_profit": o_net,
+            "trades": trades,
+            "pnl_per_trade": round(per_trade, 2),
+            "max_dd_pct": dd,
+        })
+
+    if not rows:
+        print("No passed strategies in manifest — run united sequential audit first.")
+        return
+
+    # Target: equal risk contribution via inverse DD weighting
+    inv_dd = [1.0 / max(r.get("max_dd_pct", 5), 0.5) for r in rows]
+    total_inv = sum(inv_dd)
+    for i, r in enumerate(rows):
+        weight = inv_dd[i] / total_inv
+        r["risk_weight"] = round(weight, 4)
+        r["suggested_lot_vs_darvas"] = round(weight / (inv_dd[0] / total_inv), 3) if rows else 1.0
+
+    # Scale all lots so combined net at ref balance ~ sum of individuals / sqrt(N)
+    import math
+    n = len(rows)
+    diversification = math.sqrt(n)
+    base_lot = rows[0]["lot_123set"]
+    for r in rows:
+        r["suggested_lot_at_1k"] = round(base_lot * r["suggested_lot_vs_darvas"] / diversification, 4)
+
+    result = {
+        "reference_balance": REF_BALANCE,
+        "passed_count": n,
+        "diversification_factor": diversification,
+        "note": "suggested_lot_at_1k scales 123.set lots by inverse-DD weight / sqrt(N)",
+        "strategies": rows,
+    }
+    OUT.write_text(json.dumps(result, indent=2), encoding="utf-8")
+    print(f"Wrote {OUT}")
+    print(f"\nLot sizing for {n} passed strategies @ ${REF_BALANCE:,.0f} reference:\n")
+    for r in rows:
+        print(
+            f"  {r['id']:28} lot={r['lot_123set']:8} -> suggested={r['suggested_lot_at_1k']:8} "
+            f"(weight={r['risk_weight']:.2%}, pnl/trade=${r['pnl_per_trade']:.2f})"
+        )
+
+
+if __name__ == "__main__":
+    main()