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
@@ -0,0 +1,133 @@
+#!/usr/bin/env python3
+"""Optimize SimpleEMA v5 (trend-leg cross + pullback)."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import random
+import sys
+from dataclasses import asdict
+from datetime import datetime
+from pathlib import Path
+
+import MetaTrader5 as mt5
+import pandas as pd
+
+ROOT = Path(__file__).resolve().parents[3]
+LAB = Path(__file__).resolve().parent
+sys.path.insert(0, str(ROOT / "backtesting" / "MT5"))
+sys.path.insert(0, str(LAB))
+
+from cluster_audit.backtest_core import CostModel, load_bars, resolve_symbol  # noqa: E402
+from run_backtest import pip_size  # noqa: E402
+from strategy_v5 import V5Params, load_v5_cache, market_from_cache, sample_v5, simulate_v5, write_v5_set  # noqa: E402
+
+
+def seed_params() -> list[V5Params]:
+    """Known-good cross preset + selective pullback variants."""
+    return [
+        V5Params(fast_ema=10, slow_ema=36, cross_cooldown=2, htf_ema_period=100, use_pullback=False),
+        V5Params(fast_ema=10, slow_ema=46, cross_cooldown=8, htf_ema_period=200, use_pullback=False),
+        V5Params(fast_ema=7, slow_ema=46, cross_cooldown=5, use_pullback=False),
+        V5Params(
+            fast_ema=10, slow_ema=46, cross_cooldown=8, htf_ema_period=200,
+            use_pullback=True, pullback_touch=1, pullback_adx_min=22,
+            pullback_min_gap_pips=2.0, trend_leg_bars=56, pullback_cooldown=6,
+            max_pullbacks_per_leg=1,
+        ),
+    ]
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--trials", type=int, default=2000)
+    ap.add_argument("--min-trades", type=int, default=150)
+    ap.add_argument("--max-trades", type=int, default=2500)
+    ap.add_argument("--seed", type=int, default=42)
+    args = ap.parse_args()
+    out = LAB
+    rng = random.Random(args.seed)
+
+    if not mt5.initialize():
+        raise SystemExit("MT5 init failed")
+    try:
+        sym = resolve_symbol("EURUSD")
+        df = load_bars(sym, mt5.TIMEFRAME_M15, datetime(2020, 1, 1), datetime(2026, 1, 1))
+        costs = CostModel.for_symbol(sym)
+        pip = pip_size(sym)
+        point = float(mt5.symbol_info(sym).point)
+        print(f"v5 optimize {sym} M15 trials={args.trials} trades={args.min_trades}-{args.max_trades}")
+        cache = load_v5_cache(df)
+
+        best_profit = None
+        best_balanced = None
+        target = None
+        trial_rows = []
+
+        def eval_params(p: V5Params, n: int) -> None:
+            nonlocal best_profit, best_balanced, target
+            md = market_from_cache(cache, p)
+            r = simulate_v5(md, sym, p, costs, pip, point)
+            trial_rows.append({"trial": n, "net": r.net_profit, "trades": r.total_trades, "pf": r.profit_factor, **asdict(p)})
+
+            if args.min_trades <= r.total_trades <= args.max_trades and r.net_profit > 0 and r.profit_factor >= 1.05:
+                if target is None or r.net_profit > target[0].net_profit:
+                    target = (r, p)
+                    print(f"  HIT {n}: net=${r.net_profit:,.0f} t={r.total_trades} PF={r.profit_factor:.2f}")
+
+            if r.net_profit > 0:
+                if best_balanced is None or r.total_trades > best_balanced[0].total_trades or (
+                    r.total_trades == best_balanced[0].total_trades and r.net_profit > best_balanced[0].net_profit
+                ):
+                    best_balanced = (r, p)
+
+            if best_profit is None or r.net_profit > best_profit[0].net_profit:
+                best_profit = (r, p)
+
+        for i, p in enumerate(seed_params(), 1):
+            eval_params(p, i)
+            print(f"  seed {i}: net=${trial_rows[-1]['net']:,.0f} t={trial_rows[-1]['trades']} PF={trial_rows[-1]['pf']:.2f}")
+
+        for n in range(len(seed_params()) + 1, args.trials + 1):
+            eval_params(sample_v5(rng), n)
+            if n % 500 == 0:
+                b = best_balanced or best_profit
+                print(f"  ... {n}/{args.trials} best_bal t={b[0].total_trades} net=${b[0].net_profit:,.0f} hit={'yes' if target else 'no'}")
+
+        final_r, final_p = target or best_balanced or best_profit
+        assert final_r and final_p
+
+        write_v5_set(final_p, out / "SimpleEMA_optimized.set")
+        payload = {
+            "version": 5,
+            "target_met": target is not None,
+            "params": asdict(final_p),
+            "metrics": {k: v for k, v in asdict(final_r).items() if k != "trades"},
+        }
+        (out / "best_params.json").write_text(json.dumps(payload, indent=2), encoding="utf-8")
+        pd.DataFrame(trial_rows).to_csv(out / "optimize_trials.csv", index=False)
+
+        (out / "best_run").mkdir(exist_ok=True)
+        rows = [
+            {
+                "side": t["side"],
+                "open_time": df.index[t["open_i"]],
+                "close_time": df.index[t["close_i"]],
+                "profit": t["profit"],
+                "exit_reason": t["exit_reason"],
+            }
+            for t in final_r.trades
+        ]
+        pd.DataFrame(rows).to_csv(out / "best_run" / "trades.csv", index=False)
+
+        print(
+            f"\n{'TARGET' if target else 'BEST'}: net=${final_r.net_profit:,.2f} "
+            f"trades={final_r.total_trades} PF={final_r.profit_factor:.2f} WR={final_r.win_rate:.1f}% DD={final_r.max_drawdown_pct:.1f}%"
+        )
+    finally:
+        mt5.shutdown()
+
+
+if __name__ == "__main__":
+    main()