zhutoutoutousan
605faf5310
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>
110 lines
4.0 KiB
Python
110 lines
4.0 KiB
Python
#!/usr/bin/env python3
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"""Optimize SimpleEMA v4 (regime dual entry + partial TP)."""
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from __future__ import annotations
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import argparse
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import json
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import random
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import sys
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from dataclasses import asdict
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from datetime import datetime
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from pathlib import Path
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import MetaTrader5 as mt5
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import pandas as pd
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ROOT = Path(__file__).resolve().parents[3]
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LAB = Path(__file__).resolve().parent
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sys.path.insert(0, str(ROOT / "backtesting" / "MT5"))
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sys.path.insert(0, str(LAB))
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from cluster_audit.backtest_core import CostModel, load_bars, resolve_symbol # noqa: E402
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from run_backtest import pip_size # noqa: E402
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from strategy_v4 import V4Params, load_v4_cache, market_from_cache, sample_v4, simulate_v4, write_v4_set # noqa: E402
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--trials", type=int, default=3000)
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ap.add_argument("--min-trades", type=int, default=400)
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ap.add_argument("--max-trades", type=int, default=2500)
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ap.add_argument("--seed", type=int, default=42)
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args = ap.parse_args()
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out = LAB
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rng = random.Random(args.seed)
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if not mt5.initialize():
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raise SystemExit("MT5 init failed")
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try:
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sym = resolve_symbol("EURUSD")
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df = load_bars(sym, mt5.TIMEFRAME_M15, datetime(2020, 1, 1), datetime(2026, 1, 1))
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costs = CostModel.for_symbol(sym)
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pip = pip_size(sym)
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point = float(mt5.symbol_info(sym).point)
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print(f"v4 optimize {sym} M15 trials={args.trials} trades={args.min_trades}-{args.max_trades}")
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cache = load_v4_cache(df)
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best_profit = None
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best_balanced = None
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target = None
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for n in range(1, args.trials + 1):
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p = sample_v4(rng)
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md = market_from_cache(cache, p)
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r = simulate_v4(md, sym, p, costs, pip, point)
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if args.min_trades <= r.total_trades <= args.max_trades and r.net_profit > 0 and r.profit_factor >= 1.05:
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if target is None or r.net_profit > target[0].net_profit:
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target = (r, p)
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print(f" HIT {n}: net=${r.net_profit:,.0f} t={r.total_trades} PF={r.profit_factor:.2f}")
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if r.net_profit > 0:
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if best_balanced is None or r.total_trades > best_balanced[0].total_trades or (
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r.total_trades == best_balanced[0].total_trades and r.net_profit > best_balanced[0].net_profit
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):
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best_balanced = (r, p)
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if best_profit is None or r.net_profit > best_profit[0].net_profit:
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best_profit = (r, p)
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if n % 500 == 0:
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b = best_balanced or best_profit
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print(f" ... {n}/{args.trials} best_bal t={b[0].total_trades} net=${b[0].net_profit:,.0f} hit={'yes' if target else 'no'}")
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final_r, final_p = target or best_balanced or best_profit
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assert final_r and final_p
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write_v4_set(final_p, out / "SimpleEMA_optimized.set")
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payload = {
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"version": 4,
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"target_met": target is not None,
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"params": asdict(final_p),
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"metrics": {k: v for k, v in asdict(final_r).items() if k != "trades"},
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}
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(out / "best_params.json").write_text(json.dumps(payload, indent=2), encoding="utf-8")
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(out / "best_run").mkdir(exist_ok=True)
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rows = [
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{
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"side": t["side"],
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"open_time": df.index[t["open_i"]],
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"close_time": df.index[t["close_i"]],
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"profit": t["profit"],
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"exit_reason": t["exit_reason"],
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}
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for t in final_r.trades
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]
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pd.DataFrame(rows).to_csv(out / "best_run" / "trades.csv", index=False)
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print(
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f"\n{'TARGET' if target else 'BEST'}: net=${final_r.net_profit:,.2f} "
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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}%"
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)
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finally:
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mt5.shutdown()
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if __name__ == "__main__":
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main()
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