feat: strategy parameter sweep and production param optimization
- Add independent backtest engine (backtester.py) with walk-forward support - Add backtest sanity check validator (backtest_validator.py) - Add CLI tools: run_backtest.py, strategy_sweep.py (with --combined mode) - Fix train-serve skew: unify feature z-score normalization (ml_features.py) - Add strategy params (SL/TP ATR mult, ADX filter, volume multiplier) to config.py, indicators.py, dataset_builder.py, bot.py, backtester.py - Fix WalkForwardBacktester not propagating strategy params to test folds - Update production defaults: SL=2.0x, TP=2.0x, ADX=25, Vol=2.5 (3-symbol combined PF: 0.71 → 1.24, MDD: 65.9% → 17.1%) - Retrain ML models with new strategy parameters Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
21in7
211
scripts/run_backtest.py
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211
scripts/run_backtest.py
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#!/usr/bin/env python3
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"""
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백테스트 CLI 진입점.
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사용법:
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python scripts/run_backtest.py --symbol XRPUSDT
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python scripts/run_backtest.py --symbols XRPUSDT,TRXUSDT,DOGEUSDT
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python scripts/run_backtest.py --symbol XRPUSDT --no-ml
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python scripts/run_backtest.py --symbol XRPUSDT --start 2025-06-01 --end 2026-03-01
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python scripts/run_backtest.py --symbol XRPUSDT --fee 0.04 --slippage 0.02
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python scripts/run_backtest.py --symbol XRPUSDT --walk-forward
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python scripts/run_backtest.py --symbol XRPUSDT --walk-forward --train-months 6 --test-months 1
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import argparse
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import json
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from datetime import datetime
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import numpy as np
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from loguru import logger
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from src.backtester import Backtester, BacktestConfig, WalkForwardBacktester, WalkForwardConfig
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def parse_args():
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p = argparse.ArgumentParser(description="CoinTrader Backtest Engine")
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group = p.add_mutually_exclusive_group(required=True)
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group.add_argument("--symbol", type=str, help="단일 심볼 (e.g. XRPUSDT)")
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group.add_argument("--symbols", type=str, help="멀티심볼, 콤마 구분 (e.g. XRPUSDT,TRXUSDT,DOGEUSDT)")
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p.add_argument("--start", type=str, default=None, help="시작일 (e.g. 2025-06-01)")
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p.add_argument("--end", type=str, default=None, help="종료일 (e.g. 2026-03-01)")
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p.add_argument("--balance", type=float, default=1000.0, help="초기 잔고 (기본: 1000)")
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p.add_argument("--leverage", type=int, default=10, help="레버리지 (기본: 10)")
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p.add_argument("--fee", type=float, default=0.04, help="taker 수수료 %% (기본: 0.04)")
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p.add_argument("--slippage", type=float, default=0.01, help="슬리피지 %% (기본: 0.01)")
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p.add_argument("--no-ml", action="store_true", help="ML 필터 비활성화")
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p.add_argument("--ml-threshold", type=float, default=0.55, help="ML 임계값 (기본: 0.55)")
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# Strategy params
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p.add_argument("--sl-atr", type=float, default=1.5, help="SL ATR 배수 (기본: 1.5)")
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p.add_argument("--tp-atr", type=float, default=3.0, help="TP ATR 배수 (기본: 3.0)")
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p.add_argument("--signal-threshold", type=int, default=3, help="신호 임계값 (기본: 3)")
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p.add_argument("--adx-threshold", type=float, default=0, help="ADX 필터 (0=비활성화, 기본: 0)")
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p.add_argument("--vol-multiplier", type=float, default=1.5, help="거래량 급증 배수 (기본: 1.5)")
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# Walk-Forward
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p.add_argument("--walk-forward", action="store_true", help="Walk-Forward 백테스트 (기간별 모델 학습/검증)")
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p.add_argument("--train-months", type=int, default=6, help="WF 학습 윈도우 개월 (기본: 6)")
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p.add_argument("--test-months", type=int, default=1, help="WF 검증 윈도우 개월 (기본: 1)")
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return p.parse_args()
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def print_summary(summary: dict, cfg, mode: str = "standard"):
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print("\n" + "=" * 60)
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title = "WALK-FORWARD BACKTEST RESULT" if mode == "walk_forward" else "BACKTEST RESULT"
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print(f" {title}")
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print("=" * 60)
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print(f" 심볼: {', '.join(cfg.symbols)}")
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print(f" 기간: {cfg.start or '전체'} ~ {cfg.end or '전체'}")
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print(f" 초기 잔고: {cfg.initial_balance:,.2f} USDT")
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print(f" 레버리지: {cfg.leverage}x")
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print(f" 수수료: {cfg.fee_pct}% | 슬리피지: {cfg.slippage_pct}%")
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if mode == "walk_forward":
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print(f" 학습/검증: {cfg.train_months}개월 / {cfg.test_months}개월")
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else:
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print(f" ML 필터: {'OFF' if not cfg.use_ml else f'ON (threshold={cfg.ml_threshold})'}")
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print("-" * 60)
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print(f" 총 거래: {summary['total_trades']}건")
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print(f" 총 PnL: {summary['total_pnl']:+,.4f} USDT")
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print(f" 수익률: {summary['return_pct']:+.2f}%")
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print(f" 승률: {summary['win_rate']:.1f}%")
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print(f" 평균 수익: {summary['avg_win']:+.4f} USDT")
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print(f" 평균 손실: {summary['avg_loss']:+.4f} USDT")
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pf = summary['profit_factor']
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pf_str = f"{pf:.2f}" if pf != float("inf") else "INF"
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print(f" Profit Factor: {pf_str}")
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print(f" 최대 낙폭: {summary['max_drawdown_pct']:.2f}%")
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print(f" 샤프비율: {summary['sharpe_ratio']:.2f}")
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print(f" 총 수수료: {summary['total_fees']:,.4f} USDT")
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print("-" * 60)
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print(" 청산 사유:")
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for reason, count in summary.get("close_reasons", {}).items():
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pct = count / summary["total_trades"] * 100 if summary["total_trades"] > 0 else 0
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print(f" {reason:20s} {count:4d}건 ({pct:.1f}%)")
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print("=" * 60)
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def print_fold_table(folds: list[dict]):
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print("\n" + "=" * 90)
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print(" FOLD DETAILS")
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print("=" * 90)
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print(f" {'Fold':>4} {'Test Period':>25} {'Trades':>6} {'PnL':>10} {'WinRate':>7} {'PF':>6} {'MDD':>6}")
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print("-" * 90)
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for f in folds:
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s = f["summary"]
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pf = s["profit_factor"]
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pf_str = f"{pf:.2f}" if pf != float("inf") else "INF"
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print(f" {f['fold']:>4} {f['test_period']:>25} {s['total_trades']:>6} "
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f"{s['total_pnl']:>+10.2f} {s['win_rate']:>6.1f}% {pf_str:>6} {s['max_drawdown_pct']:>5.1f}%")
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print("=" * 90)
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def save_result(result: dict, cfg):
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ts = datetime.now().strftime("%Y%m%d_%H%M%S")
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mode = result.get("mode", "standard")
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prefix = "wf_backtest" if mode == "walk_forward" else "backtest"
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for sym in cfg.symbols:
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out_dir = Path(f"results/{sym.lower()}")
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out_dir.mkdir(parents=True, exist_ok=True)
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path = out_dir / f"{prefix}_{ts}.json"
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if len(cfg.symbols) > 1:
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out_dir = Path("results/combined")
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out_dir.mkdir(parents=True, exist_ok=True)
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path = out_dir / f"{prefix}_{ts}.json"
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def sanitize(obj):
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if isinstance(obj, bool):
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return obj
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if isinstance(obj, (int, float)):
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if isinstance(obj, float):
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if obj == float("inf"):
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return "Infinity"
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if obj == float("-inf"):
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return "-Infinity"
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return obj
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if isinstance(obj, dict):
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return {k: sanitize(v) for k, v in obj.items()}
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if isinstance(obj, list):
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return [sanitize(v) for v in obj]
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if isinstance(obj, (np.integer,)):
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return int(obj)
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if isinstance(obj, (np.floating,)):
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return float(obj)
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if isinstance(obj, np.bool_):
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return bool(obj)
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return obj
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with open(path, "w") as f:
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json.dump(sanitize(result), f, indent=2, ensure_ascii=False)
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print(f"결과 저장: {path}")
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return path
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def main():
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args = parse_args()
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if args.symbol:
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symbols = [args.symbol.upper()]
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else:
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symbols = [s.strip().upper() for s in args.symbols.split(",") if s.strip()]
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if args.walk_forward:
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cfg = WalkForwardConfig(
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symbols=symbols,
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start=args.start,
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end=args.end,
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initial_balance=args.balance,
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leverage=args.leverage,
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fee_pct=args.fee,
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slippage_pct=args.slippage,
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use_ml=not args.no_ml,
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ml_threshold=args.ml_threshold,
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atr_sl_mult=args.sl_atr,
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atr_tp_mult=args.tp_atr,
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signal_threshold=args.signal_threshold,
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adx_threshold=args.adx_threshold,
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volume_multiplier=args.vol_multiplier,
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train_months=args.train_months,
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test_months=args.test_months,
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)
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logger.info(f"Walk-Forward 백테스트 시작: {', '.join(symbols)} "
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f"(학습 {cfg.train_months}개월, 검증 {cfg.test_months}개월)")
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wf = WalkForwardBacktester(cfg)
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result = wf.run()
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print_summary(result["summary"], cfg, mode="walk_forward")
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if result.get("folds"):
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print_fold_table(result["folds"])
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save_result(result, cfg)
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else:
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cfg = BacktestConfig(
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symbols=symbols,
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start=args.start,
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end=args.end,
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initial_balance=args.balance,
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leverage=args.leverage,
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fee_pct=args.fee,
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slippage_pct=args.slippage,
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use_ml=not args.no_ml,
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ml_threshold=args.ml_threshold,
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atr_sl_mult=args.sl_atr,
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atr_tp_mult=args.tp_atr,
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signal_threshold=args.signal_threshold,
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adx_threshold=args.adx_threshold,
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volume_multiplier=args.vol_multiplier,
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)
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logger.info(f"백테스트 시작: {', '.join(symbols)}")
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bt = Backtester(cfg)
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result = bt.run()
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print_summary(result["summary"], cfg)
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save_result(result, cfg)
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if __name__ == "__main__":
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main()
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317
scripts/strategy_sweep.py
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scripts/strategy_sweep.py
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#!/usr/bin/env python3
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"""
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전략 파라미터 스윕: 기존 백테스터를 활용하여 파라미터 조합별 성능을 비교한다.
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ML 필터 OFF 상태에서 순수 전략 성능만 측정한다.
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사용법:
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python scripts/strategy_sweep.py --symbol XRPUSDT
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python scripts/strategy_sweep.py --symbol XRPUSDT --train-months 3 --test-months 1
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python scripts/strategy_sweep.py --symbols XRPUSDT,TRXUSDT,DOGEUSDT
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python scripts/strategy_sweep.py --symbols XRPUSDT,TRXUSDT,DOGEUSDT --combined
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"""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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import argparse
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import json
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import itertools
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from datetime import datetime
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import numpy as np
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from loguru import logger
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from src.backtester import Backtester, BacktestConfig, WalkForwardBacktester, WalkForwardConfig
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# ── 스윕 파라미터 정의 ────────────────────────────────────────────────
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PARAM_GRID = {
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"atr_sl_mult": [1.0, 1.5, 2.0],
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"atr_tp_mult": [2.0, 3.0, 4.0],
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"signal_threshold": [3, 4, 5],
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"adx_threshold": [0, 20, 25, 30],
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"volume_multiplier": [1.5, 2.0, 2.5],
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}
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# 현재 프로덕션 파라미터
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CURRENT_PARAMS = {
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"atr_sl_mult": 2.0,
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"atr_tp_mult": 2.0,
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"signal_threshold": 3,
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"adx_threshold": 25,
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"volume_multiplier": 2.5,
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}
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EMPTY_SUMMARY = {
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"total_trades": 0, "total_pnl": 0, "return_pct": 0, "win_rate": 0,
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"avg_win": 0, "avg_loss": 0, "profit_factor": 0,
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"max_drawdown_pct": 0, "sharpe_ratio": 0, "total_fees": 0, "close_reasons": {},
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}
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def generate_combinations(grid: dict) -> list[dict]:
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keys = list(grid.keys())
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values = list(grid.values())
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combos = []
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for combo in itertools.product(*values):
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combos.append(dict(zip(keys, combo)))
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return combos
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def run_single_backtest(symbols: list[str], params: dict, train_months: int, test_months: int) -> dict:
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"""단일 파라미터 조합으로 walk-forward 백테스트 실행."""
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cfg = WalkForwardConfig(
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symbols=symbols,
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use_ml=False,
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train_months=train_months,
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test_months=test_months,
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atr_sl_mult=params["atr_sl_mult"],
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atr_tp_mult=params["atr_tp_mult"],
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signal_threshold=params["signal_threshold"],
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adx_threshold=params["adx_threshold"],
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volume_multiplier=params["volume_multiplier"],
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)
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wf = WalkForwardBacktester(cfg)
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result = wf.run()
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return result["summary"]
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def run_combined_backtest(symbols: list[str], params: dict, train_months: int, test_months: int) -> dict:
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"""심볼별 독립 walk-forward 실행 후 합산 결과 반환."""
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per_symbol = {}
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total_gross_profit = 0.0
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total_gross_loss = 0.0
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total_trades = 0
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total_pnl = 0.0
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for sym in symbols:
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try:
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summary = run_single_backtest([sym], params, train_months, test_months)
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except Exception as e:
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logger.warning(f" {sym} 실패: {e}")
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summary = EMPTY_SUMMARY.copy()
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per_symbol[sym] = summary
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# gross profit/loss 역산
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n = summary["total_trades"]
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if n > 0:
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wr = summary["win_rate"] / 100.0
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n_wins = round(wr * n)
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n_losses = n - n_wins
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gp = summary["avg_win"] * n_wins if n_wins > 0 else 0.0
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gl = abs(summary["avg_loss"]) * n_losses if n_losses > 0 else 0.0
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total_gross_profit += gp
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total_gross_loss += gl
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total_trades += n
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total_pnl += summary["total_pnl"]
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combined_pf = (total_gross_profit / total_gross_loss) if total_gross_loss > 0 else float("inf")
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return {
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"params": params,
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"combined_pf": round(combined_pf, 2),
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"combined_trades": total_trades,
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"combined_pnl": round(total_pnl, 2),
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"per_symbol": per_symbol,
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}
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def print_results_table(results: list[dict], symbols: list[str], train_months: int, test_months: int):
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sym_str = ",".join(symbols)
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print(f"\n{'=' * 100}")
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print(f" Strategy Parameter Sweep Results ({sym_str}, Walk-Forward {train_months}/{test_months})")
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print(f"{'=' * 100}")
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print(f" {'Rank':>4} {'SL×ATR':>6} {'TP×ATR':>6} {'Signal':>6} {'ADX':>4} {'Vol':>4} "
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f"{'Trades':>6} {'WinRate':>7} {'PF':>6} {'MDD':>5} {'PnL':>10} {'Sharpe':>6}")
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print(f" {'-' * 94}")
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for i, r in enumerate(results):
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p = r["params"]
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s = r["summary"]
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pf = s["profit_factor"]
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pf_str = f"{pf:.2f}" if pf != float("inf") else "INF"
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is_current = all(p[k] == CURRENT_PARAMS[k] for k in CURRENT_PARAMS)
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marker = " ← CURRENT" if is_current else ""
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print(f" {i+1:>4} {p['atr_sl_mult']:>6.1f} {p['atr_tp_mult']:>6.1f} "
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f"{p['signal_threshold']:>6} {p['adx_threshold']:>4.0f} {p['volume_multiplier']:>4.1f} "
|
||||
f"{s['total_trades']:>6} {s['win_rate']:>6.1f}% {pf_str:>6} {s['max_drawdown_pct']:>4.1f}% "
|
||||
f"{s['total_pnl']:>+10.2f} {s['sharpe_ratio']:>6.1f}{marker}")
|
||||
|
||||
print(f"{'=' * 100}")
|
||||
|
||||
|
||||
def print_combined_results_table(results: list[dict], symbols: list[str],
|
||||
train_months: int, test_months: int,
|
||||
min_pf_count: int = 2, min_pf: float = 0.9):
|
||||
sym_str = ",".join(symbols)
|
||||
# 심볼 약칭
|
||||
short = {s: s.replace("USDT", "") for s in symbols}
|
||||
|
||||
print(f"\n{'=' * 130}")
|
||||
print(f" Combined Strategy Sweep ({sym_str}, WF {train_months}/{test_months})")
|
||||
print(f" Filter: {min_pf_count}+ symbols with PF >= {min_pf}")
|
||||
print(f"{'=' * 130}")
|
||||
|
||||
# 헤더
|
||||
sym_headers = " ".join(f"{short[s]:>12s}" for s in symbols)
|
||||
print(f" {'Rank':>4} {'SL':>4} {'TP':>4} {'Sig':>3} {'ADX':>3} {'Vol':>4} "
|
||||
f"{'Tot':>4} {'CombPF':>6} {'PnL':>9} {sym_headers}")
|
||||
|
||||
# 심볼별 서브헤더
|
||||
sub = " ".join(f"{'PF/WR%/Trd':>12s}" for _ in symbols)
|
||||
print(f" {'':>4} {'':>4} {'':>4} {'':>3} {'':>3} {'':>4} "
|
||||
f"{'':>4} {'':>6} {'':>9} {sub}")
|
||||
print(f" {'-' * 124}")
|
||||
|
||||
for i, r in enumerate(results):
|
||||
p = r["params"]
|
||||
cpf = r["combined_pf"]
|
||||
cpf_str = f"{cpf:.2f}" if cpf != float("inf") else "INF"
|
||||
|
||||
is_current = all(p[k] == CURRENT_PARAMS[k] for k in CURRENT_PARAMS)
|
||||
marker = " ←CUR" if is_current else ""
|
||||
|
||||
# 심볼별 PF/WR/Trades
|
||||
sym_cols = []
|
||||
for s in symbols:
|
||||
ss = r["per_symbol"][s]
|
||||
spf = ss["profit_factor"]
|
||||
spf_str = f"{spf:.1f}" if spf != float("inf") else "INF"
|
||||
sym_cols.append(f"{spf_str}/{ss['win_rate']:.0f}%/{ss['total_trades']}")
|
||||
|
||||
sym_detail = " ".join(f"{c:>12s}" for c in sym_cols)
|
||||
|
||||
print(f" {i+1:>4} {p['atr_sl_mult']:>4.1f} {p['atr_tp_mult']:>4.1f} "
|
||||
f"{p['signal_threshold']:>3} {p['adx_threshold']:>3.0f} {p['volume_multiplier']:>4.1f} "
|
||||
f"{r['combined_trades']:>4} {cpf_str:>6} {r['combined_pnl']:>+9.1f} "
|
||||
f"{sym_detail}{marker}")
|
||||
|
||||
print(f"{'=' * 130}")
|
||||
print(f" 표시된 조합: {len(results)}개 / 전체 324개")
|
||||
print(f" 심볼별 칼럼: PF/승률%/거래수")
|
||||
|
||||
|
||||
def save_results(results: list[dict], symbols: list[str]):
|
||||
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||||
for sym in symbols:
|
||||
out_dir = Path(f"results/{sym.lower()}")
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
path = out_dir / f"strategy_sweep_{ts}.json"
|
||||
|
||||
if len(symbols) > 1:
|
||||
out_dir = Path("results/combined")
|
||||
out_dir.mkdir(parents=True, exist_ok=True)
|
||||
path = out_dir / f"strategy_sweep_{ts}.json"
|
||||
|
||||
def sanitize(obj):
|
||||
if isinstance(obj, bool):
|
||||
return obj
|
||||
if isinstance(obj, (np.integer,)):
|
||||
return int(obj)
|
||||
if isinstance(obj, (np.floating,)):
|
||||
return float(obj)
|
||||
if isinstance(obj, float) and obj == float("inf"):
|
||||
return "Infinity"
|
||||
if isinstance(obj, dict):
|
||||
return {k: sanitize(v) for k, v in obj.items()}
|
||||
if isinstance(obj, list):
|
||||
return [sanitize(v) for v in obj]
|
||||
return obj
|
||||
|
||||
with open(path, "w") as f:
|
||||
json.dump(sanitize(results), f, indent=2, ensure_ascii=False)
|
||||
print(f"결과 저장: {path}")
|
||||
|
||||
|
||||
def main():
|
||||
p = argparse.ArgumentParser(description="Strategy Parameter Sweep")
|
||||
group = p.add_mutually_exclusive_group(required=True)
|
||||
group.add_argument("--symbol", type=str)
|
||||
group.add_argument("--symbols", type=str)
|
||||
p.add_argument("--train-months", type=int, default=3)
|
||||
p.add_argument("--test-months", type=int, default=1)
|
||||
p.add_argument("--combined", action="store_true",
|
||||
help="심볼별 독립 실행 후 합산 PF 기준 정렬 (--symbols 필수)")
|
||||
p.add_argument("--min-pf", type=float, default=0.9,
|
||||
help="심볼별 최소 PF 필터 (기본: 0.9)")
|
||||
p.add_argument("--min-pf-count", type=int, default=2,
|
||||
help="최소 PF 충족 심볼 수 (기본: 2)")
|
||||
args = p.parse_args()
|
||||
|
||||
symbols = [args.symbol.upper()] if args.symbol else [s.strip().upper() for s in args.symbols.split(",")]
|
||||
|
||||
if args.combined:
|
||||
if len(symbols) < 2:
|
||||
logger.error("--combined 모드는 --symbols에 2개 이상 심볼 필요")
|
||||
sys.exit(1)
|
||||
run_combined_sweep(symbols, args)
|
||||
else:
|
||||
run_single_sweep(symbols, args)
|
||||
|
||||
|
||||
def run_single_sweep(symbols: list[str], args):
|
||||
combos = generate_combinations(PARAM_GRID)
|
||||
logger.info(f"스윕 시작: {len(combos)}개 조합, 심볼={','.join(symbols)}")
|
||||
|
||||
results = []
|
||||
for i, params in enumerate(combos):
|
||||
param_str = " | ".join(f"{k}={v}" for k, v in params.items())
|
||||
logger.info(f" [{i+1}/{len(combos)}] {param_str}")
|
||||
|
||||
try:
|
||||
summary = run_single_backtest(symbols, params, args.train_months, args.test_months)
|
||||
results.append({"params": params, "summary": summary})
|
||||
except Exception as e:
|
||||
logger.warning(f" 실패: {e}")
|
||||
results.append({"params": params, "summary": EMPTY_SUMMARY.copy()})
|
||||
|
||||
# PF 기준 내림차순 정렬
|
||||
def sort_key(r):
|
||||
pf = r["summary"]["profit_factor"]
|
||||
return pf if pf != float("inf") else 999
|
||||
results.sort(key=sort_key, reverse=True)
|
||||
|
||||
print_results_table(results, symbols, args.train_months, args.test_months)
|
||||
save_results(results, symbols)
|
||||
|
||||
|
||||
def run_combined_sweep(symbols: list[str], args):
|
||||
combos = generate_combinations(PARAM_GRID)
|
||||
total_runs = len(combos) * len(symbols)
|
||||
logger.info(f"합산 스윕 시작: {len(combos)}개 조합 × {len(symbols)}심볼 = {total_runs}회")
|
||||
|
||||
results = []
|
||||
for i, params in enumerate(combos):
|
||||
param_str = " | ".join(f"{k}={v}" for k, v in params.items())
|
||||
logger.info(f" [{i+1}/{len(combos)}] {param_str}")
|
||||
|
||||
r = run_combined_backtest(symbols, params, args.train_months, args.test_months)
|
||||
results.append(r)
|
||||
|
||||
# 필터: N개 이상 심볼에서 PF >= min_pf
|
||||
filtered = []
|
||||
for r in results:
|
||||
pf_pass = sum(
|
||||
1 for s in symbols
|
||||
if r["per_symbol"][s]["profit_factor"] >= args.min_pf
|
||||
and r["per_symbol"][s]["total_trades"] > 0
|
||||
)
|
||||
if pf_pass >= args.min_pf_count:
|
||||
filtered.append(r)
|
||||
|
||||
# 합산 PF 기준 정렬
|
||||
def sort_key(r):
|
||||
pf = r["combined_pf"]
|
||||
return pf if pf != float("inf") else 999
|
||||
filtered.sort(key=sort_key, reverse=True)
|
||||
|
||||
print_combined_results_table(filtered, symbols, args.train_months, args.test_months,
|
||||
min_pf_count=args.min_pf_count, min_pf=args.min_pf)
|
||||
save_results(filtered, symbols)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user