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>
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scripts/run_backtest.py
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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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