2783826a03
운영 백테스트(+1,873,140%)과 live/paper 체결 규칙을 맞추고, 캔들 증분 sync· tail 신호 갱신·일일 체결 상한·슬리피지를 반영한다. docs/live 차트 생성 스크립트와 .env.example·README를 갱신한다. Co-authored-by: Cursor <cursoragent@cursor.com>
107 lines
3.8 KiB
Python
107 lines
3.8 KiB
Python
#!/usr/bin/env python3
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"""fractal_swing 현실적 백테스트 — 슬리피지·일일체결 상한 시나리오."""
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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 logging
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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SRC = ROOT / "src"
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if str(SRC) not in sys.path:
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sys.path.insert(0, str(SRC))
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from deepcoin.config import load_settings
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from deepcoin.evaluation.causal_sim import normalize_signals_for_sim
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from deepcoin.ground_truth.pnl import simulate_gt_signals_pnl
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from deepcoin.operations.signal_pipeline import (
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_signals_in_lookback,
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generate_raw_signals,
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load_ops_candles,
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)
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def _configure_logging(verbose: bool) -> None:
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level = logging.DEBUG if verbose else logging.INFO
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logging.basicConfig(
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level=level,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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def main() -> int:
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"""fractal_swing 슬리피지 시나리오 백테스트."""
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parser = argparse.ArgumentParser(description="fractal_swing 현실적 3년 백테스트")
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parser.add_argument("-v", "--verbose", action="store_true")
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args = parser.parse_args()
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_configure_logging(args.verbose)
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settings = load_settings()
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df = load_ops_candles(settings)
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gen = generate_raw_signals(settings, df=df, use_cache=True)
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end = gen["data_end"]
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price = gen["last_price"]
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scoped = _signals_in_lookback(gen["raw_signals"], end, settings.gt_sim_lookback_days)
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normalized = normalize_signals_for_sim(scoped)
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scenarios = [
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{"label": "stage2_ideal", "slippage_rate": 0.0, "daily_max_trades": None},
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{"label": "ops_default", "slippage_rate": settings.ops_slippage_rate, "daily_max_trades": settings.ops_daily_max_trades},
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{"label": "slippage_0.1pct", "slippage_rate": 0.001, "daily_max_trades": settings.ops_daily_max_trades},
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{"label": "slippage_0.1pct_no_cap", "slippage_rate": 0.001, "daily_max_trades": None},
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]
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rows = []
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for sc in scenarios:
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sim = simulate_gt_signals_pnl(
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signals=normalized,
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initial_cash_krw=settings.gt_initial_cash_krw,
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fee_rate=settings.gt_trading_fee_rate,
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min_order_krw=settings.ops_min_order_krw,
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slippage_rate=sc["slippage_rate"],
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daily_max_trades=sc["daily_max_trades"],
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sim_lookback_days=settings.gt_sim_lookback_days,
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data_end=end,
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last_mark_price=price,
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)
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buys = sim["buys_executed"]
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rows.append({
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**sc,
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"return_pct": sim["total_return_pct"],
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"final_equity_krw": sim["final_equity_krw"],
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"buys_executed": buys,
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"sells_executed": sim["sells_executed"],
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"buys_skipped": sim["buys_skipped"],
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"daily_buys_avg": round(buys / settings.gt_sim_lookback_days, 2),
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})
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report = {
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"technique_id": settings.ops_technique_id,
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"symbol": settings.symbol,
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"sim_lookback_days": settings.gt_sim_lookback_days,
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"initial_cash_krw": settings.gt_initial_cash_krw,
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"signals_in_period": len(scoped),
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"scenarios": rows,
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}
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out = Path("docs/spot/3_operations/fractal_realistic_backtest.json")
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out.parent.mkdir(parents=True, exist_ok=True)
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out.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
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print("\n=== fractal_swing 현실적 백테스트 (3년) ===")
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for row in rows:
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print(
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f"{row['label']}: {row['return_pct']}% "
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f"(슬리피지 {row['slippage_rate']}, 일상한 {row['daily_max_trades']}) "
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f"매수 {row['buys_executed']} (일 {row['daily_buys_avg']})"
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)
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print(f"\nJSON: {out}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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