dsyoon
7276001700
매수만 건당 상한(2,000만 원)을 적용하고 초기 자본을 100만 원으로 올린 뒤, 현실적인 체결 규모로 spot·futures sim v1~v3를 재생성한다. Co-authored-by: Cursor <cursoragent@cursor.com>
250 lines
9.1 KiB
Python
250 lines
9.1 KiB
Python
#!/usr/bin/env python3
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"""3단계: 인과 전략 선물 시뮬 + 0단계 벤치마크 수익률 비교."""
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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.data.candle_loader import load_candles
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from deepcoin.data.intervals import interval_label
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from deepcoin.evaluation.gt_align import align_with_ground_truth
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from deepcoin.ground_truth.futures_chart import render_futures_sim_chart
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from deepcoin.ground_truth.futures_pnl import simulate_futures_gt_signals_pnl
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from deepcoin.strategy.causal_v3 import build_causal_v3_result
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from deepcoin.strategy.futures_sim import compare_futures_sims, simulate_causal_futures
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from deepcoin.strategy.report import (
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build_causal_futures_report,
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render_causal_futures_html,
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save_causal_futures_report,
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)
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from deepcoin.techniques.base import TechniqueParams
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from deepcoin.techniques.legs import signals_to_legs
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from deepcoin.techniques.runner import load_ground_truth
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def _configure_logging(verbose: bool) -> None:
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"""로깅 레벨을 설정한다."""
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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 _resolve_gt_path(settings, gt_file: str | None) -> Path:
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"""GT JSON 경로를 결정한다."""
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if gt_file:
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path = Path(gt_file)
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if not path.is_absolute():
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path = ROOT / path
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return path
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return settings.ground_truth_file
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def main() -> int:
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"""CLI 진입점."""
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parser = argparse.ArgumentParser(description="3단계: 인과 선물 시뮬 (0단계 대비)")
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parser.add_argument("--gt-file", type=str, default=None, help="v3 GT JSON 경로")
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parser.add_argument("--sim-days", type=int, default=None, help="선물 시뮬 기간(일)")
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parser.add_argument("--no-atr", action="store_true", help="ATR 손절 비활성화")
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parser.add_argument("--atr-period", type=int, default=None, help="ATR 기간")
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parser.add_argument("--atr-mult", type=float, default=None, help="ATR 손절 배수")
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parser.add_argument("--min-score", type=float, default=None, help="composite 최소 점수")
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parser.add_argument(
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"--min-bars",
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type=int,
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default=None,
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help="동일 방향 신호 최소 봉 간격 (기본 1440=3분봉 3일)",
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)
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parser.add_argument("--tolerance", type=int, default=None, help="GT 정합 허용 봉 수")
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parser.add_argument("--no-chart", action="store_true", help="sim 차트 생략")
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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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gt_path = _resolve_gt_path(settings, args.gt_file)
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if not gt_path.exists():
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logging.error("Ground Truth 파일 없음: %s", gt_path)
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return 1
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gt_result = load_ground_truth(gt_path)
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sim_days = args.sim_days or settings.gt_sim_lookback_days
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atr_period = args.atr_period or settings.strategy_atr_period
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atr_mult = args.atr_mult or settings.strategy_atr_mult
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min_score = args.min_score or settings.strategy_min_score
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min_bars = args.min_bars if args.min_bars is not None else settings.strategy_min_bars_between
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tolerance = args.tolerance or settings.gt_align_tolerance_bars
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use_atr = not args.no_atr
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params = TechniqueParams(
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interval_min=settings.gt_interval_min,
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lookback_days=settings.gt_lookback_days,
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min_leg_pct=settings.gt_min_leg_pct,
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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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extra={
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"reversal_pct": settings.gt_zigzag_reversal_pct,
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"min_score": min_score,
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},
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)
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logging.info(
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"인과 선물 전략: %s %s, %s일, ATR=%s×%s, min_score=%s",
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settings.symbol,
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interval_label(params.interval_min),
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params.lookback_days,
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atr_period,
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atr_mult,
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min_score,
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)
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df = load_candles(
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db_path=settings.db_path,
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symbol=settings.symbol,
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interval_min=params.interval_min,
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lookback_days=params.lookback_days,
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)
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last_close = float(df["close"].iloc[-1])
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causal_result = build_causal_v3_result(
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df, params, settings.symbol, min_bars_between=min_bars
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)
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settings.causal_dir.mkdir(parents=True, exist_ok=True)
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causal_json_path = settings.causal_dir / "causal_v3_signals.json"
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with causal_json_path.open("w", encoding="utf-8") as fp:
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json.dump(causal_result, fp, ensure_ascii=False, indent=2)
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from deepcoin.strategy.causal_v3 import _signals_dicts_to_technique, run_causal_v3_strategy
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technique_signals = _signals_dicts_to_technique(
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run_causal_v3_strategy(df, params),
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causal_result.get("signals") or [],
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)
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technique_legs = signals_to_legs(technique_signals, min_leg_pct=params.min_leg_pct)
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alignment = align_with_ground_truth(
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gt_result=gt_result,
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technique_signals=[s.to_dict() for s in technique_signals],
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technique_legs=technique_legs,
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tolerance_bars=tolerance,
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)
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gt_sim = simulate_futures_gt_signals_pnl(
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signals=gt_result.get("signals") or [],
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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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max_buy_order_krw=settings.gt_max_buy_order_krw,
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sim_lookback_days=sim_days,
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data_end=gt_result["meta"]["data_to"],
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last_mark_price=last_close,
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)
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causal_sim = simulate_causal_futures(
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df=df,
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causal_result=causal_result,
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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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max_buy_order_krw=settings.gt_max_buy_order_krw,
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sim_lookback_days=sim_days,
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atr_period=atr_period,
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atr_mult=atr_mult,
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use_atr_stops=use_atr,
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)
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causal_sim_no_stops = None
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if use_atr:
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causal_sim_no_stops = simulate_causal_futures(
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df=df,
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causal_result=causal_result,
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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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max_buy_order_krw=settings.gt_max_buy_order_krw,
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sim_lookback_days=sim_days,
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atr_period=atr_period,
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atr_mult=atr_mult,
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use_atr_stops=False,
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)
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comparison = compare_futures_sims(gt_sim, causal_sim, causal_sim_no_stops)
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report = build_causal_futures_report(
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causal_result=causal_result,
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gt_result=gt_result,
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alignment=alignment,
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gt_sim=gt_sim,
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causal_sim=causal_sim,
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comparison=comparison,
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causal_sim_no_stops=causal_sim_no_stops,
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)
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report_json = save_causal_futures_report(report, settings.causal_futures_report_json)
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report_html = render_causal_futures_html(report, settings.causal_futures_report_html)
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chart_path = None
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if not args.no_chart:
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chart_path = render_futures_sim_chart(
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db_path=settings.db_path,
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symbol=settings.symbol,
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gt_result=causal_result,
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sim_pnl=causal_sim,
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output_path=settings.causal_futures_chart_html,
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chart_lookback_days=settings.download_days,
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)
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summary = causal_result.get("summary", {})
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align_buy = alignment.get("buy", {})
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align_sell = alignment.get("sell", {})
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print("\n=== 3단계 인과 선물 시뮬 (composite_v3) ===")
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print(
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f"신호: 매수 {summary.get('buy_count', 0)} / 매도 {summary.get('sell_count', 0)} / "
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f"레그 {summary.get('leg_count', 0)}"
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)
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print(
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f"GT 정합: buy {align_buy.get('recall', 0)*100:.0f}% / "
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f"sell {align_sell.get('recall', 0)*100:.0f}% / "
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f"score {alignment.get('score', 0)*100:.1f}"
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)
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print(f"\n=== 0단계 vs 3단계 선물 sim ({sim_days}일) ===")
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print(
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f"0단계 GT 벤치마크: {gt_sim['total_return_pct']:+.2f}% "
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f"({gt_sim['final_equity_krw']:,.0f}원)"
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)
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if causal_sim_no_stops:
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print(
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f"인과 (손절 없음): {causal_sim_no_stops['total_return_pct']:+.2f}% "
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f"({causal_sim_no_stops['final_equity_krw']:,.0f}원)"
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)
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print(
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f"인과 (ATR {atr_period}×{atr_mult}): {causal_sim['total_return_pct']:+.2f}% "
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f"({causal_sim['final_equity_krw']:,.0f}원) | "
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f"손절 {causal_sim.get('atr_stop_signals_in_period', 0)}건"
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)
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print(
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f"체결 L↑{causal_sim['long_opens_executed']}/L↓{causal_sim['long_closes_executed']} · "
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f"S↓{causal_sim['short_opens_executed']}/S↑{causal_sim['short_closes_executed']}"
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)
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print(f"수익 포착률 (GT 대비): {comparison.get('return_capture_ratio', 0)*100:.1f}%")
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print(f"인과 신호 JSON: {causal_json_path}")
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print(f"리포트 JSON: {report_json}")
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print(f"리포트 HTML: {report_html}")
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if chart_path:
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print(f"sim 차트: {chart_path}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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