kjh2064
ee3e799de1
주요 변경: - tools/build_rebalance_engine_v1.py: REBALANCE_ENGINE_V1 신규 * account_snapshot 직접 합산(_build_snap_position_map) → 소수주 분리 행 병합 * 레짐 소스 macro.REGIME_PRELIM 최우선 (GAS 와 동일) - src/gas_adapter_parts/gdf_06_rebalance.gs: runRebalanceSheet_() 신규 * Logger.log / getSpreadsheet_() 로 run_all 연동 수정 - src/gas_adapter_parts/gdc_01_fetch_fundamentals.gs * _mergePositionRecord_(): 소수주 중복 행 합산 신규 * parseInt → parseFloat (qty, availQty) - src/gas_adapter_parts/gdf_01_price_metrics.gs * 미보유 종목 SELL_READY → WATCH_EXIT_SIGNAL - spec/41_release_dag.yaml: build_rebalance_sheet 노드 추가 (step_count 63) - spec/51_formula_lifecycle_registry.yaml: REBALANCE_ENGINE_V1 등록 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
300 lines
13 KiB
Python
300 lines
13 KiB
Python
"""build_realized_performance_v1.py — REALIZED_PERFORMANCE_V1
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기존 데이터로 CAGR / Sharpe / Sortino / MDD / win_rate 를 추정한다.
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데이터 출처:
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proposal_evaluation_history.json
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- records[].next_return_pct (T+1 수익률, n=1,066)
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- records[].t5_return_pct (T+5 수익률, n=711)
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- records[].t20_return_pct (T+20 수익률, n=510, REPLAY)
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GatherTradingData.json (hApex)
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- total_asset_krw (현재 총자산)
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- portfolio_peak_krw (고점)
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정직성 원칙 (AGENTS.md §0.3):
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- REPLAY 기반 지표는 estimated=true, source=REPLAY_FROM_KRX_EOD
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- 실현 이력 없는 지표(CAGR/Sharpe)는 replay 추정 + 주의 문구
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- 1년 이상 실현 손익 이력 없으므로 out_of_sample 비교 불가
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- 미충족 항목은 insufficient_data
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산출물: Temp/realized_performance_v1.json
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"""
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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 math
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import statistics
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from pathlib import Path
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from typing import Any
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ROOT = Path(__file__).resolve().parents[1]
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TEMP = ROOT / "Temp"
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DEFAULT_JSON = ROOT / "GatherTradingData.json"
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DEFAULT_HIST = TEMP / "proposal_evaluation_history.json"
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DEFAULT_OUT = TEMP / "realized_performance_v1.json"
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FORMULA_ID = "REALIZED_PERFORMANCE_V1"
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NA = "not_available"
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INSUF = "insufficient_data"
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RISK_FREE_ANNUAL_PCT = 3.5 # 한국 단기 무위험수익률 추정 (%)
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TRADING_DAYS_PER_YEAR = 252
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def _load(path: Path) -> Any:
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if not path.exists():
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return {}
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try:
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return json.loads(path.read_text(encoding="utf-8"))
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except Exception:
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return {}
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def _f(v: Any, default: float | None = None) -> float | None:
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try:
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return float(v)
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except Exception:
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return default
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def _extract_harness_root(payload: Any) -> dict[str, Any]:
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if not isinstance(payload, dict):
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return {}
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h = payload.get("hApex")
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dc = (payload.get("data") or {}).get("_harness_context")
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if isinstance(h, dict) and isinstance(dc, dict):
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m = dict(dc); m.update(h); return m
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return h if isinstance(h, dict) else dc if isinstance(dc, dict) else payload
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def _safe_stdev(xs: list[float]) -> float | None:
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return round(statistics.stdev(xs), 4) if len(xs) > 1 else None
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def _annualize_return(mean_pct: float, holding_days: int) -> dict[str, Any]:
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"""단기 평균 수익률 연환산 — 개별 포지션 수익률을 연속 복리로 가정 시 수치이므로
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포트폴리오 실제 CAGR과 다르다. 'NOT_MEANINGFUL_FOR_PORTFOLIO' 표기 필수."""
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r = mean_pct / 100.0
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if holding_days <= 0:
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return {"value": "not_available", "note": "holding_days=0"}
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periods_per_year = TRADING_DAYS_PER_YEAR / holding_days
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raw = round(((1 + r) ** periods_per_year - 1) * 100.0, 2)
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return {
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"value_pct": raw,
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"validity": "NOT_MEANINGFUL_FOR_PORTFOLIO",
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"note": (
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f"개별 포지션 {mean_pct:.2f}% / {holding_days}일 → 연환산 = "
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f"(1+{r:.4f})^({TRADING_DAYS_PER_YEAR}/{holding_days})-1. "
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"포트폴리오 CAGR은 전체 계좌 시계열이 필요하며 현재 insufficient_data."
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),
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"estimated": True,
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}
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def _sharpe(mean_pct: float, stdev_pct: float, holding_days: int) -> float | None:
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"""기간 단위 Sharpe → 연환산."""
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if stdev_pct <= 0:
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return None
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rf_period = RISK_FREE_ANNUAL_PCT / (TRADING_DAYS_PER_YEAR / holding_days)
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excess = mean_pct - rf_period
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sharpe_period = excess / stdev_pct
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annualized = sharpe_period * math.sqrt(TRADING_DAYS_PER_YEAR / holding_days)
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return round(annualized, 3)
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def _sortino(returns: list[float], mean_pct: float, holding_days: int) -> float | None:
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"""Sortino: 하방 편차만 사용."""
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rf_period = RISK_FREE_ANNUAL_PCT / (TRADING_DAYS_PER_YEAR / holding_days)
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downside = [r for r in returns if r < rf_period]
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if not downside:
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return None
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downside_dev = math.sqrt(sum((r - rf_period) ** 2 for r in downside) / len(downside))
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if downside_dev <= 0:
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return None
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excess = mean_pct - rf_period
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sortino_period = excess / downside_dev
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return round(sortino_period * math.sqrt(TRADING_DAYS_PER_YEAR / holding_days), 3)
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def _max_drawdown(returns: list[float]) -> dict[str, Any]:
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"""크로스섹션 수익률 분포에서 MDD를 계산하는 것은 부적합.
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포트폴리오 레벨 시계열(계좌 총자산 일별 변화)이 필요하며 현재 insufficient_data."""
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if not returns:
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return {"max_drawdown_pct": INSUF, "note": "수익률 데이터 없음"}
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# 최악 단일 포지션 손실 = 분포에서 MDD 하한 프록시
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worst_single = round(min(returns), 2)
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return {
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"max_drawdown_pct": INSUF,
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"worst_single_position_loss_pct": worst_single,
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"note": (
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"포트폴리오 MDD는 계좌 총자산 일별 시계열 필요(insufficient_data). "
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f"단일 포지션 최대 손실 = {worst_single}%."
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),
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"estimated": True,
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}
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def _win_rate(returns: list[float], threshold_pct: float = 0.0) -> dict[str, Any]:
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if not returns:
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return {"win_rate_pct": INSUF}
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wins = sum(1 for r in returns if r > threshold_pct)
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return {
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"win_rate_pct": round(wins / len(returns) * 100.0, 2),
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"threshold_pct": threshold_pct,
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"n": len(returns),
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}
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def _worst_case_mdd(harness: dict) -> dict[str, Any]:
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"""현재 포지션에서 모든 종목이 손절가에 도달할 경우 MDD 시나리오."""
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total = _f(harness.get("total_asset_krw"))
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heat = _f(harness.get("total_heat_krw"))
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heat_pct = _f(harness.get("total_heat_pct"))
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if total is None or heat is None:
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return {"worst_case_mdd_pct": INSUF, "note": "포트폴리오 데이터 없음"}
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return {
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"worst_case_mdd_pct": round(heat_pct or 0, 2),
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"worst_case_loss_krw": round(heat or 0),
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"total_asset_krw": round(total),
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"note": "현재 포지션 전 손절 시 최대 손실 = total_heat_krw",
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"source": "hApex.total_heat_pct",
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}
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--json", default=str(DEFAULT_JSON))
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ap.add_argument("--hist", default=str(DEFAULT_HIST))
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ap.add_argument("--out", default=str(DEFAULT_OUT))
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args = ap.parse_args()
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json_path = Path(args.json); json_path = json_path if json_path.is_absolute() else ROOT / json_path
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hist_path = Path(args.hist); hist_path = hist_path if hist_path.is_absolute() else ROOT / args.hist
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out_path = Path(args.out); out_path = out_path if out_path.is_absolute() else ROOT / args.out
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payload = _load(json_path)
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harness = _extract_harness_root(payload)
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hist = _load(hist_path)
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records: list[dict] = hist.get("records") or []
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# ── 데이터 분리 ───────────────────────────────────────────────────────────
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replay_records = [r for r in records if isinstance(r, dict) and
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str(r.get("validation_status") or "").upper() == "REPLAY_BACKFILL"]
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op_records = [r for r in records if isinstance(r, dict) and
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str(r.get("validation_status") or "").upper() != "REPLAY_BACKFILL"]
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# T+1 운영 수익률
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t1_op_returns = [_f(r.get("next_return_pct")) for r in op_records
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if _f(r.get("next_return_pct")) is not None]
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# T+5 운영 수익률
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t5_op_returns = [_f(r.get("t5_return_pct")) for r in op_records
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if _f(r.get("t5_return_pct")) is not None]
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# T+20 replay 수익률
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t20_replay_returns = [_f(r.get("t20_return_pct")) for r in replay_records
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if _f(r.get("t20_return_pct")) is not None]
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def _stats_block(returns: list[float], holding_days: int,
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label: str, estimated: bool, source: str) -> dict[str, Any]:
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if not returns:
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return {"status": INSUF, "n": 0, "label": label}
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mean = round(statistics.mean(returns), 3)
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stdev = _safe_stdev(returns)
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cagr = _annualize_return(mean, holding_days)
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sharpe = _sharpe(mean, stdev or 0, holding_days) if stdev else None
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sortino = _sortino(returns, mean, holding_days)
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mdd = _max_drawdown(returns)
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wr = _win_rate(returns)
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return {
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"label": label,
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"n": len(returns),
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"holding_days": holding_days,
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"mean_return_pct": mean,
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"stdev_pct": stdev,
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"cagr_annualized": cagr, # value_pct=NOT_MEANINGFUL_FOR_PORTFOLIO — 개별 포지션 연환산
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"sharpe_ratio_annualized": sharpe,
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"sortino_ratio_annualized": sortino,
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"max_drawdown": mdd,
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"win_rate": wr,
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"estimated": estimated,
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"source": source,
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"risk_free_rate_annual_pct": RISK_FREE_ANNUAL_PCT,
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"methodology": (
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f"mean_return={mean:.2f}% → 연환산 CAGR = (1+{mean/100:.4f})^({TRADING_DAYS_PER_YEAR}/{holding_days}) - 1; "
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f"Sharpe = (mean - rf/{TRADING_DAYS_PER_YEAR}*{holding_days}) / stdev × √({TRADING_DAYS_PER_YEAR}/{holding_days})"
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),
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}
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t1_stats = _stats_block(t1_op_returns, 1, "T+1_operational",
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estimated=True, source="proposal_evaluation_history.operational")
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t5_stats = _stats_block(t5_op_returns, 5, "T+5_operational",
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estimated=True, source="proposal_evaluation_history.operational")
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t20_stats = _stats_block(t20_replay_returns, 20, "T+20_replay",
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estimated=True, source="REPLAY_FROM_KRX_EOD (estimated=true)")
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# ── 현재 포트폴리오 MDD 시나리오 ─────────────────────────────────────────
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peak = _f(harness.get("portfolio_peak_krw"))
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total = _f(harness.get("total_asset_krw"))
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current_dd = {
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"portfolio_peak_krw": peak,
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"portfolio_current_krw": total,
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"current_drawdown_pct": (
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round((peak - total) / peak * 100, 2) if peak and total and peak > 0
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else 0.0
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),
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"worst_case_scenario": _worst_case_mdd(harness),
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}
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# ── 미충족 항목 ───────────────────────────────────────────────────────────
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insufficient = {
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"CAGR_realized_1y": INSUF,
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"sharpe_realized_1y": INSUF,
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"MDD_realized": INSUF,
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"win_rate_realized_closed_trades": INSUF,
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"profit_factor": INSUF,
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"slippage_impact": INSUF,
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"transaction_cost_impact": INSUF,
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"in_sample_vs_oos_gap": INSUF,
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"reason": "1년 이상 청산 완료 거래 이력 없음 — backdata MAE/MFE/pnl 전 행 공란",
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}
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result = {
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"formula_id": FORMULA_ID,
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"as_of_date": str((ROOT / "GatherTradingData.json").stat().st_mtime)[:10] if json_path.exists() else NA,
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"data_quality_note": (
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"모든 통계는 REPLAY 또는 운영 제안 방향 일치율 기반 추정. "
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"청산 완료 실현 손익 이력이 없으므로 CAGR/Sharpe는 estimated=true. "
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"실제 운용 성과와 상이할 수 있음."
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),
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"performance_metrics": {
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"t1_operational": t1_stats,
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"t5_operational": t5_stats,
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"t20_replay_estimated": t20_stats,
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},
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"current_portfolio_mdd": current_dd,
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"insufficient_data_items": insufficient,
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"summary": {
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"best_estimated_cagr": (t20_stats.get("cagr_annualized") or {}).get("value_pct", INSUF),
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"cagr_validity": "NOT_MEANINGFUL_FOR_PORTFOLIO",
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"best_estimated_sharpe": t20_stats.get("sharpe_ratio_annualized", INSUF),
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"best_estimated_source": "T+20 replay (n=510, estimated=true)",
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"current_drawdown_pct": current_dd["current_drawdown_pct"],
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"worst_case_mdd_pct": (current_dd["worst_case_scenario"] or {}).get("worst_case_mdd_pct", INSUF),
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"t5_win_rate_pct": t5_stats.get("win_rate", {}).get("win_rate_pct", INSUF),
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"disclaimer": "spec/29_backtest_harness_contract.yaml 기준 — operational 실측 아님",
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},
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}
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out_path.write_text(json.dumps(result, ensure_ascii=False, indent=2) + "\n", encoding="utf-8")
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s = result["summary"]
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print(
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f"[{FORMULA_ID}] T20_replay_CAGR(est)={s.get('best_estimated_cagr_pct')}% "
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f"Sharpe(est)={s.get('best_estimated_sharpe')} "
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f"MDD_worst={s.get('worst_case_mdd_pct')}% "
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f"T5_win_rate={s.get('t5_win_rate_pct')}% -> {out_path}"
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)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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