feat: 리밸런싱 엔진 V1 + GAS 버그 수정 (2026-06-13)
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>
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from __future__ import annotations
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import argparse
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import json
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from datetime import date, datetime, timedelta
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from pathlib import Path
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from typing import Any
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from pykrx import stock
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ROOT = Path(__file__).resolve().parents[1]
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DEFAULT_JSON = ROOT / "GatherTradingData.json"
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DEFAULT_HISTORY = ROOT / "Temp" / "proposal_evaluation_history.json"
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def _load_json(path: Path) -> dict[str, Any]:
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if not path.exists():
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return {}
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try:
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obj = json.loads(path.read_text(encoding="utf-8"))
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except Exception:
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return {}
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return obj if isinstance(obj, dict) else {}
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def _parse_rows(value: Any) -> list[dict[str, Any]]:
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if isinstance(value, list):
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return [r for r in value if isinstance(r, dict)]
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if isinstance(value, str):
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try:
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parsed = json.loads(value)
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if isinstance(parsed, list):
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return [r for r in parsed if isinstance(r, dict)]
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except Exception:
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return []
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return []
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def _text(v: Any) -> str:
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return str(v or "").strip()
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def _to_num(v: Any) -> float | None:
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try:
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if v is None or v == "":
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return None
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return float(v)
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except Exception:
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return None
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def _expected_direction(action: str, order_type: str) -> str:
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raw = f"{action} {order_type}".upper()
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if "BUY" in raw or "ADD" in raw:
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return "UP"
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if "SELL" in raw or "TRIM" in raw or "EXIT" in raw or "STOP" in raw:
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return "DOWN_OR_RISK_REDUCED"
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if "WATCH" in raw:
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return "NEUTRAL_TO_UP"
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return "NEUTRAL"
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def _classify(ret: float, expected: str, action: str, horizon: str) -> str:
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if horizon == "t1":
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up_pass, up_fail = 0.5, -1.0
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down_pass, down_fail = 0.5, 1.5
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nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -1.5, 3.0, -2.5, 5.0, 1.5
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elif horizon == "t5":
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up_pass, up_fail = 2.0, -3.0
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down_pass, down_fail = 1.0, 4.0
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nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -3.0, 7.0, -6.0, 12.0, 3.0
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else:
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up_pass, up_fail = 5.0, -8.0
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down_pass, down_fail = 2.0, 10.0
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nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -5.0, 15.0, -10.0, 25.0, 6.0
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if expected == "UP":
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if ret >= up_pass:
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return "MATCHED"
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if ret <= up_fail:
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return "MISMATCHED"
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return "INCONCLUSIVE"
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if expected == "DOWN_OR_RISK_REDUCED":
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if ret <= down_pass:
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return "MATCHED"
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if ret >= down_fail:
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return "MISMATCHED"
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return "INCONCLUSIVE"
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if expected == "NEUTRAL_TO_UP":
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if nu_lo <= ret <= nu_hi:
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return "MATCHED"
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if ret <= nu_fail_lo or ret >= nu_fail_hi:
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return "MISMATCHED"
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return "INCONCLUSIVE"
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if abs(ret) <= neut:
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return "MATCHED"
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if abs(ret) >= neut * 2:
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return "MISMATCHED"
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return "INCONCLUSIVE"
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def _summarize(records: list[dict[str, Any]]) -> dict[str, Any]:
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def hsum(status_key: str, outcome_key: str, ret_key: str) -> dict[str, Any]:
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ev = [r for r in records if str(r.get(status_key) or "").startswith("EVALUATED_")]
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m = [r for r in ev if r.get(outcome_key) == "MATCHED"]
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mm = [r for r in ev if r.get(outcome_key) == "MISMATCHED"]
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rets = [r.get(ret_key) for r in ev if isinstance(r.get(ret_key), (int, float))]
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return {
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"evaluated_count": len(ev),
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"matched_count": len(m),
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"mismatched_count": len(mm),
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"match_rate_pct": round((len(m) / len(ev)) * 100, 2) if ev else None,
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"avg_return_pct": round(sum(rets) / len(rets), 2) if rets else None,
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}
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t1 = [r for r in records if r.get("evaluation_status") == "EVALUATED_T1"]
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t1m = [r for r in t1 if r.get("outcome") == "MATCHED"]
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t1mm = [r for r in t1 if r.get("outcome") == "MISMATCHED"]
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return {
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"evaluated_count": len(t1),
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"matched_count": len(t1m),
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"mismatched_count": len(t1mm),
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"match_rate_pct": round((len(t1m) / len(t1)) * 100, 2) if t1 else None,
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"t5_horizon": hsum("t5_evaluation_status", "t5_outcome", "t5_return_pct"),
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"t20_horizon": hsum("t20_evaluation_status", "t20_outcome", "t20_return_pct"),
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"last_updated": datetime.now().isoformat(timespec="seconds"),
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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("--history", default=str(DEFAULT_HISTORY))
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ap.add_argument("--lookback_days", type=int, default=90)
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ap.add_argument("--max_trade_days", type=int, default=45)
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args = ap.parse_args()
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jp = Path(args.json)
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hp = Path(args.history)
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if not jp.is_absolute():
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jp = ROOT / jp
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if not hp.is_absolute():
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hp = ROOT / hp
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payload = _load_json(jp)
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data = payload.get("data") if isinstance(payload.get("data"), dict) else {}
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hctx = data.get("_harness_context") if isinstance(data.get("_harness_context"), dict) else {}
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hist = _load_json(hp)
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records = hist.get("records") if isinstance(hist.get("records"), list) else []
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existing = {_text(r.get("proposal_id")) for r in records if isinstance(r, dict)}
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decisions = { _text(r.get("ticker")): r for r in _parse_rows(hctx.get("decisions_json")) if _text(r.get("ticker")) }
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blueprint = _parse_rows(hctx.get("order_blueprint_json"))
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names = {}
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templates: list[dict[str, Any]] = []
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for row in blueprint:
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ticker = _text(row.get("ticker"))
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if not ticker:
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continue
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dec = decisions.get(ticker, {})
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action = _text(dec.get("final_action") or row.get("order_type") or "WATCH")
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order_type = _text(row.get("order_type") or "WATCH")
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names[ticker] = _text(row.get("name"))
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templates.append({"ticker": ticker, "name": names[ticker], "action": action, "order_type": order_type})
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end_d = date.today()
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start_d = end_d - timedelta(days=max(35, args.lookback_days))
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start_s = start_d.strftime("%Y%m%d")
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end_s = end_d.strftime("%Y%m%d")
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replay_rows: list[dict[str, Any]] = []
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for t in templates:
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ticker = t["ticker"]
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try:
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df = stock.get_market_ohlcv(start_s, end_s, ticker)
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except Exception:
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continue
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if df is None or len(df.index) < 30:
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continue
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closes = []
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for idx, row in df.iterrows():
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c = _to_num(row.get("종가"))
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if c is None or c <= 0:
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continue
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d = idx.date().isoformat() if hasattr(idx, "date") else str(idx)[:10]
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closes.append((d, c))
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if len(closes) < 30:
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continue
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start_i = max(0, len(closes) - args.max_trade_days - 21)
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end_i = len(closes) - 21
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expected = _expected_direction(t["action"], t["order_type"])
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for i in range(start_i, end_i):
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proposal_date, p_close = closes[i]
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d1, c1 = closes[i + 1]
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d5, c5 = closes[i + 5]
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d20, c20 = closes[i + 20]
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pid = f"REPLAY:{proposal_date}:{ticker}:{t['order_type']}:{t['action']}"
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if pid in existing:
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continue
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ret1 = round((c1 / p_close - 1.0) * 100.0, 2)
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ret5 = round((c5 / p_close - 1.0) * 100.0, 2)
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ret20 = round((c20 / p_close - 1.0) * 100.0, 2)
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replay_rows.append({
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"proposal_id": pid,
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"record_type": "HISTORICAL_REPLAY_EOD",
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"data_origin": "REPLAY_FROM_KRX_EOD",
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"proposal_date": proposal_date,
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"ticker": ticker,
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"name": t["name"],
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"action": t["action"],
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"order_type": t["order_type"],
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"validation_status": "REPLAY_BACKFILL",
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"expected_direction": expected,
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"proposed_close": p_close,
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"proposed_limit_price": None,
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"proposed_quantity": None,
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"rule_basis": "REPLAY_BACKFILL_KRX_EOD",
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"evaluation_status": "EVALUATED_T1",
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"result_date": d1,
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"result_close": c1,
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"next_return_pct": ret1,
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"outcome": _classify(ret1, expected, t["action"], "t1"),
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"error_cause": "REPLAY_BACKFILL",
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"improvement_proposal": "REPLAY_ONLY_DO_NOT_AUTO_ADOPT",
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"t5_evaluation_status": "EVALUATED_T5",
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"t5_result_date": d5,
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"t5_return_pct": ret5,
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"t5_outcome": _classify(ret5, expected, t["action"], "t5"),
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"t20_evaluation_status": "EVALUATED_T20",
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"t20_result_date": d20,
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"t20_return_pct": ret20,
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"t20_outcome": _classify(ret20, expected, t["action"], "t20"),
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})
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records.extend(replay_rows)
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records = [r for r in records if isinstance(r, dict)]
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records.sort(key=lambda r: (_text(r.get("proposal_date")), _text(r.get("ticker")), _text(r.get("proposal_id"))))
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hist["schema_version"] = "2026-05-25-proposal-evaluation-v3-replay"
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hist["records"] = records
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hist["summary"] = _summarize(records)
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hp.parent.mkdir(parents=True, exist_ok=True)
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hp.write_text(json.dumps(hist, ensure_ascii=False, indent=2), encoding="utf-8")
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print(f"REPLAY_BACKFILL_OK records_added={len(replay_rows)} total_records={len(records)}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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