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>
This commit is contained in:
2026-06-13 13:20:14 +09:00
commit ee3e799de1
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from __future__ import annotations
import argparse
import json
from datetime import date, datetime, timedelta
from pathlib import Path
from typing import Any
from pykrx import stock
ROOT = Path(__file__).resolve().parents[1]
DEFAULT_JSON = ROOT / "GatherTradingData.json"
DEFAULT_HISTORY = ROOT / "Temp" / "proposal_evaluation_history.json"
def _load_json(path: Path) -> dict[str, Any]:
if not path.exists():
return {}
try:
obj = json.loads(path.read_text(encoding="utf-8"))
except Exception:
return {}
return obj if isinstance(obj, dict) else {}
def _parse_rows(value: Any) -> list[dict[str, Any]]:
if isinstance(value, list):
return [r for r in value if isinstance(r, dict)]
if isinstance(value, str):
try:
parsed = json.loads(value)
if isinstance(parsed, list):
return [r for r in parsed if isinstance(r, dict)]
except Exception:
return []
return []
def _text(v: Any) -> str:
return str(v or "").strip()
def _to_num(v: Any) -> float | None:
try:
if v is None or v == "":
return None
return float(v)
except Exception:
return None
def _expected_direction(action: str, order_type: str) -> str:
raw = f"{action} {order_type}".upper()
if "BUY" in raw or "ADD" in raw:
return "UP"
if "SELL" in raw or "TRIM" in raw or "EXIT" in raw or "STOP" in raw:
return "DOWN_OR_RISK_REDUCED"
if "WATCH" in raw:
return "NEUTRAL_TO_UP"
return "NEUTRAL"
def _classify(ret: float, expected: str, action: str, horizon: str) -> str:
if horizon == "t1":
up_pass, up_fail = 0.5, -1.0
down_pass, down_fail = 0.5, 1.5
nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -1.5, 3.0, -2.5, 5.0, 1.5
elif horizon == "t5":
up_pass, up_fail = 2.0, -3.0
down_pass, down_fail = 1.0, 4.0
nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -3.0, 7.0, -6.0, 12.0, 3.0
else:
up_pass, up_fail = 5.0, -8.0
down_pass, down_fail = 2.0, 10.0
nu_lo, nu_hi, nu_fail_lo, nu_fail_hi, neut = -5.0, 15.0, -10.0, 25.0, 6.0
if expected == "UP":
if ret >= up_pass:
return "MATCHED"
if ret <= up_fail:
return "MISMATCHED"
return "INCONCLUSIVE"
if expected == "DOWN_OR_RISK_REDUCED":
if ret <= down_pass:
return "MATCHED"
if ret >= down_fail:
return "MISMATCHED"
return "INCONCLUSIVE"
if expected == "NEUTRAL_TO_UP":
if nu_lo <= ret <= nu_hi:
return "MATCHED"
if ret <= nu_fail_lo or ret >= nu_fail_hi:
return "MISMATCHED"
return "INCONCLUSIVE"
if abs(ret) <= neut:
return "MATCHED"
if abs(ret) >= neut * 2:
return "MISMATCHED"
return "INCONCLUSIVE"
def _summarize(records: list[dict[str, Any]]) -> dict[str, Any]:
def hsum(status_key: str, outcome_key: str, ret_key: str) -> dict[str, Any]:
ev = [r for r in records if str(r.get(status_key) or "").startswith("EVALUATED_")]
m = [r for r in ev if r.get(outcome_key) == "MATCHED"]
mm = [r for r in ev if r.get(outcome_key) == "MISMATCHED"]
rets = [r.get(ret_key) for r in ev if isinstance(r.get(ret_key), (int, float))]
return {
"evaluated_count": len(ev),
"matched_count": len(m),
"mismatched_count": len(mm),
"match_rate_pct": round((len(m) / len(ev)) * 100, 2) if ev else None,
"avg_return_pct": round(sum(rets) / len(rets), 2) if rets else None,
}
t1 = [r for r in records if r.get("evaluation_status") == "EVALUATED_T1"]
t1m = [r for r in t1 if r.get("outcome") == "MATCHED"]
t1mm = [r for r in t1 if r.get("outcome") == "MISMATCHED"]
return {
"evaluated_count": len(t1),
"matched_count": len(t1m),
"mismatched_count": len(t1mm),
"match_rate_pct": round((len(t1m) / len(t1)) * 100, 2) if t1 else None,
"t5_horizon": hsum("t5_evaluation_status", "t5_outcome", "t5_return_pct"),
"t20_horizon": hsum("t20_evaluation_status", "t20_outcome", "t20_return_pct"),
"last_updated": datetime.now().isoformat(timespec="seconds"),
}
def main() -> int:
ap = argparse.ArgumentParser()
ap.add_argument("--json", default=str(DEFAULT_JSON))
ap.add_argument("--history", default=str(DEFAULT_HISTORY))
ap.add_argument("--lookback_days", type=int, default=90)
ap.add_argument("--max_trade_days", type=int, default=45)
args = ap.parse_args()
jp = Path(args.json)
hp = Path(args.history)
if not jp.is_absolute():
jp = ROOT / jp
if not hp.is_absolute():
hp = ROOT / hp
payload = _load_json(jp)
data = payload.get("data") if isinstance(payload.get("data"), dict) else {}
hctx = data.get("_harness_context") if isinstance(data.get("_harness_context"), dict) else {}
hist = _load_json(hp)
records = hist.get("records") if isinstance(hist.get("records"), list) else []
existing = {_text(r.get("proposal_id")) for r in records if isinstance(r, dict)}
decisions = { _text(r.get("ticker")): r for r in _parse_rows(hctx.get("decisions_json")) if _text(r.get("ticker")) }
blueprint = _parse_rows(hctx.get("order_blueprint_json"))
names = {}
templates: list[dict[str, Any]] = []
for row in blueprint:
ticker = _text(row.get("ticker"))
if not ticker:
continue
dec = decisions.get(ticker, {})
action = _text(dec.get("final_action") or row.get("order_type") or "WATCH")
order_type = _text(row.get("order_type") or "WATCH")
names[ticker] = _text(row.get("name"))
templates.append({"ticker": ticker, "name": names[ticker], "action": action, "order_type": order_type})
end_d = date.today()
start_d = end_d - timedelta(days=max(35, args.lookback_days))
start_s = start_d.strftime("%Y%m%d")
end_s = end_d.strftime("%Y%m%d")
replay_rows: list[dict[str, Any]] = []
for t in templates:
ticker = t["ticker"]
try:
df = stock.get_market_ohlcv(start_s, end_s, ticker)
except Exception:
continue
if df is None or len(df.index) < 30:
continue
closes = []
for idx, row in df.iterrows():
c = _to_num(row.get("종가"))
if c is None or c <= 0:
continue
d = idx.date().isoformat() if hasattr(idx, "date") else str(idx)[:10]
closes.append((d, c))
if len(closes) < 30:
continue
start_i = max(0, len(closes) - args.max_trade_days - 21)
end_i = len(closes) - 21
expected = _expected_direction(t["action"], t["order_type"])
for i in range(start_i, end_i):
proposal_date, p_close = closes[i]
d1, c1 = closes[i + 1]
d5, c5 = closes[i + 5]
d20, c20 = closes[i + 20]
pid = f"REPLAY:{proposal_date}:{ticker}:{t['order_type']}:{t['action']}"
if pid in existing:
continue
ret1 = round((c1 / p_close - 1.0) * 100.0, 2)
ret5 = round((c5 / p_close - 1.0) * 100.0, 2)
ret20 = round((c20 / p_close - 1.0) * 100.0, 2)
replay_rows.append({
"proposal_id": pid,
"record_type": "HISTORICAL_REPLAY_EOD",
"data_origin": "REPLAY_FROM_KRX_EOD",
"proposal_date": proposal_date,
"ticker": ticker,
"name": t["name"],
"action": t["action"],
"order_type": t["order_type"],
"validation_status": "REPLAY_BACKFILL",
"expected_direction": expected,
"proposed_close": p_close,
"proposed_limit_price": None,
"proposed_quantity": None,
"rule_basis": "REPLAY_BACKFILL_KRX_EOD",
"evaluation_status": "EVALUATED_T1",
"result_date": d1,
"result_close": c1,
"next_return_pct": ret1,
"outcome": _classify(ret1, expected, t["action"], "t1"),
"error_cause": "REPLAY_BACKFILL",
"improvement_proposal": "REPLAY_ONLY_DO_NOT_AUTO_ADOPT",
"t5_evaluation_status": "EVALUATED_T5",
"t5_result_date": d5,
"t5_return_pct": ret5,
"t5_outcome": _classify(ret5, expected, t["action"], "t5"),
"t20_evaluation_status": "EVALUATED_T20",
"t20_result_date": d20,
"t20_return_pct": ret20,
"t20_outcome": _classify(ret20, expected, t["action"], "t20"),
})
records.extend(replay_rows)
records = [r for r in records if isinstance(r, dict)]
records.sort(key=lambda r: (_text(r.get("proposal_date")), _text(r.get("ticker")), _text(r.get("proposal_id"))))
hist["schema_version"] = "2026-05-25-proposal-evaluation-v3-replay"
hist["records"] = records
hist["summary"] = _summarize(records)
hp.parent.mkdir(parents=True, exist_ok=True)
hp.write_text(json.dumps(hist, ensure_ascii=False, indent=2), encoding="utf-8")
print(f"REPLAY_BACKFILL_OK records_added={len(replay_rows)} total_records={len(records)}")
return 0
if __name__ == "__main__":
raise SystemExit(main())