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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import sys
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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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DEFAULT_JSON = ROOT / "GatherTradingData.json"
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DEFAULT_ENGINE_AUDIT = ROOT / "Temp" / "engine_audit_v1.json"
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DEFAULT_OUT = ROOT / "Temp" / "imputed_data_exposure_gate_v2.json"
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FORMULA_ID = "IMPUTED_DATA_EXPOSURE_GATE_V2"
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BLOCK_RATIO = 0.50
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WARN_RATIO = 0.25
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FUND_FACTOR_MIN_COVERAGE = 0.50
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DOMAIN_WEIGHTS = {
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"fundamental_core": 0.30,
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"realized_outcome": 0.30,
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"trade_quality": 0.15,
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"pattern": 0.10,
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"alpha_eval": 0.15,
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}
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if sys.stdout.encoding and sys.stdout.encoding.lower() not in ("utf-8", "utf8"):
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sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf-8", buffering=1)
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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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data = json.loads(path.read_text(encoding="utf-8"))
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except Exception:
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return {}
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return data if isinstance(data, dict) else {}
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def _as_float(value: Any, default: float | None = None) -> float | None:
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try:
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return float(value)
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except Exception:
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return default
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def _extract_harness_root(payload: dict[str, Any]) -> dict[str, Any]:
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h_apex = payload.get("hApex")
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data_apex = ((payload.get("data") or {}).get("_harness_context")) if isinstance(payload.get("data"), dict) else None
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if isinstance(h_apex, dict) and isinstance(data_apex, dict):
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merged = dict(data_apex)
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merged.update(h_apex)
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return merged
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if isinstance(h_apex, dict):
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return h_apex
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if isinstance(data_apex, dict):
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return data_apex
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return payload
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def build_gate(payload: dict[str, Any], audit: dict[str, Any]) -> dict[str, Any]:
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hctx = _extract_harness_root(payload)
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exposure = audit.get("imputed_data_exposure") if isinstance(audit.get("imputed_data_exposure"), dict) else {}
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weighted_coverage = _as_float(exposure.get("weighted_coverage"))
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imputed_field_ratio = _as_float(exposure.get("imputed_field_ratio"))
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effective_confidence_honest = _as_float(exposure.get("effective_confidence_honest"))
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raw_confidence_cap_basis = _as_float(exposure.get("raw_confidence_cap_basis"))
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confidence_cap_inflation_gap = _as_float(exposure.get("confidence_cap_inflation_gap"))
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fundamental_core_factor_coverage = _as_float(exposure.get("fundamental_core_factor_coverage"))
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fundamental_missing_ratio = _as_float(exposure.get("fundamental_missing_ratio"))
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surrogate_outcome_ratio = _as_float(exposure.get("surrogate_outcome_ratio"))
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domain_coverage = exposure.get("domain_coverage") if isinstance(exposure.get("domain_coverage"), dict) else {}
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if weighted_coverage is None:
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weights = DOMAIN_WEIGHTS
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weighted_coverage = 0.0
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for key, weight in weights.items():
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weighted_coverage += weight * float(domain_coverage.get(key, 0.0) or 0.0)
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weighted_coverage = round(weighted_coverage, 4)
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if imputed_field_ratio is None:
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imputed_field_ratio = round(1.0 - weighted_coverage, 4)
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if raw_confidence_cap_basis is None:
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raw_confidence_cap_basis = _as_float(hctx.get("confidence_cap_basis_score"), 0.0)
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if effective_confidence_honest is None and raw_confidence_cap_basis is not None:
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effective_confidence_honest = round(raw_confidence_cap_basis * (0.4 + 0.6 * weighted_coverage), 1)
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if confidence_cap_inflation_gap is None and raw_confidence_cap_basis is not None and effective_confidence_honest is not None:
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confidence_cap_inflation_gap = round(raw_confidence_cap_basis - effective_confidence_honest, 1)
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if fundamental_core_factor_coverage is None:
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fundamental_core_factor_coverage = _as_float(domain_coverage.get("fundamental_core"), 0.0)
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if fundamental_missing_ratio is None:
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fundamental_missing_ratio = round(max(0.0, 1.0 - (fundamental_core_factor_coverage or 0.0)), 4)
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if surrogate_outcome_ratio is None:
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surrogate_outcome_ratio = round(max(0.0, 1.0 - _as_float(domain_coverage.get("realized_outcome"), 0.0)), 4)
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if imputed_field_ratio >= BLOCK_RATIO:
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gate_status = "IMPUTED_DATA_BLOCK"
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elif imputed_field_ratio >= WARN_RATIO:
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gate_status = "IMPUTED_DATA_WARN"
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else:
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gate_status = "PASS"
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t20_sample = _as_float(hctx.get("t20_operational_sample"), 0.0) or 0.0
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long_horizon_allowed = bool(t20_sample > 0 and (fundamental_core_factor_coverage or 0.0) >= FUND_FACTOR_MIN_COVERAGE)
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fundamental_claim_allowed = bool((fundamental_core_factor_coverage or 0.0) >= FUND_FACTOR_MIN_COVERAGE)
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exposure_reasons: list[str] = []
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if fundamental_core_factor_coverage is not None and fundamental_core_factor_coverage < FUND_FACTOR_MIN_COVERAGE:
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exposure_reasons.append(
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"FUNDAMENTAL_CORE_FACTORS_MISSING: "
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f"coverage={fundamental_core_factor_coverage:.2f}"
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)
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if t20_sample <= 0:
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exposure_reasons.append("REALIZED_OUTCOME_T20_ZERO: t20_sample=0")
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if confidence_cap_inflation_gap is not None and confidence_cap_inflation_gap > 0:
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exposure_reasons.append(
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"CONFIDENCE_CAP_INFLATED: "
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f"reported={raw_confidence_cap_basis} honest={effective_confidence_honest} gap={confidence_cap_inflation_gap}"
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)
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result = {
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"formula_id": FORMULA_ID,
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"gate_status": gate_status,
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"imputed_field_ratio": round(imputed_field_ratio, 4),
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"imputed_domain_ratio": round(sum(1 for v in domain_coverage.values() if float(v or 0.0) < 0.5) / len(DOMAIN_WEIGHTS), 4)
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if domain_coverage
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else 1.0,
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"weighted_coverage": round(weighted_coverage, 4),
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"domain_coverage": {
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key: round(float(domain_coverage.get(key, 0.0) or 0.0), 4)
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for key in DOMAIN_WEIGHTS
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},
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"fundamental_core_factor_coverage": round(fundamental_core_factor_coverage or 0.0, 4),
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"fundamental_missing_ratio": round(fundamental_missing_ratio or 0.0, 4),
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"surrogate_outcome_ratio": round(surrogate_outcome_ratio or 0.0, 4),
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"raw_confidence_cap_basis": raw_confidence_cap_basis,
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"effective_confidence_honest": effective_confidence_honest,
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"confidence_cap_inflation_gap": confidence_cap_inflation_gap,
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"long_horizon_allowed": long_horizon_allowed,
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"fundamental_claim_allowed": fundamental_claim_allowed,
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"report_render_skew": {
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"report_dqg_completeness_pct": audit.get("report_render_skew", {}).get("report_dqg_completeness_pct") if isinstance(audit.get("report_render_skew"), dict) else "not_available",
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"authoritative_dqg_completeness_pct": audit.get("report_render_skew", {}).get("authoritative_dqg_completeness_pct") if isinstance(audit.get("report_render_skew"), dict) else "not_available",
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"skew_detected": bool(audit.get("report_render_skew", {}).get("skew_detected")) if isinstance(audit.get("report_render_skew"), dict) else False,
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},
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"exposure_reasons": exposure_reasons,
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"thresholds": {
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"block_ratio": BLOCK_RATIO,
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"warn_ratio": WARN_RATIO,
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"fund_factor_min_coverage": FUND_FACTOR_MIN_COVERAGE,
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},
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"formula": (
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"weighted_coverage = Σ(weight_d × coverage_d); "
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"imputed_field_ratio = 1 - weighted_coverage; "
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"effective_confidence_honest = raw_cap × (0.4 + 0.6 × weighted_coverage)"
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),
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"source": {
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"payload_path": str(DEFAULT_JSON),
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"engine_audit_path": str(DEFAULT_ENGINE_AUDIT),
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},
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}
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return result
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def main() -> int:
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ap = argparse.ArgumentParser(description="Build imputed data exposure gate from engine audit artifacts.")
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ap.add_argument("--json", default=str(DEFAULT_JSON))
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ap.add_argument("--audit", default=str(DEFAULT_ENGINE_AUDIT))
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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)
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audit_path = Path(args.audit)
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out_path = Path(args.out)
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if not json_path.is_absolute():
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json_path = ROOT / json_path
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if not audit_path.is_absolute():
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audit_path = ROOT / audit_path
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if not out_path.is_absolute():
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out_path = ROOT / out_path
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payload = _load_json(json_path)
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audit = _load_json(audit_path)
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result = build_gate(payload, audit)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
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print(json.dumps(result, ensure_ascii=False, indent=2))
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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