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>
2026-06-13 13:20:14 +09:00
commit ee3e799de1
1474 changed files with 176087 additions and 0 deletions
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+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import statistics
+from pathlib import Path
+
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_HISTORY = ROOT / "Temp" / "proposal_evaluation_history.json"
+DEFAULT_CONTINUOUS = ROOT / "Temp" / "continuous_evaluation_dashboard_v1.json"
+DEFAULT_OUT = ROOT / "Temp" / "prediction_lift_dashboard_v1.json"
+
+
+def _load_json(path: Path) -> object:
+    if not path.exists():
+        return {}
+    try:
+        return json.loads(path.read_text(encoding="utf-8"))
+    except Exception:
+        return {}
+
+
+def _records(payload: object) -> list[dict]:
+    if isinstance(payload, dict) and isinstance(payload.get("records"), list):
+        return [r for r in payload["records"] if isinstance(r, dict)]
+    if isinstance(payload, list):
+        return [r for r in payload if isinstance(r, dict)]
+    return []
+
+
+def _confidence_interval_pct(rate_pct: float, n: int) -> tuple[float, float]:
+    if n <= 0:
+        return 0.0, 0.0
+    p = rate_pct / 100.0
+    z = 1.96
+    denom = 1.0 + z * z / n
+    center = (p + z * z / (2 * n)) / denom
+    margin = z * math.sqrt((p * (1 - p) + z * z / (4 * n)) / n) / denom
+    return round(max(0.0, (center - margin) * 100.0), 2), round(min(100.0, (center + margin) * 100.0), 2)
+
+
+def _horizon_stats(records: list[dict], status_key: str, outcome_key: str, return_key: str) -> dict:
+    total = len(records)
+    matched = [r for r in records if str(r.get(outcome_key) or "").upper() == "MATCHED"]
+    pass_rate = round(len(matched) / total * 100.0, 2) if total else 0.0
+    returns = [float(r.get(return_key)) for r in records if r.get(return_key) is not None]
+    avg_ret = round(statistics.mean(returns), 2) if returns else None
+    ci_low, ci_high = _confidence_interval_pct(pass_rate, total)
+    return {
+        "sample_count": total,
+        "pass_rate_pct": pass_rate,
+        "avg_return_pct": avg_ret,
+        "confidence_interval_pct": [ci_low, ci_high],
+        "status_key": status_key,
+    }
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--history", default=str(DEFAULT_HISTORY))
+    ap.add_argument("--continuous", default=str(DEFAULT_CONTINUOUS))
+    ap.add_argument("--out", default=str(DEFAULT_OUT))
+    args = ap.parse_args()
+
+    history = _load_json(Path(args.history))
+    continuous = _load_json(Path(args.continuous))
+    records = _records(history)
+
+    t5 = [r for r in records if str(r.get("t5_evaluation_status") or "") == "EVALUATED_T5"]
+    t20 = [r for r in records if str(r.get("t20_evaluation_status") or "") == "EVALUATED_T20"]
+    t60 = [r for r in records if str(r.get("evaluation_status") or "").startswith("EVALUATED_")]
+
+    t5_stats = _horizon_stats(t5, "t5_evaluation_status", "t5_outcome", "t5_return_pct")
+    t20_stats = _horizon_stats(t20, "t20_evaluation_status", "t20_outcome", "t20_return_pct")
+    t60_stats = {
+        "sample_count": len(t60),
+        "pass_rate_pct": round((len([r for r in t60 if str(r.get("evaluation_status") or "").startswith("EVALUATED_")]) / max(1, len(t60))) * 100.0, 2),
+        "avg_return_pct": round(statistics.mean([float(r.get("t20_return_pct")) for r in t60 if r.get("t20_return_pct") is not None]), 2) if any(r.get("t20_return_pct") is not None for r in t60) else None,
+        "confidence_interval_pct": [0.0, 0.0],
+        "status_key": "evaluation_status",
+    }
+
+    baseline_random_pct = 50.0
+    benchmark_sector_neutral_pct = round(float((continuous.get("performance_readiness_score") or 0.0)), 2)
+    lift_vs_random = round(t5_stats["pass_rate_pct"] - baseline_random_pct, 2)
+    lift_vs_benchmark = round(t5_stats["pass_rate_pct"] - benchmark_sector_neutral_pct, 2)
+    after_slippage_pct = round((t5_stats["avg_return_pct"] or 0.0) - 0.35, 2) if t5_stats["avg_return_pct"] is not None else None
+
+    result = {
+        "formula_id": "PREDICTION_LIFT_DASHBOARD_V1",
+        "gate": "PASS" if t5_stats["sample_count"] > 0 else "INSUFFICIENT_DATA",
+        "baseline_random_pct": baseline_random_pct,
+        "benchmark_sector_neutral_pct": benchmark_sector_neutral_pct,
+        "transaction_cost_bps": 35,
+        "prediction_lift_vs_baseline_ppt": lift_vs_random,
+        "prediction_lift_vs_benchmark_ppt": lift_vs_benchmark,
+        "after_slippage_pct": after_slippage_pct,
+        "horizons": {
+            "t5": t5_stats,
+            "t20": t20_stats,
+            "t60": t60_stats,
+        },
+        "sample_count": len(records),
+        "confidence_policy": "Wilson 95% CI",
+        "source_paths": [str(Path(args.history)), str(Path(args.continuous))],
+    }
+    out = Path(args.out)
+    out.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
+    print(json.dumps(result, ensure_ascii=False, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())