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>
70 lines
2.6 KiB
Python
70 lines
2.6 KiB
Python
from __future__ import annotations
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import argparse
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import json
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from datetime import datetime, timezone
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from statistics import mean
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from v7_hardening_common import ROOT, TEMP, load_json, save_json
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DEFAULT_OUT = TEMP / "confidence_calibration_v2.json"
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def _label_rank(label: str) -> int:
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order = {
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"BEARISH": 0,
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"NEUTRAL": 1,
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"BULLISH": 2,
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"STRONG_BULLISH": 3,
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}
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return order.get(str(label).upper(), 1)
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--out", default=str(DEFAULT_OUT))
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args = ap.parse_args()
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exposure = load_json(TEMP / "imputed_data_exposure_gate_v2.json")
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pred = load_json(TEMP / "prediction_accuracy_harness_v2.json")
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conf = load_json(TEMP / "portfolio_alpha_confidence_per_ticker_v1.json")
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rows = [r for r in conf.get("rows", []) if isinstance(r, dict)]
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monotonic = True
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if rows:
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ordered = sorted(rows, key=lambda r: float(r.get("pac_score") or 0.0))
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label_scores = [_label_rank(r.get("pac_label") or "") for r in ordered]
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monotonic = all(a <= b for a, b in zip(label_scores, label_scores[1:]))
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high_conf_low_evidence = sum(
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1 for r in rows
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if float(r.get("pac_score") or 0.0) >= 80.0 and str(r.get("fundamental_grade") or "").upper() in {"D", "F", "E"}
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)
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result = {
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"formula_id": "CONFIDENCE_CALIBRATION_V2",
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"generated_at": datetime.now(timezone.utc).isoformat(),
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"confidence_cap_basis_score": float(exposure.get("raw_confidence_cap_basis") or exposure.get("effective_confidence_honest") or 0.0),
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"effective_confidence_honest": float(exposure.get("effective_confidence_honest") or 0.0),
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"confidence_cap_gap_pct": round(float(exposure.get("confidence_cap_inflation_gap") or 0.0), 1),
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"calibration_brier_score_improving": str(pred.get("calibration_state") or "").upper() == "CALIBRATED",
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"confidence_bucket_monotonicity": "PASS" if monotonic else "FAIL",
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"high_confidence_low_evidence_count": high_conf_low_evidence,
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"calibration_state": pred.get("calibration_state"),
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"portfolio_label_diversity": int(conf.get("label_diversity") or 0),
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"portfolio_score_mean": round(mean(float(r.get("pac_score") or 0.0) for r in rows), 2) if rows else 0.0,
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"source_paths": [
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"Temp/imputed_data_exposure_gate_v2.json",
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"Temp/prediction_accuracy_harness_v2.json",
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"Temp/portfolio_alpha_confidence_per_ticker_v1.json",
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],
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}
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save_json(args.out, result)
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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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