docs: .NET 렌더러 운영 상태와 검증 기준 정리
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- 운영 상태 문서와 README를 .NET canonical renderer 기준으로 정리했습니다. - 레거시 렌더러 비운영 선언과 감사/검증기 경로를 통일했습니다. - 운영 보정 로직의 데이터 소스 반영을 정리했습니다.
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@@ -45,13 +45,13 @@ def _count_renderer_calcs(path: Path) -> int:
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def _count_reverse_dependencies(root: Path) -> int:
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count = 0
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for p in root.rglob("*.py"):
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if p.name in ["render_operational_report.py", "build_architecture_boundaries_v2.py"]:
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if p.name in ["build_architecture_boundaries_v2.py", "Program.cs"]:
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continue
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try:
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txt = p.read_text(encoding="utf-8")
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except Exception:
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continue
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if "import render_operational_report" in txt or "from render_operational_report" in txt:
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if "import render_operational_report" in txt or "from render_operational_report" in txt or "render_operational_report.py" in txt:
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count += 1
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return count
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@@ -61,7 +61,7 @@ def main() -> int:
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ap.add_argument("--out", default=str(DEFAULT_OUT))
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args = ap.parse_args()
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renderer = ROOT / "tools" / "render_operational_report.py"
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renderer = ROOT / "src" / "dotnet" / "QuantEngine.Tools" / "Program.cs"
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harness = load_json(TEMP / "module_io_coverage_v1.json")
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artifact_chain = load_json(TEMP / "artifact_chain_hash_v4.json")
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@@ -76,7 +76,7 @@ def main() -> int:
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"source_artifacts": [
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"Temp/module_io_coverage_v1.json",
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"Temp/artifact_chain_hash_v4.json",
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"tools/render_operational_report.py",
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"src/dotnet/QuantEngine.Tools/Program.cs",
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],
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}
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save_json(args.out, result)
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@@ -3,7 +3,7 @@ build_canonical_metrics_v1.py
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목적: spec/25_canonical_metrics_registry.yaml에 정의된 논리 지표를
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단일 정규 원천에서 읽어 Temp/canonical_metrics_v1.json으로 산출.
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렌더러(render_operational_report.py)는 이 파일을 경유해서만 지표값을 조회하고
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렌더러(src/dotnet/QuantEngine.Tools)는 이 파일을 경유해서만 지표값을 조회하고
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직접 harness_context의 중복 키를 읽지 않는다.
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출력 구조:
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@@ -4,6 +4,7 @@ import argparse
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import json
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from pathlib import Path
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from typing import Any
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import re
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ROOT = Path(__file__).resolve().parents[1]
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@@ -31,6 +32,20 @@ def _f(value: Any, default: float = 0.0) -> float:
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return default
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def _extract_float(text: Any, pattern: str, default: float | None = None) -> float | None:
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try:
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s = str(text)
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except Exception:
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return default
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m = re.search(pattern, s)
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if not m:
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return default
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try:
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return float(m.group(1))
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except Exception:
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return default
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def main() -> int:
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ap = argparse.ArgumentParser()
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ap.add_argument("--outcome", default=str(DEFAULT_OUTCOME))
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@@ -46,15 +61,37 @@ def main() -> int:
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trade_quality = _load(Path(args.trade_quality) if Path(args.trade_quality).is_absolute() else ROOT / args.trade_quality)
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scr_arg = args.scr_v5 or args.scr_v4 or str(DEFAULT_SCR)
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scr_v4 = _load(Path(scr_arg) if Path(scr_arg).is_absolute() else ROOT / scr_arg)
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live_outcome = _load(ROOT / "Temp" / "live_outcome_ledger_v1.json")
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strategy_hardening = _load(ROOT / "Temp" / "strategy_hardening_harness_v2.json")
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metrics = outcome.get("metrics") if isinstance(outcome.get("metrics"), dict) else {}
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hardening_scores = strategy_hardening.get("domain_scores") or {}
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oq_score = _f(outcome.get("score"))
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hardening_oq = _f(hardening_scores.get("outcome_quality"), oq_score)
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if hardening_oq > 0.0:
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oq_score = hardening_oq
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t20_sample = int(_f(metrics.get("t20_operational_evaluated_count"), 0.0))
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t20_rate = _f(metrics.get("t20_operational_pass_rate"))
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if t20_sample <= 0:
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t20_sample = int(_f(live_outcome.get("live_t20_evaluated_count"), 0.0))
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if t20_rate <= 0.0:
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live_samples = live_outcome.get("live_t20_samples") if isinstance(live_outcome.get("live_t20_samples"), list) else []
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if live_samples:
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live_correct = sum(1 for row in live_samples if isinstance(row, dict) and row.get("decision_correct") is True)
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live_total = sum(1 for row in live_samples if isinstance(row, dict))
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if live_total > 0:
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t20_rate = round((live_correct / live_total) * 100.0, 2)
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t5_rate = _f(prediction.get("t5_op_rate"))
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t5_sample = int(_f(prediction.get("t5_sample"), 0.0))
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tq_score = _f(trade_quality.get("summary_score"))
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hardening_tq = _f(hardening_scores.get("prediction_match_rate_pct"), tq_score)
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if hardening_tq > 0.0:
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tq_score = hardening_tq
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value_damage = _f(scr_v4.get("value_damage_pct_avg"))
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hardening_value_damage = _f(hardening_scores.get("cash_recovery_value_damage_pct"), value_damage)
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if hardening_value_damage > 0.0:
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value_damage = hardening_value_damage
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# [Work 20] 임계값 현실화 — MONITOR 상태(t5≥45%) 데이터 성숙도에 맞게 조정
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# t5=55 → 50: MONITOR 하한(45%)과 CALIBRATED(60%) 사이 현실적 중간값
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@@ -31,7 +31,7 @@ PY_FILES = [
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ROOT / "tools" / "compute_formula_outputs.py",
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ROOT / "tools" / "validate_alpha_execution_harness.py",
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ROOT / "tools" / "validate_harness_context.py",
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ROOT / "tools" / "render_operational_report.py",
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ROOT / "src" / "dotnet" / "QuantEngine.Tools" / "Program.cs",
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# Phase-1 결정론 도구 (Python-tool-only formulas)
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ROOT / "tools" / "build_ejce_view_renderer_v1.py",
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ROOT / "tools" / "build_smart_cash_recovery_v3.py",
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@@ -71,7 +71,7 @@ def main() -> int:
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corpus = _scan_code()
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spec_total = len(formula_ids)
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impl = [fid for fid in formula_ids if fid in corpus]
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report_binding = [fid for fid in formula_ids if fid in corpus and "render_operational_report.py" in corpus]
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report_binding = [fid for fid in formula_ids if fid in corpus and "src/dotnet/QuantEngine.Tools" in corpus]
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outcome_binding = [fid for fid in formula_ids if fid.startswith(("OUTCOME_", "TRADE_", "SHORT_HORIZON_", "LATE_", "REBOUND_", "CASH_RAISE_")) and fid in corpus]
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golden_path = GOLDEN_V2 if GOLDEN_V2.exists() else GOLDEN_TEMP
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