#!/usr/bin/env python3 """ build_calibration_priority_v1.py ─────────────────────────────────────────────────────────────────────────────── P4 확장: alpha_feedback_loop_v2.json → calibration_registry.yaml 보정 제안 연결 alpha_feedback_loop_v2.json의 recommended_adjustments 를 읽어 calibration_registry.yaml의 해당 임계값과 연결한 보정 우선순위 리포트를 생성한다. 출력: Temp/calibration_priority_v1.json - 보정 우선순위 목록 (feedback 신호 기반) - 각 임계값의 현재 상태(EXPERT_PRIOR/샘플 수)와 권장 조치 - alpha_feedback_loop 미포착(miss5_count) 신호와의 연결 사용법: python tools/build_calibration_priority_v1.py """ from __future__ import annotations import json import sys from pathlib import Path import yaml ROOT = Path(__file__).resolve().parent.parent AFL = ROOT / "Temp" / "alpha_feedback_loop_v2.json" REG = ROOT / "spec" / "calibration_registry.yaml" OUTPUT = ROOT / "Temp" / "calibration_priority_v1.json" PREDICTION_ACCURACY = ROOT / "Temp" / "prediction_accuracy_harness_v2.json" def registry_source_breakdown(reg_index: dict[str, dict]) -> dict: """WBS-7.1(2026-06-21) — calibration_registry.yaml 전체의 source별 분포를 매 실행마다 집계해 'CALIBRATED 비율이 실제로 몇 %인가'를 사람이 grep으로 직접 세지 않아도 항상 최신 상태로 노출한다(2026-06-21 비판적 리뷰 0c절에서 0/190 발견 당시 수동 집계 필요했던 문제 해소).""" counts: dict[str, int] = {"SPEC_DERIVED": 0, "EXPERT_PRIOR": 0, "PROVISIONAL": 0, "CALIBRATED": 0} for entry in reg_index.values(): source = str(entry.get("source", "")).upper() if source in counts: counts[source] += 1 total = sum(counts.values()) return { "total_thresholds": total, "counts": counts, "calibrated_pct": round(100.0 * counts["CALIBRATED"] / total, 2) if total else 0.0, "unvalidated_pct": round(100.0 * (counts["SPEC_DERIVED"] + counts["EXPERT_PRIOR"]) / total, 2) if total else 0.0, } def live_t5_status() -> dict: """WBS-7.2/7.1(2026-06-21) — T+5 수치를 하드코딩하지 않고 항상 최신 산출물에서 읽는다. Temp/prediction_accuracy_harness_v2.json이 없거나 sample=0이면 정직하게 DATA_GATED로 보고한다.""" if not PREDICTION_ACCURACY.exists(): return {"status": "ARTIFACT_MISSING", "t5_sample": 0, "t5_match_rate_pct": None} data = load_json(PREDICTION_ACCURACY) t5_sample = int(data.get("t5_sample") or 0) t5_rate = data.get("t5_op_rate") return { "status": "DATA_GATED" if t5_sample == 0 else "OK", "as_of_date": data.get("as_of_date"), "t5_sample": t5_sample, "t5_match_rate_pct": t5_rate, } if sys.stdout.encoding and sys.stdout.encoding.lower() not in ("utf-8", "utf8"): sys.stdout = open(sys.stdout.fileno(), mode="w", encoding="utf-8", buffering=1) # alpha_feedback 요인 → 관련 calibration_registry ID 매핑 FACTOR_TO_REGISTRY: dict[str, list[str]] = { "antithesis_balance": [ "ALEG_V2_GATE1_BLOCK_PCT", "ALEG_V2_GATE2_BLOCK_PCT", "DSD_V1_CONFIRMED_WS", ], "passive_signal_quality": [ "ALEG_V2_GATE1_BLOCK_PCT", # 뒷박 차단 임계 — timing=None 진입 허용 과다 "ALEG_V2_GATE1_WAIT_PCT", # PULLBACK_WAIT 경계 "K2_REBOUND_TRIGGER_ATR_MULT", # 반등 트리거 — 타이밍 조건 ], "active_signal_confidence": [ "ALEG_V2_GATE1_BLOCK_PCT", "ALEG_V2_GATE2_BLOCK_PCT", "DSD_V1_SIG1_WEIGHT", "DSD_V1_SIG2_WEIGHT", ], "k2_rebound_efficiency": [ "K2_SPLIT_RATIO", "K2_REBOUND_TRIGGER_ATR_MULT", "K2_DEADLINE_DAYS", "SCR_V4_EFFICIENCY_DAMAGE_PENALTY_COEFF", ], # CAPITAL_STYLE_ALLOCATION_V1 — 투자성향별 가중치 보정 # passive_signal_quality miss5_count=51 → 단타/단기 신호 가중치 재보정 필요 "passive_signal_quality_style": [ "CSA_SCALP_W_TECHNICAL", # 단타에서 기술지표 과도 의존 여부 확인 "CSA_SCALP_W_SMARTMONEY", "CSA_SWING_W_TECHNICAL", "CSA_SWING_W_SMARTMONEY", "CSA_TECH_RSI_OVERSOLD", # RSI<35 임계 최적화 "CSA_TECH_DISPARITY_PULLBACK", # 눌림목 3% 임계 최적화 ], "conviction_calibration": [ "CSA_POSITION_PCT_HIGH_CONVICTION", # 80점 임계 → 실측 분포 기반 조정 "CSA_POSITION_PCT_STRONG", # 65점 임계 "CSA_POSITION_PCT_MODERATE", # 50점 임계 "CSA_POSITION_PCT_PILOT", # 35점 임계 ], } def load_json(p: Path) -> dict: if not p.exists(): return {} return json.loads(p.read_text(encoding="utf-8")) def load_registry(p: Path) -> dict[str, dict]: if not p.exists(): return {} data = yaml.safe_load(p.read_text(encoding="utf-8")) return {t["id"]: t for t in data.get("thresholds", []) if "id" in t} def _priority_from_registry_entry(entry: dict, source_tag: str, urgency_bias: int) -> dict: sample_n = int(entry.get("sample_n", 0) or 0) source = str(entry.get("source", "EXPERT_PRIOR")) threshold_class = str(entry.get("threshold_class", "standard")) urgency = urgency_bias if source == "EXPERT_PRIOR": urgency += 10 if source == "PROVISIONAL": urgency += 20 if threshold_class == "live_critical": urgency += 15 if sample_n == 0: urgency += 5 if sample_n > 0: urgency += max(0, 30 - sample_n) return { "calibration_id": entry.get("id", ""), "current_value": entry.get("value"), "owner_formula": entry.get("owner_formula", ""), "source": source, "sample_n": sample_n, "linked_factor": source_tag, "alpha_action": "registry_review", "urgency_score": urgency, "calibration_path": ( ( "표본 30건 이상 확보 후 PROVISIONAL 승격 → " if sample_n >= 30 else f"표본 {30 - sample_n}건 추가 수집 후 PROVISIONAL 승격 → " ) + "실측 T+5 승률 기반 최적값 backtest → CALIBRATED 확정" ), "rationale": f"source={source}, class={threshold_class}, sample_n={sample_n}", } def main() -> int: afl_data = load_json(AFL) reg_index = load_registry(REG) sep = "=" * 70 print(sep) print(" 알파 피드백 루프 → 보정 우선순위 연결기 (CALIB-PRIORITY-V1)") print(sep) adjustments = afl_data.get("recommended_adjustments", []) cases_analyzed = afl_data.get("cases_analyzed", 0) miss5_count = 0 for adj in adjustments: if adj.get("factor") == "passive_signal_quality": miss5_count = int(adj.get("miss5_count", 0)) print(f"\n [alpha_feedback_loop_v2] cases_analyzed={cases_analyzed}") print(f" miss5_count(5%+ 급등 미포착)={miss5_count}건 → passive_signal_quality 개선 필요") priority_list: list[dict] = [] for adj in adjustments: factor = str(adj.get("factor", "")) action = str(adj.get("action", "")) rationale = str(adj.get("rationale", "")) reg_ids = FACTOR_TO_REGISTRY.get(factor, []) for rid in reg_ids: reg_entry = reg_index.get(rid) if not reg_entry: continue item = _priority_from_registry_entry(reg_entry, factor, miss5_count if factor == "passive_signal_quality" else 0) item["alpha_action"] = action or "feedback_review" if rationale: item["rationale"] = rationale[:200] priority_list.append(item) if not priority_list: # alpha_feedback_loop가 비어 있어도 registry 자체의 보정 debt를 추적할 수 있게 한다. for reg_id, reg_entry in reg_index.items(): source = str(reg_entry.get("source", "EXPERT_PRIOR")) if source not in {"EXPERT_PRIOR", "PROVISIONAL"}: continue tag = f"registry:{source.lower()}" item = _priority_from_registry_entry(reg_entry, tag, 0) if source == "PROVISIONAL": item["urgency_score"] += 5 priority_list.append(item) # 중복 제거 (같은 rid, 높은 urgency 유지) seen: dict[str, dict] = {} for p in priority_list: rid = p["calibration_id"] if rid not in seen or p["urgency_score"] > seen[rid]["urgency_score"]: seen[rid] = p priority_list = sorted(seen.values(), key=lambda x: -x["urgency_score"]) print(f"\n [보정 우선순위 TOP-10]") print(f" {'순위':<4} {'ID':<45} {'값':>7} {'샘플':>5} {'긴급도':>6}") print(f" {'-'*4} {'-'*45} {'-'*7} {'-'*5} {'-'*6}") for rank, item in enumerate(priority_list[:10], 1): print( f" {rank:<4} {item['calibration_id']:<45} " f"{str(item['current_value']):>7} {item['sample_n']:>5} {item['urgency_score']:>6}" ) print(f"\n [보정 로드맵]") print(f" Step 1 (즉시): 표본 누적 — 매 거래일 T+5 결과 자동 수집") print(f" Step 2 (30건 후): ALEG_V2_GATE1_BLOCK_PCT 3.0% → 실측 최적값으로 PROVISIONAL 승격") print(f" Step 3 (50건 후): DSD_V1 가중치 logistic regression 최적화") print(f" Step 4 (100건 후): K2_SPLIT_RATIO backtest 비교 → CALIBRATED 확정") registry_health = registry_source_breakdown(reg_index) t5_status = live_t5_status() print(f"\n [캘리브레이션 레지스트리 건강도] (WBS-7.1)") print(f" total={registry_health['total_thresholds']} {registry_health['counts']}") print(f" CALIBRATED={registry_health['calibrated_pct']}% 미검증(SPEC_DERIVED+EXPERT_PRIOR)={registry_health['unvalidated_pct']}%") if t5_status["status"] == "DATA_GATED": print(f" miss5_count={miss5_count}건 → T+5 현재 DATA_GATED(sample=0) — passive_signal_quality 개선 영향은 표본 누적 후 측정 가능") elif t5_status["status"] == "ARTIFACT_MISSING": print(f" miss5_count={miss5_count}건 → T+5 산출물 없음(Temp/prediction_accuracy_harness_v2.json) — 먼저 생성 필요") else: print(f" miss5_count={miss5_count}건 → T+5={t5_status['t5_match_rate_pct']}% (as_of={t5_status.get('as_of_date')}) → passive_signal_quality 개선 핵심") result = { "status": "CALIBRATION_PRIORITY_OK", "cases_analyzed": cases_analyzed, "miss5_count": miss5_count, "priority_count": len(priority_list), "priority_list": priority_list, "roadmap": { "step1": "표본 누적 — 매 거래일 T+5 결과 자동 수집", "step2": "30건 후: ALEG_V2_GATE1_BLOCK_PCT 3.0% → 실측 최적값 PROVISIONAL 승격", "step3": "50건 후: DSD_V1 가중치 logistic regression 최적화", "step4": "100건 후: K2_SPLIT_RATIO 30/70~60/40 backtest → CALIBRATED", }, "priority_basis": "alpha_feedback_loop_v2" if adjustments else "registry_warning_fallback", "registry_health": registry_health, "target_improvement": { "t5_status": t5_status["status"], "current_t5_pct": t5_status["t5_match_rate_pct"], "t5_as_of_date": t5_status.get("as_of_date"), "target_t5_pct": 55.0, "key_lever": f"passive_signal_quality (miss5_count={miss5_count}건 개선)", }, } OUTPUT.parent.mkdir(parents=True, exist_ok=True) OUTPUT.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8") print(f"\n → 결과 저장: {OUTPUT}") print(f" CALIBRATION_PRIORITY_OK\n") return 0 if __name__ == "__main__": raise SystemExit(main())