feat(phase-4): LGBM 모델 + 앙상블 + 매칭/재학습 잡

- backend/app/models/lgbm.py: 종목 × horizon 별 LightGBM 회귀(y_ret_h)
  + 다중분류(y_dir_h, 3-class). joblib 으로 backend/data/models/{code}_h{H}_*.pkl
  저장. early_stopping(30). predict_one() 으로 최신 영업일 피처에 추론.
- backend/app/models/weights.py: ensemble_weights 테이블 IO,
  default w_chronos=0.6 / w_lgbm=0.4 (DB 행 없으면 fallback).
- backend/app/models/ensemble.py: Chronos sample 분포 + LGBM regression+cls
  결합. point/q10/q90 + prob_up/flat/down + direction 라벨. 한쪽 모델
  실패 시 다른 쪽 단독 fallback (cold start: chronos 단독).
- backend/app/pipelines/predict_one.py: predict_and_store(). 결과를
  predictions 테이블에 UPSERT, user_triggered 누적 OR. base_date = 마지막
  ohlcv 거래일, target_date = base_date + H 영업일(주말 스킵, 공휴일은
  매칭잡에서 자연 보정).
- backend/app/pipelines/match_outcomes.py: target_date == d 인
  user_triggered=TRUE 예측을 d 의 실제 종가와 매칭 → prediction_outcomes
  적재. direction_hit(±0.3% flat band) + abs_error. 실제 종가 없으면
  자연 skip.
- backend/app/pipelines/retrain_weekly.py: 시드 10종목 × H 재학습 +
  최근 30일 model_performance 적재.
- backend/app/db/migrations/003_ensemble_weights.sql: (code, horizon) →
  (w_chronos, w_lgbm, hit_rate_*, sample_count).
- backend/app/pipelines/scheduler.py:
    daily_batch    : 평일 16:00 KST
    match_outcomes : 평일 16:30 KST   ← 사용자가 확정한 매칭 시점
    retrain_weekly : 일요일 02:00 KST

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

backend/app/pipelines/retrain_weekly.py

@@ -0,0 +1,122 @@
+"""주간 재학습 + 앙상블 가중치 보정.
+
+일요일 02:00 KST 실행:
+  1. 시드 10종목 × horizon (1,3,5) 별로 LGBM 학습 (train_one).
+  2. 최근 30일 prediction_outcomes 의 model 별 hit_rate 산출, model_performance 적재.
+  3. 같은 30일 윈도우에서 chronos vs lgbm hit_rate 로 ensemble_weights 갱신.
+     (방법: w_chronos = clamp(0.1, hr_c / (hr_c + hr_l), 0.9), w_lgbm = 1 - w_chronos.
+      hit_rate 데이터가 부족하면 default 0.6/0.4 유지.)
+
+지금은 'ensemble' 모델 단일 종류로 predictions 가 쌓이므로, 가중치 보정은
+chronos 단독 시뮬레이션 / lgbm 단독 시뮬레이션 hit_rate 비교가 진정한 방식인데,
+Phase 4 단순화: 'ensemble' 의 종합 hit_rate 만 model_performance 에 기록하고
+가중치는 default 유지. 진짜 비교는 Phase 7 (chronos 단독 + lgbm 단독 예측을
+shadow 로 같이 적재하는 구조) 로 확장.
+"""
+from __future__ import annotations
+
+import json
+import logging
+from datetime import date, timedelta
+from typing import Any
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+from app.models.lgbm import train_one
+from app.seed.seed_tickers import SEED_TICKERS
+
+logger = logging.getLogger(__name__)
+
+HORIZONS = (1, 3, 5)
+WINDOW_DAYS = 30
+
+
+def retrain_all() -> list[dict[str, Any]]:
+    """시드 10종목 × horizons 학습."""
+    out: list[dict[str, Any]] = []
+    for t in SEED_TICKERS:
+        for h in HORIZONS:
+            try:
+                res = train_one(t.code, h)
+            except Exception as exc:  # noqa: BLE001
+                logger.exception("train_one failed for %s h=%d", t.code, h)
+                res = {"code": t.code, "horizon": h, "status": "failed", "error": str(exc)}
+            out.append(res)
+    return out
+
+
+def record_performance(as_of: date) -> list[dict[str, Any]]:
+    """최근 WINDOW_DAYS 의 prediction_outcomes 로 (code, model, horizon) 별
+    hit_rate / mae 산출, model_performance 에 upsert."""
+    eng = get_engine()
+    start = as_of - timedelta(days=WINDOW_DAYS)
+    summary: list[dict[str, Any]] = []
+    with eng.begin() as conn:
+        rows = conn.execute(
+            text(
+                """
+                SELECT code, model, horizon,
+                       AVG(CASE WHEN direction_hit THEN 1.0 ELSE 0.0 END) AS hit_rate,
+                       AVG(abs_error)                                     AS mae,
+                       COUNT(*)                                           AS n
+                  FROM prediction_outcomes
+                 WHERE resolved_at >= :start
+                 GROUP BY code, model, horizon
+                """
+            ),
+            {"start": start},
+        ).all()
+        for code, model, horizon, hr, mae, n in rows:
+            conn.execute(
+                text(
+                    """
+                    INSERT INTO model_performance
+                      (code, model, window_days, as_of, hit_rate, mae, sample_count)
+                    VALUES (:c, :m, :w, :as_of, :hr, :mae, :n)
+                    ON CONFLICT (code, model, window_days, as_of) DO UPDATE SET
+                      hit_rate = EXCLUDED.hit_rate,
+                      mae      = EXCLUDED.mae,
+                      sample_count = EXCLUDED.sample_count
+                    """
+                ),
+                {
+                    "c": code,
+                    "m": model,
+                    "w": WINDOW_DAYS,
+                    "as_of": as_of,
+                    "hr": float(hr) if hr is not None else None,
+                    "mae": float(mae) if mae is not None else None,
+                    "n": int(n),
+                },
+            )
+            summary.append(
+                {
+                    "code": code,
+                    "model": model,
+                    "horizon": horizon,
+                    "hit_rate": float(hr) if hr is not None else None,
+                    "mae": float(mae) if mae is not None else None,
+                    "n": int(n),
+                }
+            )
+    return summary
+
+
+def run_weekly() -> dict[str, Any]:
+    """일요일 02:00 KST 호출 entry-point."""
+    from datetime import datetime, timezone
+
+    kst = timezone(timedelta(hours=9))
+    as_of = datetime.now(kst).date()
+    return {
+        "as_of": str(as_of),
+        "trained": retrain_all(),
+        "performance": record_performance(as_of),
+    }
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+    out = run_weekly()
+    print(json.dumps(out, ensure_ascii=False, indent=2, default=str))