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/predict_one.py

@@ -0,0 +1,138 @@
+"""On-demand 예측 + DB 적재.
+
+POST /api/predict/{code} 에서 호출. 사용자가 "예상차트 보기" 누른 시점.
+- ensemble.predict() 로 horizons (1,3,5) 결과 계산
+- base_date = 마지막 ohlcv_daily.date, target_date = base_date + horizon 영업일
+  (대충 calendar 일로 +h * 1.4 — KRX 영업일 추정. Phase 4 단순화: base_date + h 영업일은
+   ohlcv 상의 다음 h 거래일이 아닌, "거래일 카운트" 대신 단순 calendar+h 로 저장하고
+   매칭 잡에서 ohlcv_daily 에 그 날짜 행이 있는지로 자연 보정.)
+
+대안 정확도 위해: 매칭 잡은 "예측의 target_date 이 오늘"인 행을 그날 종가와 비교.
+calendar date 가 비거래일이면 매칭이 안 되니, 매칭 잡은 매일 실행되어 모일 때 처리.
+"""
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import asdict
+from datetime import date, datetime, timedelta, timezone
+from typing import Any
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+from app.models.ensemble import EnsemblePrediction, predict as ensemble_predict
+
+logger = logging.getLogger(__name__)
+
+KST = timezone(timedelta(hours=9))
+
+
+def _next_trading_target(base_date: date, horizon: int) -> date:
+    """base_date + horizon 거래일 (주말만 스킵, 공휴일은 무시 — 매칭잡이 자연 보정)."""
+    d = base_date
+    added = 0
+    while added < horizon:
+        d = d + timedelta(days=1)
+        if d.weekday() < 5:  # 0..4 = Mon..Fri
+            added += 1
+    return d
+
+
+def _last_trading_date(code: str) -> date | None:
+    eng = get_engine()
+    with eng.connect() as conn:
+        row = conn.execute(
+            text("SELECT MAX(date) FROM ohlcv_daily WHERE code = :c"),
+            {"c": code},
+        ).first()
+    return row[0] if row and row[0] else None
+
+
+def predict_and_store(
+    code: str,
+    *,
+    horizons: tuple[int, ...] = (1, 3, 5),
+    user_triggered: bool = True,
+) -> dict[str, Any]:
+    """앙상블 예측 실행 + predictions 테이블 적재. 결과 JSON-serializable dict 반환."""
+    base_date = _last_trading_date(code)
+    if base_date is None:
+        raise RuntimeError(f"no ohlcv_daily for {code}; refresh first")
+
+    pred: EnsemblePrediction = ensemble_predict(code, horizons=horizons)
+    now = datetime.now(KST)
+
+    eng = get_engine()
+    saved_ids: list[int] = []
+    with eng.begin() as conn:
+        for step in pred.steps:
+            target_date = _next_trading_target(base_date, step.horizon)
+            features_snap = {
+                "base_close": pred.base_close,
+                "sources_used": pred.sources_used,
+                "direction": step.direction,
+            }
+            row = conn.execute(
+                text(
+                    """
+                    INSERT INTO predictions
+                      (code, predicted_at, base_date, target_date, horizon, model,
+                       direction, prob_up, prob_flat, prob_down, expected_return,
+                       point_forecast, ci_low, ci_high, features_snapshot, user_triggered)
+                    VALUES
+                      (:code, :predicted_at, :base_date, :target_date, :horizon, 'ensemble',
+                       :direction, :p_up, :p_fl, :p_dn, :exp_ret,
+                       :point, :lo, :hi, CAST(:feats AS JSONB), :ut)
+                    ON CONFLICT (code, base_date, target_date, horizon, model)
+                    DO UPDATE SET
+                      predicted_at = EXCLUDED.predicted_at,
+                      direction    = EXCLUDED.direction,
+                      prob_up      = EXCLUDED.prob_up,
+                      prob_flat    = EXCLUDED.prob_flat,
+                      prob_down    = EXCLUDED.prob_down,
+                      expected_return = EXCLUDED.expected_return,
+                      point_forecast  = EXCLUDED.point_forecast,
+                      ci_low          = EXCLUDED.ci_low,
+                      ci_high         = EXCLUDED.ci_high,
+                      features_snapshot = EXCLUDED.features_snapshot,
+                      user_triggered = predictions.user_triggered OR EXCLUDED.user_triggered
+                    RETURNING id
+                    """
+                ),
+                {
+                    "code": code,
+                    "predicted_at": now,
+                    "base_date": base_date,
+                    "target_date": target_date,
+                    "horizon": step.horizon,
+                    "direction": step.direction,
+                    "p_up": step.prob_up,
+                    "p_fl": step.prob_flat,
+                    "p_dn": step.prob_down,
+                    "exp_ret": step.expected_return,
+                    "point": step.point_close,
+                    "lo": step.ci_low,
+                    "hi": step.ci_high,
+                    "feats": json.dumps(features_snap),
+                    "ut": user_triggered,
+                },
+            ).first()
+            if row:
+                saved_ids.append(int(row[0]))
+
+    return {
+        "code": code,
+        "base_date": str(base_date),
+        "base_close": pred.base_close,
+        "sources_used": pred.sources_used,
+        "steps": [
+            {
+                **asdict(s),
+                "target_date": str(_next_trading_target(base_date, s.horizon)),
+            }
+            for s in pred.steps
+        ],
+        "saved_prediction_ids": saved_ids,
+        "user_triggered": user_triggered,
+    }