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/models/lgbm.py

@@ -0,0 +1,180 @@
+"""LightGBM 회귀 + 분류 모델. 종목 × horizon 별 별도 저장.
+
+- 회귀: target = y_ret_h{H}.  예측 후 base_close*(1+pred) 로 가격 환산.
+- 분류: target = y_dir_h{H} ∈ {-1, 0, +1}. 3-class softmax 로 prob_up/flat/down.
+
+저장 경로: backend/data/models/{code}_h{H}_reg.pkl, _cls.pkl  (joblib).
+"""
+from __future__ import annotations
+
+import logging
+import os
+from dataclasses import dataclass
+from pathlib import Path
+
+import joblib
+import numpy as np
+import pandas as pd
+
+from app.models.features import build_features, feature_columns
+
+logger = logging.getLogger(__name__)
+
+MODEL_DIR = Path(os.environ.get("LGBM_MODEL_DIR", "/app/data/models"))
+
+
+@dataclass
+class LgbmForecast:
+    horizon: int
+    base_close: float
+    predicted_close: float
+    predicted_return: float
+    prob_up: float
+    prob_flat: float
+    prob_down: float
+
+
+def _model_paths(code: str, horizon: int) -> tuple[Path, Path]:
+    MODEL_DIR.mkdir(parents=True, exist_ok=True)
+    return (
+        MODEL_DIR / f"{code}_h{horizon}_reg.pkl",
+        MODEL_DIR / f"{code}_h{horizon}_cls.pkl",
+    )
+
+
+def _prepare_xy(code: str, horizon: int, lookback_days: int) -> tuple[pd.DataFrame, pd.Series, pd.Series, list[str]]:
+    ff = build_features(
+        code,
+        lookback_days=lookback_days,
+        horizons=(horizon,),
+        with_targets=True,
+    )
+    df = ff.df
+    if df.empty:
+        return df, pd.Series(dtype=float), pd.Series(dtype=int), []
+    y_ret_col = f"y_ret_h{horizon}"
+    y_dir_col = f"y_dir_h{horizon}"
+    # 타깃 NaN (마지막 H 행) 제거.
+    df = df.dropna(subset=[y_ret_col, y_dir_col])
+    feats = feature_columns(df)
+    if not feats:
+        return df, pd.Series(dtype=float), pd.Series(dtype=int), []
+    X = df[feats]
+    # LightGBM 은 NaN 자체 처리 가능.
+    y_ret = df[y_ret_col].astype(float)
+    y_dir = df[y_dir_col].astype(int)
+    return X, y_ret, y_dir, feats
+
+
+def train_one(code: str, horizon: int, *, lookback_days: int = 365 * 3) -> dict:
+    """1종목 × 1 horizon 학습. 저장된 모델 파일 경로 + 샘플 수 반환."""
+    import lightgbm as lgb
+
+    X, y_ret, y_dir, feats = _prepare_xy(code, horizon, lookback_days)
+    if X.empty or len(X) < 100:
+        return {"code": code, "horizon": horizon, "status": "skipped_too_few_rows", "n_rows": int(len(X))}
+
+    reg_params = dict(
+        objective="regression",
+        learning_rate=0.05,
+        num_leaves=31,
+        min_data_in_leaf=20,
+        feature_fraction=0.85,
+        bagging_fraction=0.8,
+        bagging_freq=5,
+        verbose=-1,
+    )
+    cls_params = dict(
+        objective="multiclass",
+        num_class=3,
+        learning_rate=0.05,
+        num_leaves=31,
+        min_data_in_leaf=20,
+        feature_fraction=0.85,
+        bagging_fraction=0.8,
+        bagging_freq=5,
+        verbose=-1,
+    )
+
+    # 분류는 -1/0/1 → 0/1/2 인덱스로 매핑.
+    y_dir_idx = (y_dir + 1).astype(int)
+
+    n = len(X)
+    split = int(n * 0.85)
+    X_tr, X_val = X.iloc[:split], X.iloc[split:]
+    yr_tr, yr_val = y_ret.iloc[:split], y_ret.iloc[split:]
+    yc_tr, yc_val = y_dir_idx.iloc[:split], y_dir_idx.iloc[split:]
+
+    reg_train = lgb.Dataset(X_tr, label=yr_tr)
+    reg_valid = lgb.Dataset(X_val, label=yr_val, reference=reg_train)
+    reg_model = lgb.train(
+        reg_params,
+        reg_train,
+        num_boost_round=400,
+        valid_sets=[reg_valid],
+        callbacks=[lgb.early_stopping(stopping_rounds=30, verbose=False)],
+    )
+
+    cls_train = lgb.Dataset(X_tr, label=yc_tr)
+    cls_valid = lgb.Dataset(X_val, label=yc_val, reference=cls_train)
+    cls_model = lgb.train(
+        cls_params,
+        cls_train,
+        num_boost_round=400,
+        valid_sets=[cls_valid],
+        callbacks=[lgb.early_stopping(stopping_rounds=30, verbose=False)],
+    )
+
+    reg_path, cls_path = _model_paths(code, horizon)
+    joblib.dump({"model": reg_model, "features": feats}, reg_path)
+    joblib.dump({"model": cls_model, "features": feats}, cls_path)
+
+    return {
+        "code": code,
+        "horizon": horizon,
+        "status": "ok",
+        "n_rows": int(len(X)),
+        "reg_best_iter": int(reg_model.best_iteration or 0),
+        "cls_best_iter": int(cls_model.best_iteration or 0),
+        "reg_path": str(reg_path),
+        "cls_path": str(cls_path),
+    }
+
+
+def predict_one(code: str, horizon: int, *, lookback_days: int = 400) -> LgbmForecast | None:
+    """1종목 × 1 horizon 추론. 모델 없으면 None.
+
+    가장 최신 영업일 피처를 사용. base_close 는 그 행의 close.
+    """
+    reg_path, cls_path = _model_paths(code, horizon)
+    if not reg_path.exists() or not cls_path.exists():
+        return None
+    reg_blob = joblib.load(reg_path)
+    cls_blob = joblib.load(cls_path)
+    feats_reg = reg_blob["features"]
+    feats_cls = cls_blob["features"]
+    reg_model = reg_blob["model"]
+    cls_model = cls_blob["model"]
+
+    ff = build_features(code, lookback_days=lookback_days, horizons=(horizon,), with_targets=False)
+    df = ff.df
+    if df.empty:
+        return None
+    last = df.iloc[[-1]]
+    base_close = float(last["close"].iloc[0])
+    # 피처 정렬 (모델이 학습 당시 본 컬럼 순서대로).
+    X_reg = last.reindex(columns=feats_reg).fillna(value=np.nan)
+    X_cls = last.reindex(columns=feats_cls).fillna(value=np.nan)
+    pred_ret = float(reg_model.predict(X_reg)[0])
+    probs = cls_model.predict(X_cls)[0]
+    # 인덱스 0=-1(down), 1=0(flat), 2=+1(up)
+    prob_down, prob_flat, prob_up = float(probs[0]), float(probs[1]), float(probs[2])
+    return LgbmForecast(
+        horizon=horizon,
+        base_close=base_close,
+        predicted_close=base_close * (1.0 + pred_ret),
+        predicted_return=pred_ret,
+        prob_up=prob_up,
+        prob_flat=prob_flat,
+        prob_down=prob_down,
+    )