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>
2026-05-20 16:03:01 +09:00
parent b1ca6ab5d3
commit bf4fb01146
8 changed files with 896 additions and 2 deletions
--- a/backend/app/pipelines/match_outcomes.py
+++ b/backend/app/pipelines/match_outcomes.py
@@ -0,0 +1,165 @@
+"""prediction_outcomes 매칭 배치.
+
+평일 16:30 KST 에 실행. 다음 거래일 장 종료 후 (KRX 정규장 마감 15:30) 의
+확정 종가가 16:00~16:30 사이 pykrx 로 들어온 뒤, target_date == 오늘인
+user_triggered=TRUE 예측을 그 종가와 매칭.
+
+cold-start / 휴장일 대비: 인자로 받은 target_date 의 ohlcv_daily 에 종가가
+없으면 자연스럽게 skip. 다음 거래일 매칭 잡이 다시 시도하면 그 날짜는
+여전히 매칭되지 않으므로 (매칭 sql 이 target_date 기준), 영원히 매칭 안되는
+잘못된 calendar date 예측은 cleanup CLI 로 별도 정리 가능 (Phase 7).
+"""
+from __future__ import annotations
+
+import json
+import logging
+from dataclasses import dataclass
+from datetime import date, timedelta
+from typing import Any
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+logger = logging.getLogger(__name__)
+
+# direction_hit 판정 시 ±0.3% 이내는 flat. (features 의 FLAT_BAND 와 동일)
+FLAT_BAND = 0.003
+
+
+@dataclass
+class MatchSummary:
+    target_date: str
+    candidates: int
+    matched: int
+    skipped_no_actual: int
+    already_resolved: int
+
+
+def _direction_label(ret: float) -> str:
+    if ret > FLAT_BAND:
+        return "up"
+    if ret < -FLAT_BAND:
+        return "down"
+    return "flat"
+
+
+def match_for_date(d: date) -> MatchSummary:
+    """target_date == d 인 user_triggered=TRUE 예측을 매칭."""
+    eng = get_engine()
+    with eng.begin() as conn:
+        # 매칭 대상 예측 + 매칭 안 됐는지 확인.
+        candidate_rows = conn.execute(
+            text(
+                """
+                SELECT p.id, p.code, p.base_date, p.horizon, p.point_forecast,
+                       p.direction, p.model
+                  FROM predictions p
+             LEFT JOIN prediction_outcomes po ON po.prediction_id = p.id
+                 WHERE p.target_date = :d
+                   AND p.user_triggered = TRUE
+                   AND po.prediction_id IS NULL
+                """
+            ),
+            {"d": d},
+        ).all()
+        candidates = len(candidate_rows)
+        if not candidates:
+            return MatchSummary(str(d), 0, 0, 0, 0)
+
+        # 종목별로 actual close 조회 (한번에 batch).
+        codes = list({r[1] for r in candidate_rows})
+        actual_map: dict[tuple[str, date], float] = {}
+        for code in codes:
+            row = conn.execute(
+                text(
+                    "SELECT close FROM ohlcv_daily WHERE code = :c AND date = :d"
+                ),
+                {"c": code, "d": d},
+            ).first()
+            if row and row[0] is not None:
+                actual_map[(code, d)] = float(row[0])
+
+        # base_close (각 예측의 base_date 종가) 도 필요 — direction 판정용.
+        base_close_map: dict[tuple[str, date], float] = {}
+        for pid, code, base_date, *_ in candidate_rows:
+            key = (code, base_date)
+            if key in base_close_map:
+                continue
+            row = conn.execute(
+                text("SELECT close FROM ohlcv_daily WHERE code = :c AND date = :d"),
+                {"c": code, "d": base_date},
+            ).first()
+            if row and row[0] is not None:
+                base_close_map[key] = float(row[0])
+
+        matched = 0
+        skipped = 0
+        already = 0
+        for pid, code, base_date, horizon, point_forecast, pred_dir, model in candidate_rows:
+            actual = actual_map.get((code, d))
+            base_close = base_close_map.get((code, base_date))
+            if actual is None or base_close is None:
+                skipped += 1
+                continue
+            actual_ret = actual / base_close - 1.0
+            actual_dir = _direction_label(actual_ret)
+            dir_hit = (pred_dir == actual_dir)
+            abs_err = abs(float(point_forecast) - actual) if point_forecast is not None else None
+
+            try:
+                conn.execute(
+                    text(
+                        """
+                        INSERT INTO prediction_outcomes
+                          (prediction_id, code, target_date, horizon, model,
+                           predicted_close, actual_close, actual_return, direction_hit, abs_error)
+                        VALUES
+                          (:pid, :code, :d, :h, :m, :pc, :ac, :ar, :dh, :ae)
+                        ON CONFLICT (prediction_id) DO NOTHING
+                        """
+                    ),
+                    {
+                        "pid": pid,
+                        "code": code,
+                        "d": d,
+                        "h": horizon,
+                        "m": model,
+                        "pc": float(point_forecast) if point_forecast is not None else None,
+                        "ac": actual,
+                        "ar": float(actual_ret),
+                        "dh": bool(dir_hit),
+                        "ae": float(abs_err) if abs_err is not None else None,
+                    },
+                )
+                matched += 1
+            except Exception as exc:  # noqa: BLE001
+                logger.warning("match insert failed pid=%s: %s", pid, exc)
+                already += 1
+
+        return MatchSummary(
+            target_date=str(d),
+            candidates=candidates,
+            matched=matched,
+            skipped_no_actual=skipped,
+            already_resolved=already,
+        )
+
+
+def match_today() -> dict[str, Any]:
+    """평일 16:30 KST 호출용. target_date == today (KST) 인 행 매칭."""
+    from datetime import datetime, timezone, timedelta as td
+
+    kst = timezone(td(hours=9))
+    today = datetime.now(kst).date()
+    summary = match_for_date(today)
+    return {
+        "today": str(today),
+        "summary": summary.__dict__,
+    }
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+    out = match_today()
+    print(json.dumps(out, ensure_ascii=False, indent=2, default=str))