feat(weights): shadow chronos/lgbm 예측 + ensemble_weights 자동 보정

- ensemble.predict() 가 chronos_raw / lgbm_raw 를 함께 반환
- predict_and_store() 가 매 호출마다 3종 행 적재:
    model='ensemble' (user_triggered=인자)
    model='chronos'  (user_triggered=FALSE, shadow)
    model='lgbm'     (user_triggered=FALSE, shadow)
- retrain_weekly.adjust_weights(): 최근 30일 prediction_outcomes 의
  chronos vs lgbm hit_rate 로 ensemble_weights upsert
    w_chronos = clamp(0.1, hr_c/(hr_c+hr_l), 0.9), w_lgbm = 1 - w_chronos
  모델별 표본 < 10 이면 기본값(0.6/0.4) 유지
- API 응답에 saved_shadow_ids 추가 (TS 타입도 동기화)
- README: 동작 모델 메모 섹션을 실제 구현과 일치하도록 갱신

리뷰어 지적 3번 (ensemble_weights 가 영원히 갱신 안됨, upsert_weights 미호출) 해결.

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

README.md

@@ -155,9 +155,13 @@ stock_chart_site/
 
 ## 동작 모델 메모
 
-- 예측 트리거: 사용자가 "예상차트 보기" 누른 종목에 대해 즉시 inference. 결과는 `predictions(user_triggered=TRUE)` 로 저장.
-- 매칭 배치: 다음 거래일 장 종료 후 (KRX 정규장 마감 15:30, 종가 확정 후 16:00 ~ 16:30 KST 사이) `user_triggered=TRUE` 인 예측 중 `target_date == 오늘 거래일`인 행들에 대해 실제 종가/방향과 매칭 → `prediction_outcomes` 적재. 주말/공휴일이면 다음 거래일로 이월.
-- 주간 02:00 (일요일): 종목/모델별 최근 30일 hit rate 기반으로 앙상블 가중치를 자동 보정. hit rate가 임계 미만이면 LGBM 재학습.
+- 예측 트리거: 사용자가 "예상차트 보기" 누른 종목에 대해 즉시 inference. 결과는 세 종류 행으로 적재:
+  - `model='ensemble'` (user_triggered=TRUE) — UI 가 표시하는 최종 예측
+  - `model='chronos'`  (user_triggered=FALSE, shadow) — Chronos 단독 성능 추적용
+  - `model='lgbm'`     (user_triggered=FALSE, shadow) — LGBM 단독 성능 추적용
+- 매칭 배치: 평일 16:30 KST. `target_date <= today AND outcomes 미존재` 인 모든 행에 대해 `target_date` 이상 `today` 이하 범위의 **최초 거래일 종가**를 actual_close 로 사용 → 주말/공휴일 자동 이월. shadow 행도 함께 매칭됨.
+- 주간 02:00 (일요일): 시드 10종목 × horizons LGBM 재학습. 최근 30일 prediction_outcomes 의 chronos vs lgbm hit_rate 비교 → `w_chronos = clamp(0.1, hr_c/(hr_c+hr_l), 0.9)` 공식으로 `ensemble_weights` upsert. 모델별 표본이 10 미만이면 기본값(0.6/0.4) 유지.
+- DB bootstrap: 백엔드 첫 부팅 시 lifespan 에서 idempotent migration + symbols 시드(비어있을 때만 pykrx 전 종목 적재) 자동 수행. `BOOTSTRAP_DISABLED=1` 로 비활성화 가능.
 
 ## 안전/한계
 

backend/app/main.py

@@ -1,10 +1,12 @@
 from __future__ import annotations
 
 import logging
+import os
 from contextlib import asynccontextmanager
 
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
+from sqlalchemy import text
 
 from app.api.chart import router as chart_router
 from app.api.metrics import router as metrics_router
@@ -13,7 +15,7 @@ from app.api.predict import router as predict_router
 from app.api.refresh import router as refresh_router
 from app.api.symbols import router as symbols_router
 from app.config import settings
-from app.db.connection import ping as db_ping
+from app.db.connection import get_engine, ping as db_ping
 from app.fetch import dart as dart_mod
 from app.fetch import kis as kis_mod
 from app.pipelines.scheduler import shutdown_scheduler, start_scheduler
@@ -25,13 +27,55 @@ logging.basicConfig(
 logger = logging.getLogger(__name__)
 
 
+def _bootstrap_db() -> None:
+    """첫 부팅 자동화:
+      1) migrations/*.sql idempotent 적용 (timescale/pgvector 확장 + 스키마)
+      2) symbols 테이블 비어있으면 pykrx 로 전 종목 시드 (SEED 10 마크 포함)
+
+    BOOTSTRAP_DISABLED=1 이면 스킵 (테스트/CI 용). 어떤 단계든 실패해도 서버는
+    뜬다 — /health/db 가 진단을 알려준다.
+    """
+    if os.environ.get("BOOTSTRAP_DISABLED") == "1":
+        logger.info("bootstrap skipped (BOOTSTRAP_DISABLED=1)")
+        return
+
+    # 1) migrations
+    try:
+        from app.db.migrate import apply_all
+        res = apply_all()
+        logger.info("bootstrap migrate: %s", res)
+    except Exception:  # noqa: BLE001
+        logger.exception("bootstrap migrate failed")
+        return  # 스키마 없으면 시드 불가
+
+    # 2) symbols 시드 (비어있을 때만 — pykrx 호출이 비싸므로 항상 돌리지 않음)
+    try:
+        eng = get_engine()
+        with eng.connect() as conn:
+            row = conn.execute(text("SELECT COUNT(*) FROM symbols")).first()
+            count = int(row[0]) if row else 0
+        if count == 0:
+            logger.info("symbols empty — running initial seed")
+            from app.fetch.symbols_seed import seed_symbols
+            report = seed_symbols()
+            logger.info("bootstrap seed_symbols: %s", report)
+        else:
+            logger.info("symbols already populated (count=%d) — skip seed", count)
+    except Exception:  # noqa: BLE001
+        logger.exception("bootstrap seed_symbols failed")
+
+
 @asynccontextmanager
 async def lifespan(_: FastAPI):
+    _bootstrap_db()
     # 스케줄러는 옵션. CI/테스트에서 disable 하고 싶으면 SCHEDULER_DISABLED 같은 env 추가 가능.
-    try:
-        start_scheduler()
-    except Exception:  # noqa: BLE001
-        logger.exception("scheduler start failed")
+    if os.environ.get("SCHEDULER_DISABLED") == "1":
+        logger.info("scheduler skipped (SCHEDULER_DISABLED=1)")
+    else:
+        try:
+            start_scheduler()
+        except Exception:  # noqa: BLE001
+            logger.exception("scheduler start failed")
     yield
     shutdown_scheduler()
 

backend/app/models/ensemble.py

@@ -19,7 +19,7 @@ Chronos 도 실패하면 LGBM 단독으로 진행.
 from __future__ import annotations
 
 import logging
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 
 import numpy as np
 
@@ -53,6 +53,10 @@ class EnsemblePrediction:
     horizons: list[int]
     steps: list[EnsembleStep]
     sources_used: list[str]
+    # shadow 저장용 원본 출력 (predict_one.py 가 ensemble + chronos 단독 + lgbm 단독
+    # 3 종을 predictions 에 적재해서 retrain_weekly 가 모델별 hit_rate 비교 가능하게 함).
+    chronos_raw: ChronosForecast | None = None
+    lgbm_raw: dict[int, LgbmForecast] = field(default_factory=dict)
 
 
 def _chronos_direction(samples: list[list[float]], base_close: float, horizon: int) -> tuple[float, float, float]:
@@ -90,12 +94,14 @@ def predict(code: str, *, horizons: tuple[int, ...] = (1, 3, 5)) -> EnsemblePred
         logger.warning("chronos forecast failed for %s: %s", code, exc)
 
     steps: list[EnsembleStep] = []
+    lgbm_raw: dict[int, LgbmForecast] = {}
     for h in horizons:
         lf: LgbmForecast | None = None
         try:
             lf = lgbm_predict(code, h)
             if lf is not None:
                 sources_used.append(f"lgbm_h{h}")
+                lgbm_raw[h] = lf
         except Exception as exc:  # noqa: BLE001
             logger.warning("lgbm predict failed for %s h=%d: %s", code, h, exc)
 
@@ -171,4 +177,6 @@ def predict(code: str, *, horizons: tuple[int, ...] = (1, 3, 5)) -> EnsemblePred
         horizons=list(horizons),
         steps=steps,
         sources_used=sources_used,
+        chronos_raw=cf,
+        lgbm_raw=lgbm_raw,
     )

backend/app/pipelines/match_outcomes.py

@@ -1,13 +1,16 @@
 """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.
+확정 종가가 16:00~16:30 사이 pykrx 로 들어온 뒤, 매칭 미해결 예측을 실제
+종가와 매칭한다.
 
-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).
+이월/공휴일 정책:
+  target_date 가 calendar date 라서 비거래일이면 ohlcv_daily 에 행이 없다.
+  그래서 `target_date <= today` 인 미해결 행을 전부 후보로 잡고, 각 행마다
+  `target_date <= ohlcv_daily.date <= today` 범위의 최초 거래일 종가로
+  매칭한다 (=다음 거래일로 자동 이월).
+
+shadow prediction 도 같은 방식으로 매칭한다 (user_triggered 필터 없음).
 """
 from __future__ import annotations
 
@@ -29,7 +32,7 @@ FLAT_BAND = 0.003
 
 @dataclass
 class MatchSummary:
-    target_date: str
+    today: str
     candidates: int
     matched: int
     skipped_no_actual: int
@@ -44,64 +47,62 @@ def _direction_label(ret: float) -> str:
     return "flat"
 
 
-def match_for_date(d: date) -> MatchSummary:
-    """target_date == d 인 user_triggered=TRUE 예측을 매칭."""
+def match_up_to(today: date) -> MatchSummary:
+    """target_date <= today 인 모든 미해결 예측을 매칭.
+
+    각 행마다 ohlcv_daily 에서 target_date 이상, today 이하 범위의 최초
+    거래일 종가를 actual_close 로 사용 — 공휴일/주말 이월 자연 처리.
+    """
     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
+                SELECT p.id, p.code, p.base_date, p.target_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
+                 WHERE p.target_date <= :today
                    AND po.prediction_id IS NULL
                 """
             ),
-            {"d": d},
+            {"today": today},
         ).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])
+            return MatchSummary(str(today), 0, 0, 0, 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:
+        for pid, code, base_date, target_date, horizon, point_forecast, pred_dir, model in candidate_rows:
+            # 첫 거래일 종가 (target_date <= date <= today)
+            actual_row = conn.execute(
+                text(
+                    """
+                    SELECT date, close FROM ohlcv_daily
+                     WHERE code = :c AND date >= :td AND date <= :today
+                     ORDER BY date ASC
+                     LIMIT 1
+                    """
+                ),
+                {"c": code, "td": target_date, "today": today},
+            ).first()
+            if not actual_row or actual_row[1] is None:
                 skipped += 1
                 continue
+            actual_date = actual_row[0]
+            actual = float(actual_row[1])
+
+            base_close_row = conn.execute(
+                text("SELECT close FROM ohlcv_daily WHERE code = :c AND date = :d"),
+                {"c": code, "d": base_date},
+            ).first()
+            if not base_close_row or base_close_row[0] is None:
+                skipped += 1
+                continue
+            base_close = float(base_close_row[0])
+
             actual_ret = actual / base_close - 1.0
             actual_dir = _direction_label(actual_ret)
             dir_hit = (pred_dir == actual_dir)
@@ -122,7 +123,8 @@ def match_for_date(d: date) -> MatchSummary:
                     {
                         "pid": pid,
                         "code": code,
-                        "d": d,
+                        # 실제 매칭된 거래일 (이월된 경우 target_date 와 다를 수 있음)
+                        "d": actual_date,
                         "h": horizon,
                         "m": model,
                         "pc": float(point_forecast) if point_forecast is not None else None,
@@ -138,7 +140,7 @@ def match_for_date(d: date) -> MatchSummary:
                 already += 1
 
         return MatchSummary(
-            target_date=str(d),
+            today=str(today),
             candidates=candidates,
             matched=matched,
             skipped_no_actual=skipped,
@@ -146,13 +148,19 @@ def match_for_date(d: date) -> MatchSummary:
         )
 
 
+# 하위 호환 alias — 이전 시그니처를 쓰던 호출자 (예: 단일 날짜 매칭 테스트)
+def match_for_date(d: date) -> MatchSummary:
+    """legacy: target_date == d 만 매칭하던 동작 → 이제 target_date <= d 전체 처리."""
+    return match_up_to(d)
+
+
 def match_today() -> dict[str, Any]:
-    """평일 16:30 KST 호출용. target_date == today (KST) 인 행 매칭."""
+    """평일 16:30 KST 호출용. target_date <= today (KST) 인 미해결 행 일괄 매칭."""
     from datetime import datetime, timezone, timedelta as td
 
     kst = timezone(td(hours=9))
     today = datetime.now(kst).date()
-    summary = match_for_date(today)
+    summary = match_up_to(today)
     return {
         "today": str(today),
         "summary": summary.__dict__,

backend/app/pipelines/predict_one.py

@@ -3,12 +3,16 @@
 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.)
+  (주말만 스킵하는 단순 카운트. 공휴일은 match_outcomes 가 "target_date 이후
+   최초 거래일 종가"로 자동 이월하여 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.
+세 종류의 행을 함께 저장한다:
+  - model='ensemble'  : 사용자에게 보여주는 최종 예측. user_triggered 플래그 따라감.
+  - model='chronos'   : Chronos 단독 (shadow). user_triggered=FALSE 로 항상 적재.
+  - model='lgbm'      : LGBM 단독 (shadow). user_triggered=FALSE 로 항상 적재.
+
+shadow 행은 retrain_weekly 가 모델별 hit_rate 를 비교해 ensemble_weights 를
+자동 보정하는 입력이 된다.
 """
 from __future__ import annotations
 
@@ -27,6 +31,9 @@ logger = logging.getLogger(__name__)
 
 KST = timezone(timedelta(hours=9))
 
+# ±0.3% flat band — features.FLAT_BAND, match_outcomes.FLAT_BAND 와 동일.
+FLAT_BAND = 0.003
+
 
 def _next_trading_target(base_date: date, horizon: int) -> date:
     """base_date + horizon 거래일 (주말만 스킵, 공휴일은 무시 — 매칭잡이 자연 보정)."""
@@ -49,77 +56,194 @@ def _last_trading_date(code: str) -> date | None:
     return row[0] if row and row[0] else None
 
 
+def _direction_label(ret: float) -> str:
+    if ret > FLAT_BAND:
+        return "up"
+    if ret < -FLAT_BAND:
+        return "down"
+    return "flat"
+
+
+_INSERT_PREDICTION_SQL = 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, :model,
+       :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
+    """
+)
+
+
+def _insert_prediction(conn, *, model: str, code: str, predicted_at: datetime,
+                       base_date: date, target_date: date, horizon: int,
+                       direction: str, p_up: float, p_fl: float, p_dn: float,
+                       expected_return: float, point: float | None,
+                       lo: float | None, hi: float | None,
+                       features_snap: dict, user_triggered: bool) -> int | None:
+    row = conn.execute(
+        _INSERT_PREDICTION_SQL,
+        {
+            "code": code,
+            "predicted_at": predicted_at,
+            "base_date": base_date,
+            "target_date": target_date,
+            "horizon": horizon,
+            "model": model,
+            "direction": direction,
+            "p_up": p_up,
+            "p_fl": p_fl,
+            "p_dn": p_dn,
+            "exp_ret": expected_return,
+            "point": point,
+            "lo": lo,
+            "hi": hi,
+            "feats": json.dumps(features_snap),
+            "ut": user_triggered,
+        },
+    ).first()
+    return int(row[0]) if row 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."""
+    """앙상블 예측 실행 + predictions 테이블 적재.
+
+    적재 행:
+      - 'ensemble' (user_triggered 인자 반영)
+      - 'chronos'  (shadow, user_triggered=FALSE) — Chronos 가 성공했을 때만
+      - 'lgbm'     (shadow, user_triggered=FALSE) — LGBM 이 성공한 horizon 만
+    """
     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)
+    base_close = pred.base_close
 
     eng = get_engine()
-    saved_ids: list[int] = []
+    saved_ids: dict[str, list[int]] = {"ensemble": [], "chronos": [], "lgbm": []}
     with eng.begin() as conn:
         for step in pred.steps:
             target_date = _next_trading_target(base_date, step.horizon)
+
+            # --- ensemble row ---
             features_snap = {
-                "base_close": pred.base_close,
+                "base_close": 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]))
+            pid = _insert_prediction(
+                conn,
+                model="ensemble",
+                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,
+                expected_return=step.expected_return,
+                point=step.point_close,
+                lo=step.ci_low,
+                hi=step.ci_high,
+                features_snap=features_snap,
+                user_triggered=user_triggered,
+            )
+            if pid is not None:
+                saved_ids["ensemble"].append(pid)
+
+            # --- chronos shadow row ---
+            cf = pred.chronos_raw
+            if cf is not None:
+                c_med = float(cf.median[step.horizon - 1])
+                c_q10 = float(cf.q10[step.horizon - 1])
+                c_q90 = float(cf.q90[step.horizon - 1])
+                # direction prob: chronos sample 분포
+                try:
+                    import numpy as np
+                    arr = np.array(cf.samples)[:, step.horizon - 1]
+                    ret = arr / base_close - 1.0
+                    cp_up = float((ret > FLAT_BAND).mean())
+                    cp_dn = float((ret < -FLAT_BAND).mean())
+                    cp_fl = max(0.0, 1.0 - cp_up - cp_dn)
+                except Exception:  # noqa: BLE001
+                    cp_up = cp_fl = cp_dn = 1.0 / 3.0
+                exp_ret_c = c_med / base_close - 1.0
+                c_dir = _direction_label(exp_ret_c)
+                pid_c = _insert_prediction(
+                    conn,
+                    model="chronos",
+                    code=code,
+                    predicted_at=now,
+                    base_date=base_date,
+                    target_date=target_date,
+                    horizon=step.horizon,
+                    direction=c_dir,
+                    p_up=cp_up,
+                    p_fl=cp_fl,
+                    p_dn=cp_dn,
+                    expected_return=exp_ret_c,
+                    point=c_med,
+                    lo=c_q10,
+                    hi=c_q90,
+                    features_snap={"shadow": True, "base_close": base_close},
+                    user_triggered=False,
+                )
+                if pid_c is not None:
+                    saved_ids["chronos"].append(pid_c)
+
+            # --- lgbm shadow row ---
+            lf = pred.lgbm_raw.get(step.horizon)
+            if lf is not None:
+                l_close = float(lf.predicted_close)
+                exp_ret_l = l_close / base_close - 1.0
+                l_dir = _direction_label(exp_ret_l)
+                pid_l = _insert_prediction(
+                    conn,
+                    model="lgbm",
+                    code=code,
+                    predicted_at=now,
+                    base_date=base_date,
+                    target_date=target_date,
+                    horizon=step.horizon,
+                    direction=l_dir,
+                    p_up=float(lf.prob_up),
+                    p_fl=float(lf.prob_flat),
+                    p_dn=float(lf.prob_down),
+                    expected_return=exp_ret_l,
+                    point=l_close,
+                    lo=l_close * 0.97,
+                    hi=l_close * 1.03,
+                    features_snap={"shadow": True, "base_close": base_close},
+                    user_triggered=False,
+                )
+                if pid_l is not None:
+                    saved_ids["lgbm"].append(pid_l)
 
     return {
         "code": code,
@@ -133,6 +257,11 @@ def predict_and_store(
             }
             for s in pred.steps
         ],
-        "saved_prediction_ids": saved_ids,
+        # UI 는 ensemble id 만 본다. shadow 는 디버깅/검증용으로 별도 키.
+        "saved_prediction_ids": saved_ids["ensemble"],
+        "saved_shadow_ids": {
+            "chronos": saved_ids["chronos"],
+            "lgbm": saved_ids["lgbm"],
+        },
         "user_triggered": user_triggered,
     }

backend/app/pipelines/retrain_weekly.py

@@ -2,16 +2,20 @@
 
 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.)
+  2. 최근 30일 prediction_outcomes 의 (code, model, horizon) 별 hit_rate / mae
+     산출, model_performance 적재.
+  3. shadow 행 (model='chronos' / 'lgbm') 의 hit_rate 를 비교해서
+     ensemble_weights 자동 보정.
 
-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.
+가중치 공식:
+  w_c = clamp(0.1, hr_c / (hr_c + hr_l), 0.9)
+  w_l = 1 - w_c
+  단 sample_count_c < MIN_SAMPLE 또는 sample_count_l < MIN_SAMPLE 이면
+  기본값 유지 (DB row 미생성). hr_c + hr_l == 0 (둘 다 0%) 이면 50:50.
+
+predict_one 이 매 호출마다 chronos/lgbm shadow 행을 함께 적재하고
+match_outcomes 가 user_triggered 무관하게 매칭하므로, hit_rate 데이터는
+사용자가 예측을 한 번이라도 본 종목에 대해 자연스럽게 쌓인다.
 """
 from __future__ import annotations
 
@@ -24,12 +28,16 @@ from sqlalchemy import text
 
 from app.db.connection import get_engine
 from app.models.lgbm import train_one
+from app.models.weights import upsert_weights
 from app.seed.seed_tickers import SEED_TICKERS
 
 logger = logging.getLogger(__name__)
 
 HORIZONS = (1, 3, 5)
 WINDOW_DAYS = 30
+MIN_SAMPLE = 10  # 모델당 최소 매칭 표본
+W_CHRONOS_MIN = 0.1
+W_CHRONOS_MAX = 0.9
 
 
 def retrain_all() -> list[dict[str, Any]]:
@@ -103,6 +111,82 @@ def record_performance(as_of: date) -> list[dict[str, Any]]:
     return summary
 
 
+def adjust_weights(as_of: date) -> list[dict[str, Any]]:
+    """shadow chronos/lgbm hit_rate 로 ensemble_weights 자동 보정.
+
+    반환: (code, horizon, w_chronos, w_lgbm, hr_c, hr_l, n_c, n_l, action) 의
+    리스트. action ∈ {'updated', 'skipped_insufficient', 'skipped_zero'}.
+    """
+    eng = get_engine()
+    start = as_of - timedelta(days=WINDOW_DAYS)
+    out: list[dict[str, Any]] = []
+    with eng.begin() as conn:
+        rows = conn.execute(
+            text(
+                """
+                SELECT code, horizon, model,
+                       AVG(CASE WHEN direction_hit THEN 1.0 ELSE 0.0 END) AS hit_rate,
+                       COUNT(*) AS n
+                  FROM prediction_outcomes
+                 WHERE resolved_at >= :start
+                   AND model IN ('chronos', 'lgbm')
+                 GROUP BY code, horizon, model
+                """
+            ),
+            {"start": start},
+        ).all()
+
+        # (code, horizon) -> {'chronos': (hr, n), 'lgbm': (hr, n)}
+        agg: dict[tuple[str, int], dict[str, tuple[float, int]]] = {}
+        for code, horizon, model, hr, n in rows:
+            key = (code, int(horizon))
+            agg.setdefault(key, {})[str(model)] = (
+                float(hr) if hr is not None else 0.0,
+                int(n),
+            )
+
+        for (code, horizon), m in agg.items():
+            c = m.get("chronos")
+            l = m.get("lgbm")
+            if c is None or l is None:
+                out.append({
+                    "code": code, "horizon": horizon,
+                    "action": "skipped_missing_model",
+                    "have": list(m.keys()),
+                })
+                continue
+            hr_c, n_c = c
+            hr_l, n_l = l
+            if n_c < MIN_SAMPLE or n_l < MIN_SAMPLE:
+                out.append({
+                    "code": code, "horizon": horizon,
+                    "hr_chronos": hr_c, "hr_lgbm": hr_l,
+                    "n_chronos": n_c, "n_lgbm": n_l,
+                    "action": "skipped_insufficient",
+                })
+                continue
+            total = hr_c + hr_l
+            if total <= 0:
+                w_c = 0.5
+            else:
+                w_c = hr_c / total
+            w_c = max(W_CHRONOS_MIN, min(W_CHRONOS_MAX, w_c))
+            w_l = 1.0 - w_c
+            upsert_weights(
+                code, horizon, w_c, w_l,
+                hit_rate_chronos=hr_c, hit_rate_lgbm=hr_l,
+                sample_count=min(n_c, n_l),
+            )
+            out.append({
+                "code": code, "horizon": horizon,
+                "hr_chronos": hr_c, "hr_lgbm": hr_l,
+                "n_chronos": n_c, "n_lgbm": n_l,
+                "w_chronos": w_c, "w_lgbm": w_l,
+                "action": "updated",
+            })
+    return out
+
+
 def run_weekly() -> dict[str, Any]:
     """일요일 02:00 KST 호출 entry-point."""
     from datetime import datetime, timezone
@@ -113,6 +197,7 @@ def run_weekly() -> dict[str, Any]:
         "as_of": str(as_of),
         "trained": retrain_all(),
         "performance": record_performance(as_of),
+        "weights": adjust_weights(as_of),
     }
 
 

web/lib/api.ts

@@ -72,6 +72,7 @@ export type PredictResponse = {
   sources_used: string[];
   steps: PredictionStep[];
   saved_prediction_ids: number[];
+  saved_shadow_ids?: { chronos: number[]; lgbm: number[] };
   user_triggered: boolean;
 };