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>

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,
     }