chore(gitignore): backend/data/ 추가 (LGBM 학습 체크포인트)

리뷰어가 호스트에서 backend/data/ 가 untracked 로 잡힌 걸 발견. LGBM
체크포인트는 런타임 산출물이라 커밋되면 안 됨.

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

.gitignore

@@ -20,6 +20,7 @@ build/
 # Models / artifacts (downloaded HF caches, trained LGBM)
 backend/artifacts/
 backend/.cache/
+backend/data/
 .huggingface/
 
 # Node

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/Dockerfile

@@ -7,27 +7,55 @@ ENV DEBIAN_FRONTEND=noninteractive \
     PYTHONDONTWRITEBYTECODE=1 \
     PIP_NO_CACHE_DIR=1 \
     PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    PYTHONPATH=/app \
     TZ=Asia/Seoul
 
+# Ubuntu 22.04 의 python3-pip 는 python3.10 을 가리키므로 설치하지 않고,
+# python3.11 + get-pip.py 로 3.11 전용 pip 를 부트스트랩한다.
+# (Debian/Ubuntu 의 시스템 python 은 ensurepip 가 막혀 있어 get-pip.py 가 가장 깔끔함.)
+# 이후 모든 호출은 `python -m pip` 로 통일해 인터프리터/스크립트 불일치를 차단.
 RUN apt-get update && apt-get install -y --no-install-recommends \
-    python3.11 python3.11-venv python3-pip \
+    python3.11 python3.11-venv \
     build-essential git curl ca-certificates tzdata \
     libgomp1 \
     && ln -sf /usr/bin/python3.11 /usr/local/bin/python \
     && ln -sf /usr/bin/python3.11 /usr/local/bin/python3 \
+    && curl -sSL https://bootstrap.pypa.io/get-pip.py -o /tmp/get-pip.py \
+    && python /tmp/get-pip.py \
+    && rm /tmp/get-pip.py \
+    && python -m pip install --upgrade pip wheel \
+    && python -m pip install "setuptools<80" \
     && rm -rf /var/lib/apt/lists/*
+# setuptools 80+ 은 pkg_resources 모듈을 제거함. pykrx 가 `import pkg_resources` 를
+# 하므로 80 미만으로 핀. 아래 reqs.txt 단계에서 다른 deps 가 setuptools 재upgrade 를
+# 트리거하지 않도록 별도 명령으로 고정.
+
+# Sanity check: 이 출력은 빌드 로그에 박혀서 다음에 인터프리터 불일치 의심될 때 즉시 확인 가능.
+RUN python -V && python -m pip -V
 
 WORKDIR /app
 
 COPY pyproject.toml ./
+
 # Install PyTorch (CUDA 12.1 wheels) first so the rest of deps don't downgrade it.
-RUN pip install --extra-index-url https://download.pytorch.org/whl/cu121 \
+RUN python -m pip install --extra-index-url https://download.pytorch.org/whl/cu121 \
         torch==2.3.1 torchvision==0.18.1
-RUN pip install --no-deps -e . || true
-RUN pip install -e .
+
+# Install runtime deps from pyproject.toml WITHOUT installing the project itself.
+# - 이전 `pip install -e .` 은 app/ 가 아직 COPY 되기 전이라 packages.find 결과가 비고,
+#   ubuntu 22.04 기본 pip 의 PEP 660 editable hook 과 충돌해 실패했음.
+# - 런타임에는 PYTHONPATH=/app 으로 `app.*` 임포트가 동작하므로 프로젝트 설치 자체가 불필요.
+# - deps 만 별도 레이어로 캐시 → 코드 변경 시 ML 휠 재빌드 회피.
+RUN python -c "import tomllib; \
+deps = tomllib.load(open('pyproject.toml','rb'))['project']['dependencies']; \
+open('/tmp/reqs.txt','w').write('\n'.join(deps))" \
+    && python -m pip install -r /tmp/reqs.txt \
+    && python -m pip install "setuptools<80" \
+    && python -c "import pkg_resources; print('pkg_resources OK from', pkg_resources.__file__)"
 
 COPY app ./app
 
 EXPOSE 8000
 
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]
+# uvicorn 콘솔 스크립트 대신 `python -m uvicorn` 으로 호출 — 3.11 인터프리터에서 실행됨을 보장.
+CMD ["python", "-m", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--reload"]

backend/app/api/chart.py

@@ -1,26 +1,221 @@
 """차트 데이터 API: OHLCV + 보조 데이터 (감성, 거시).
 
-UI: /code 페이지 첫 로드 시 호출 → lightweight-charts 캔들 데이터로 사용.
+UI: /code 페이지가 호출 → lightweight-charts 캔들 데이터로 사용.
+
+interval 파라미터로 캔들 단위 선택:
+  - "10m" : 당일 10분봉. ohlcv_1m 을 time_bucket 으로 10분 단위 집계.
+            stale (>10분) 이면 KIS inquire-time-itemchartprice 로 즉시 보충.
+  - "1d"  : 일봉. ohlcv_daily 직접 조회. 비어있으면 pykrx auto-refresh.
+  - "1w"  : 주봉. ohlcv_daily 를 date_trunc('week') 로 집계.
+  - "1mo" : 월봉. ohlcv_daily 를 date_trunc('month') 로 집계.
+
+10m 외에는 date 필드가 'YYYY-MM-DD' ISO date 문자열,
+10m 일 때는 'YYYY-MM-DDTHH:MM:SS' ISO datetime (KST) 으로 통일.
 """
 from __future__ import annotations
 
-from datetime import date, timedelta
+import logging
+from datetime import date, datetime, time as dtime, timedelta, timezone
 
 from fastapi import APIRouter, HTTPException, Query
 from sqlalchemy import text
 
 from app.db.connection import get_engine
 
+logger = logging.getLogger(__name__)
+
 router = APIRouter(prefix="/api/chart", tags=["chart"])
 
+ALLOWED_INTERVALS = ("10m", "1d", "1w", "1mo")
+KST = timezone(timedelta(hours=9))
+
+
+def _query_ohlcv_daily(conn, code: str, start: date, end: date):
+    return conn.execute(
+        text(
+            """
+            SELECT date, open, high, low, close, volume
+              FROM ohlcv_daily
+             WHERE code = :c AND date BETWEEN :s AND :e
+             ORDER BY date
+            """
+        ),
+        {"c": code, "s": start, "e": end},
+    ).all()
+
+
+def _query_ohlcv_bucketed(conn, code: str, start: date, end: date, trunc: str):
+    """1d → 1w/1mo 집계. date_trunc 로 bucket 잡고, 첫/마지막/최고/최저/합 집계.
+
+    open=bucket 첫 거래일 시가, close=마지막 거래일 종가. PostgreSQL window 함수로 구한다.
+    """
+    return conn.execute(
+        text(
+            f"""
+            WITH base AS (
+              SELECT date_trunc(:trunc, date)::date AS bucket,
+                     date, open, high, low, close, volume
+                FROM ohlcv_daily
+               WHERE code = :c AND date BETWEEN :s AND :e
+            ),
+            ranked AS (
+              SELECT bucket, date, open, high, low, close, volume,
+                     ROW_NUMBER() OVER (PARTITION BY bucket ORDER BY date ASC)  AS rn_first,
+                     ROW_NUMBER() OVER (PARTITION BY bucket ORDER BY date DESC) AS rn_last
+                FROM base
+            )
+            SELECT bucket AS date,
+                   MAX(open) FILTER (WHERE rn_first = 1)  AS open,
+                   MAX(high)                              AS high,
+                   MIN(low)                               AS low,
+                   MAX(close) FILTER (WHERE rn_last = 1)  AS close,
+                   SUM(volume)                            AS volume
+              FROM ranked
+             GROUP BY bucket
+             ORDER BY bucket
+            """
+        ),
+        {"c": code, "s": start, "e": end, "trunc": trunc},
+    ).all()
+
+
+def _query_ohlcv_10m(conn, code: str, start_ts: datetime, end_ts: datetime):
+    """ohlcv_1m → 10분봉. TimescaleDB time_bucket 으로 10분 단위 집계.
+
+    first()/last() 는 TimescaleDB 의 집계함수.
+    """
+    return conn.execute(
+        text(
+            """
+            SELECT time_bucket(INTERVAL '10 minutes', ts) AS bucket,
+                   first(open,  ts) AS open,
+                   MAX(high)        AS high,
+                   MIN(low)         AS low,
+                   last(close, ts)  AS close,
+                   SUM(volume)      AS volume
+              FROM ohlcv_1m
+             WHERE code = :c AND ts >= :s AND ts < :e
+             GROUP BY bucket
+             ORDER BY bucket
+            """
+        ),
+        {"c": code, "s": start_ts, "e": end_ts},
+    ).all()
+
+
+def _upsert_ohlcv_1m(conn, code: str, rows: list[dict]) -> int:
+    """KIS 분봉 응답을 ohlcv_1m 에 UPSERT. 같은 (code, ts) 는 덮어쓰기 (장중 갱신용)."""
+    if not rows:
+        return 0
+    conn.execute(
+        text(
+            """
+            INSERT INTO ohlcv_1m (code, ts, open, high, low, close, volume)
+            VALUES (:code, :ts, :open, :high, :low, :close, :volume)
+            ON CONFLICT (code, ts) DO UPDATE SET
+              open   = EXCLUDED.open,
+              high   = EXCLUDED.high,
+              low    = EXCLUDED.low,
+              close  = EXCLUDED.close,
+              volume = EXCLUDED.volume
+            """
+        ),
+        [{"code": code, **r} for r in rows],
+    )
+    return len(rows)
+
+
+def _intraday_window_today() -> tuple[datetime, datetime]:
+    """오늘 KST 의 장 시간대 윈도우 (08:50 ~ 16:00). 토/일은 직전 영업일."""
+    now = datetime.now(KST)
+    d = now.date()
+    # 주말이면 직전 금요일로
+    while d.weekday() >= 5:
+        d -= timedelta(days=1)
+    start = datetime.combine(d, dtime(8, 50), tzinfo=KST)
+    end = datetime.combine(d, dtime(16, 0), tzinfo=KST)
+    return start, end
+
+
+def _ensure_intraday_fresh(conn, code: str) -> str:
+    """오늘 윈도우의 ohlcv_1m 을 필요한 만큼만 KIS 에서 보충.
+
+    분기:
+      - 주말: KIS 분봉 endpoint 는 "당일" 만 지원 → 호출하지 않음. 'weekend'.
+      - 장외 (평일 09:00 이전 또는 15:35 이후) + 이미 오늘 데이터 있음: 'cached_closed'.
+        (마감 후엔 데이터 늘지 않으므로 KIS 호출 의미 없음)
+      - 장중 + last_ts 가 10분 이내: 'fresh' (DB 만 읽음)
+      - 그 외 (장중 stale / 장 막 끝나서 마지막 마감 데이터 1회 필요 / 캐시 비어있음):
+        last_ts+1m ~ now 사이의 빈 구간을 fetch_minute_range 로 페이지네이션 채움.
+        DB 캐시가 그날 데이터를 이미 갖고 있으면 자연히 호출 1~2 페이지로 끝.
+
+    Returns: 'fresh' | 'refreshed' | 'cached_closed' | 'weekend' |
+             'skipped_missing_key' | 'failed' | 'no_data'
+    """
+    now = datetime.now(KST)
+    if now.weekday() >= 5:
+        return "weekend"
+
+    win_start, win_end = _intraday_window_today()
+    last_ts = conn.execute(
+        text(
+            "SELECT MAX(ts) FROM ohlcv_1m WHERE code = :c AND ts >= :s AND ts < :e"
+        ),
+        {"c": code, "s": win_start, "e": win_end},
+    ).scalar()
+
+    market_open = dtime(9, 0)
+    market_close_buffer = dtime(15, 35)
+    in_session = market_open <= now.time() <= market_close_buffer
+
+    # 장외이고 이미 오늘 데이터 있음 → 추가 호출 불필요
+    if not in_session and last_ts is not None:
+        return "cached_closed"
+
+    # 장중 + 10분 이내 갱신 → 추가 호출 불필요
+    if in_session and last_ts is not None and (now - last_ts) < timedelta(minutes=10):
+        return "fresh"
+
+    # fetch 윈도우 = [last_ts+1m or win_start, min(now, win_end)]
+    fetch_to = min(now, win_end)
+    if last_ts is not None and last_ts >= win_start:
+        fetch_from = last_ts + timedelta(minutes=1)
+    else:
+        fetch_from = win_start
+    if fetch_from >= fetch_to:
+        return "fresh"
+
+    try:
+        from app.fetch.kis import SkippedMissingKey, fetch_minute_range
+    except Exception:  # noqa: BLE001
+        return "failed"
+
+    try:
+        rows = fetch_minute_range(code, fetch_from, fetch_to)
+    except SkippedMissingKey:
+        return "skipped_missing_key"
+    except Exception:  # noqa: BLE001
+        logger.exception("intraday refresh failed for %s", code)
+        return "failed"
+
+    if not rows:
+        return "no_data"
+    _upsert_ohlcv_1m(conn, code, rows)
+    conn.commit()
+    return "refreshed"
+
 
 @router.get("/{code}")
 def get_chart(
     code: str,
-    days: int = Query(default=180, ge=10, le=3650),
+    days: int = Query(default=180, ge=1, le=3650),
+    interval: str = Query(default="1d"),
     include_sentiment: bool = Query(default=True),
     include_trading_value: bool = Query(default=True),
 ) -> dict:
+    if interval not in ALLOWED_INTERVALS:
+        raise HTTPException(status_code=400, detail=f"interval must be one of {ALLOWED_INTERVALS}")
+
     eng = get_engine()
     end = date.today()
     start = end - timedelta(days=days)
@@ -32,17 +227,44 @@ def get_chart(
         if not symbol:
             raise HTTPException(status_code=404, detail=f"unknown code: {code}")
 
-        ohlcv_rows = conn.execute(
-            text(
-                """
-                SELECT date, open, high, low, close, volume
-                  FROM ohlcv_daily
-                 WHERE code = :c AND date BETWEEN :s AND :e
-                 ORDER BY date
-                """
-            ),
-            {"c": code, "s": start, "e": end},
-        ).all()
+        ohlcv: list[dict] = []
+        intraday_status: str | None = None
+
+        if interval == "10m":
+            intraday_status = _ensure_intraday_fresh(conn, code)
+            win_start, win_end = _intraday_window_today()
+            rows = _query_ohlcv_10m(conn, code, win_start, win_end)
+            ohlcv = [
+                {
+                    # KST aware datetime → ISO datetime. 프론트에서 Date 파싱.
+                    "date": (r[0].astimezone(KST) if r[0].tzinfo else r[0].replace(tzinfo=KST))
+                            .strftime("%Y-%m-%dT%H:%M:%S"),
+                    "open": float(r[1]) if r[1] is not None else None,
+                    "high": float(r[2]) if r[2] is not None else None,
+                    "low": float(r[3]) if r[3] is not None else None,
+                    "close": float(r[4]) if r[4] is not None else None,
+                    "volume": int(r[5]) if r[5] is not None else None,
+                }
+                for r in rows
+            ]
+        else:
+            if interval == "1d":
+                rows = _query_ohlcv_daily(conn, code, start, end)
+            elif interval == "1w":
+                rows = _query_ohlcv_bucketed(conn, code, start, end, "week")
+            else:  # "1mo"
+                rows = _query_ohlcv_bucketed(conn, code, start, end, "month")
+
+            if not rows and interval == "1d":
+                # 첫 방문 → pykrx auto-refresh.
+                try:
+                    from app.pipelines.refresh_one import refresh_code
+                    logger.info("chart: ohlcv_daily empty for %s — auto-refresh", code)
+                    refresh_code(symbol[0], symbol[1], lookback_days=max(days, 365))
+                    rows = _query_ohlcv_daily(conn, code, start, end)
+                except Exception:  # noqa: BLE001
+                    logger.exception("chart: auto-refresh failed for %s", code)
+
             ohlcv = [
                 {
                     "date": str(r[0]),
@@ -52,11 +274,11 @@ def get_chart(
                     "close": float(r[4]) if r[4] is not None else None,
                     "volume": int(r[5]) if r[5] is not None else None,
                 }
-            for r in ohlcv_rows
+                for r in rows
             ]
 
         sentiment: list[dict] = []
-        if include_sentiment:
+        if include_sentiment and interval != "10m":
             try:
                 s_rows = conn.execute(
                     text(
@@ -79,11 +301,10 @@ def get_chart(
                     for r in s_rows
                 ]
             except Exception:  # noqa: BLE001
-                # v_sentiment_daily 뷰 아직 없을 수 있음 (마이그레이션 미실행)
                 sentiment = []
 
         trading: list[dict] = []
-        if include_trading_value:
+        if include_trading_value and interval != "10m":
             tv_rows = conn.execute(
                 text(
                     """
@@ -109,7 +330,10 @@ def get_chart(
         "code": symbol[0],
         "name": symbol[1],
         "market": symbol[2],
+        "interval": interval,
+        "intraday_status": intraday_status,
         "range": {"from": str(start), "to": str(end)},
+        "today": date.today().isoformat(),
         "ohlcv": ohlcv,
         "sentiment": sentiment,
         "trading_value": trading,

backend/app/api/refresh.py

@@ -4,6 +4,10 @@ POST /api/refresh/{code}
   body: 없음
   query: ?lookback_days=7 (기본)
   resp:  refresh_one.RefreshReport.to_dict()
+
+POST /api/refresh/seed/symbols
+  symbols 테이블 강제 재시드 (SEED 10 + KRX 전 종목). 부팅 시 시드가 실패한
+  경우 컨테이너 재기동 없이 복구하기 위한 admin 엔드포인트.
 """
 from __future__ import annotations
 
@@ -11,6 +15,7 @@ from fastapi import APIRouter, HTTPException, Query
 from sqlalchemy import text
 
 from app.db.connection import get_engine
+from app.fetch.symbols_seed import seed_symbols
 from app.pipelines.refresh_one import refresh_code
 
 router = APIRouter(prefix="/api", tags=["refresh"])
@@ -33,3 +38,35 @@ def refresh_endpoint(
         raise HTTPException(status_code=404, detail=f"unknown code: {code} (symbols 테이블에 없음. 시드 필요)")
     report = refresh_code(code, name, lookback_days=lookback_days)
     return report.to_dict()
+
+
+@router.post("/refresh/seed/symbols")
+def reseed_symbols() -> dict:
+    """symbols 테이블 강제 재시드.
+
+    호출 예 (Windows cmd):
+        curl -X POST http://localhost:8000/api/refresh/seed/symbols
+
+    KRX 가 주말/장 마감 시간에 비정상 응답을 줄 때도 SEED 10 종목은 항상 보장하므로
+    엔드포인트는 200 을 돌려준다. 부분 성공 정보는 응답 body 에 담아 사용자가 판단.
+    """
+    try:
+        report = seed_symbols()
+        return {
+            "ok": True,
+            "inserted": report.inserted,
+            "updated": report.updated,
+            "seed_marked": report.seed_marked,
+            "markets": report.markets,
+        }
+    except Exception as e:  # noqa: BLE001
+        # seed_symbols 내부에서 다 잡지만, 만에 하나 외부로 새는 예외 (logger 포매터
+        # 자체 버그 등) 도 200 으로 흡수해서 SEED 10 만이라도 살리는 게 UX 목표.
+        return {
+            "ok": False,
+            "inserted": 0,
+            "updated": 0,
+            "seed_marked": 0,
+            "markets": {},
+            "error": repr(e)[:300],
+        }

backend/app/fetch/kis.py

@@ -13,11 +13,14 @@ status='skipped_missing_key' 처리.
 """
 from __future__ import annotations
 
+import json
 import logging
+import os
 import threading
 import time
 from dataclasses import dataclass
 from datetime import date, datetime
+from pathlib import Path
 from typing import Any
 
 import httpx
@@ -30,6 +33,16 @@ logger = logging.getLogger(__name__)
 KIS_BASE = "https://openapi.koreainvestment.com:9443"
 USER_AGENT = "stock_chart_site/0.1 (+personal)"
 
+# 토큰 디스크 캐시 경로. 기본값은 컨테이너 안 /app/.cache/kis_token.json — docker-compose
+# 의 `./backend:/app` 바인드 마운트 덕에 호스트 `./backend/.cache/` 에 영속된다.
+# `backend/.cache/` 는 .gitignore 에 들어있어 secrets 가 커밋되지 않는다.
+#
+# 왜 디스크 캐시가 필요한가:
+#   KIS 는 access_token 발급을 1분 1회, 하루 N회로 강하게 제한한다. 메모리만 쓰면
+#   `restart.bat` / `build.bat` / 컨테이너 재기동 때마다 새 발급 → 403 (EGW00133 등) 빈발.
+#   토큰 자체는 24시간 유효하므로, 컨테이너 인스턴스가 바뀌어도 같은 토큰을 재사용한다.
+_TOKEN_CACHE_PATH = Path(os.environ.get("KIS_TOKEN_CACHE_PATH", "/app/.cache/kis_token.json"))
+
 
 class SkippedMissingKey(RuntimeError):
     """KIS 키 미설정 시 발생. 호출 측에서 skipped 로 매핑."""
@@ -49,6 +62,54 @@ def _has_keys() -> bool:
     return bool(settings.kis_app_key and settings.kis_app_secret)
 
 
+def _current_key_prefix() -> str:
+    # app_key 가 바뀌었는데 옛 키로 받은 토큰을 그대로 쓰면 401. 캐시 무효화 키로 사용.
+    return (settings.kis_app_key or "")[:8]
+
+
+def _load_disk_cache() -> _Token | None:
+    try:
+        with _TOKEN_CACHE_PATH.open() as f:
+            data = json.load(f)
+        if data.get("key_prefix") != _current_key_prefix():
+            # .env 에서 app_key 가 바뀌었을 가능성 → 캐시 폐기
+            return None
+        tok = _Token(value=str(data["value"]), expires_at=float(data["expires_at"]))
+        if tok.expires_at <= time.time():
+            return None
+        return tok
+    except FileNotFoundError:
+        return None
+    except (OSError, ValueError, KeyError, TypeError) as exc:
+        logger.warning("kis token disk-cache read failed (%s): %s", _TOKEN_CACHE_PATH, exc)
+        return None
+
+
+def _save_disk_cache(tok: _Token) -> None:
+    try:
+        _TOKEN_CACHE_PATH.parent.mkdir(parents=True, exist_ok=True)
+        tmp = _TOKEN_CACHE_PATH.with_suffix(".json.tmp")
+        # atomic write: 부분 쓰기 중 컨테이너가 죽어도 다음 시작 시 깨진 파일 안 읽음
+        with tmp.open("w") as f:
+            json.dump(
+                {
+                    "value": tok.value,
+                    "expires_at": tok.expires_at,
+                    "key_prefix": _current_key_prefix(),
+                },
+                f,
+            )
+        os.replace(tmp, _TOKEN_CACHE_PATH)
+        # 토큰 파일은 키 동등의 secret. 0600 권한.
+        try:
+            os.chmod(_TOKEN_CACHE_PATH, 0o600)
+        except OSError:
+            pass
+    except OSError as exc:
+        # 캐시 쓰기 실패는 치명적이지 않음 — 메모리 캐시로만 동작 가능. 경고만.
+        logger.warning("kis token disk-cache write failed (%s): %s", _TOKEN_CACHE_PATH, exc)
+
+
 @retry(
     stop=stop_after_attempt(3),
     wait=wait_exponential(multiplier=1, min=1, max=8),
@@ -75,13 +136,29 @@ def _issue_token() -> _Token:
 
 
 def get_token() -> str:
-    """캐시된 토큰 반환. 만료 60초 전부터 재발급. 키 없으면 SkippedMissingKey."""
+    """캐시된 토큰 반환. 메모리 → 디스크 → 신규 발급 순. 키 없으면 SkippedMissingKey.
+
+    디스크 캐시는 컨테이너 재기동 시 토큰 재발급 1분 제한 (EGW00133) 회피용.
+    """
     global _token_cache
     with _token_lock:
         if _token_cache and _token_cache.expires_at > time.time():
             return _token_cache.value
+        disk = _load_disk_cache()
+        if disk is not None:
+            _token_cache = disk
+            logger.info(
+                "kis token loaded from disk, expires_at=%s",
+                datetime.fromtimestamp(disk.expires_at),
+            )
+            return disk.value
         _token_cache = _issue_token()
-        logger.info("kis token issued, expires_at=%s", datetime.fromtimestamp(_token_cache.expires_at))
+        _save_disk_cache(_token_cache)
+        logger.info(
+            "kis token issued (and cached to %s), expires_at=%s",
+            _TOKEN_CACHE_PATH,
+            datetime.fromtimestamp(_token_cache.expires_at),
+        )
         return _token_cache.value
 
 
@@ -156,6 +233,130 @@ def fetch_daily_price(
     return out
 
 
+@retry(
+    stop=stop_after_attempt(2),
+    wait=wait_exponential(multiplier=1, min=1, max=4),
+    retry=retry_if_exception_type((httpx.HTTPError, httpx.TimeoutException)),
+    reraise=True,
+)
+def fetch_minute_price(
+    code: str,
+    *,
+    end_hour: str | None = None,
+) -> list[dict[str, Any]]:
+    """당일 1분봉 시세 조회 (read-only). 최신 30개 캔들을 반환.
+
+    KIS 분봉 endpoint (`inquire-time-itemchartprice`) 는 base 시각 (FID_INPUT_HOUR_1) 부터
+    역순으로 최대 30개의 1분봉을 돌려준다. base 를 비우면 KIS 가 가장 최근 시각으로 해석.
+    즉 장중 호출 → 직전 30분 / 장 종료 후 호출 → 15:00~15:30 의 30분.
+
+    Returns: [{ts: datetime(KST aware), open, high, low, close, volume}, ...]
+            ts 오름차순 정렬.
+
+    Note: 이 endpoint 는 "당일" 분봉만 지원. 어제 이전 분봉은 별도 endpoint 가 필요한데,
+    이 사이트의 사용 패턴 (장중 라이브 차트) 에는 당일 데이터로 충분하다.
+    """
+    if not _has_keys():
+        raise SkippedMissingKey("kis app_key/secret missing")
+    url = f"{KIS_BASE}/uapi/domestic-stock/v1/quotations/inquire-time-itemchartprice"
+    params = {
+        "FID_ETC_CLS_CODE": "",
+        "FID_COND_MRKT_DIV_CODE": "J",
+        "FID_INPUT_ISCD": code,
+        # 비우면 KIS 가 "지금" 으로 해석. 장 마감 후엔 15:30:00 부근 데이터.
+        "FID_INPUT_HOUR_1": end_hour or "",
+        "FID_PW_DATA_INCU_YN": "Y",   # 과거 데이터 포함 (장 시작 직후 빈 데이터 방지)
+    }
+    with httpx.Client(timeout=15.0) as cli:
+        resp = cli.get(url, headers=_headers("FHKST03010200"), params=params)
+        resp.raise_for_status()
+        data = resp.json()
+    if data.get("rt_cd") != "0":
+        raise RuntimeError(f"kis error: {data.get('msg1')} (rt_cd={data.get('rt_cd')})")
+
+    # KIS 응답은 KST. tz-aware 로 변환해서 DB (TIMESTAMPTZ) 에 안전 적재.
+    from datetime import timedelta, timezone as _tz
+    KST = _tz(timedelta(hours=9))
+
+    out: list[dict[str, Any]] = []
+    for row in data.get("output2", []) or []:
+        raw_date = row.get("stck_bsop_date")
+        raw_hour = row.get("stck_cntg_hour")
+        if not raw_date or not raw_hour:
+            continue
+        try:
+            ts = datetime.strptime(raw_date + raw_hour.zfill(6), "%Y%m%d%H%M%S").replace(tzinfo=KST)
+        except ValueError:
+            continue
+        out.append(
+            {
+                "ts": ts,
+                "open": float(row.get("stck_oprc") or 0),
+                "high": float(row.get("stck_hgpr") or 0),
+                "low": float(row.get("stck_lwpr") or 0),
+                # 분봉에서는 종가가 stck_prpr (현재가) 로 옴
+                "close": float(row.get("stck_prpr") or row.get("stck_clpr") or 0),
+                "volume": int(row.get("cntg_vol") or 0),
+            }
+        )
+    # KIS 응답은 보통 최신→과거 역순. UI/DB 적재 편의를 위해 오름차순으로 뒤집는다.
+    out.sort(key=lambda r: r["ts"])
+    return out
+
+
+def fetch_minute_range(
+    code: str,
+    from_ts: datetime,
+    to_ts: datetime,
+    *,
+    max_pages: int = 20,
+) -> list[dict[str, Any]]:
+    """[from_ts, to_ts] 윈도우의 1분봉 전체. KIS 30-bar 페이지를 역순으로 반복 호출.
+
+    KIS `inquire-time-itemchartprice` 는 한 번에 최대 30개 1분봉만 주고,
+    `FID_INPUT_HOUR_1` 기준 그 시각 포함 이전 30분을 반환한다. 그래서 to_ts 부터
+    시작해서 가장 이른 응답 시각의 -1분을 다음 cursor 로 잡아 from_ts 까지 후퇴.
+
+    중복 키 (ts) 는 dict 로 자연 dedupe. 더 이상 새 행이 안 들어오거나 max_pages 도달
+    하면 종료 (rate-limit/무한루프 방지).
+
+    Note: 이 endpoint 는 "당일" 만 지원. from_ts/to_ts 는 같은 날짜여야 한다.
+    """
+    if not _has_keys():
+        raise SkippedMissingKey("kis app_key/secret missing")
+    if from_ts >= to_ts:
+        return []
+
+    from datetime import timedelta as _td
+
+    accumulated: dict[datetime, dict[str, Any]] = {}
+    cursor = to_ts
+    pages = 0
+    while cursor > from_ts and pages < max_pages:
+        pages += 1
+        rows = fetch_minute_price(code, end_hour=cursor.strftime("%H%M%S"))
+        if not rows:
+            break
+        added = 0
+        for r in rows:
+            ts = r["ts"]
+            if ts < from_ts or ts > to_ts:
+                continue
+            if ts not in accumulated:
+                accumulated[ts] = r
+                added += 1
+        if added == 0:
+            # 같은 30개를 또 받았다 — 더 과거가 없거나 KIS 가 똑같은 페이지를 반환.
+            break
+        earliest_ts = min(r["ts"] for r in rows)
+        next_cursor = earliest_ts - _td(minutes=1)
+        if next_cursor >= cursor:
+            break
+        cursor = next_cursor
+
+    return sorted(accumulated.values(), key=lambda r: r["ts"])
+
+
 def ping() -> dict[str, Any]:
     """토큰 발급만 시도해서 키 유효성 확인."""
     if not _has_keys():

backend/app/fetch/symbols_seed.py

@@ -41,21 +41,70 @@ def _fetch_market_listing(market: str) -> list[tuple[str, str]]:
     return out
 
 
+def _upsert_seed_tickers() -> int:
+    """SEED 10종목 강제 upsert. 네트워크 불필요 → KRX 실패와 무관하게 항상 성공.
+
+    별도 트랜잭션이라 KRX 시드가 나중에 실패해도 살아남는다.
+    """
+    engine = get_engine()
+    with engine.begin() as conn:
+        for t in SEED_TICKERS:
+            conn.execute(
+                text(
+                    """
+                    INSERT INTO symbols (code, name, market, is_seed)
+                    VALUES (:code, :name, :market, TRUE)
+                    ON CONFLICT (code) DO UPDATE
+                      SET name    = EXCLUDED.name,
+                          market  = EXCLUDED.market,
+                          is_seed = TRUE
+                    """
+                ),
+                {"code": t.code, "name": t.name, "market": t.market},
+            )
+    return len(SEED_TICKERS)
+
+
 def seed_symbols() -> SeedReport:
-    """KOSPI + KOSDAQ 전 종목을 upsert. SEED 10 종목은 is_seed=TRUE."""
-    rows: list[tuple[str, str, str]] = []  # (code, name, market)
+    """KOSPI + KOSDAQ 전 종목을 upsert. SEED 10 종목은 is_seed=TRUE.
+
+    순서:
+      1) SEED_TICKERS 먼저 별도 트랜잭션으로 강제 upsert (KRX 실패와 무관하게 검색 가능)
+      2) KRX 리스팅 fetch (네트워크 의존) → 별도 트랜잭션으로 일괄 upsert.
+         시장별 fetch 실패 시 해당 시장만 스킵하고 나머지 진행.
+    """
+    # 1) SEED_TICKERS — 항상 보장
+    try:
+        _upsert_seed_tickers()
+        seed_marked = len(SEED_TICKERS)
+        logger.info("seed_symbols: seed-tickers upserted (%d)", seed_marked)
+    except Exception as e:  # noqa: BLE001
+        # logger.exception 은 Python 3.11 의 traceback 포매터가 pykrx 소스의 한글 주석
+        # 'df = 가...' 바이트를 만나면 UnicodeDecodeError 를 던지는 버그가 있어, 그 예외가
+        # try 밖으로 escape 해서 500 을 만든다. 그래서 traceback 안 찍는다.
+        logger.error("seed_symbols: seed-tickers upsert failed: %s", repr(e)[:300])
+        seed_marked = 0
+
+    # 2) KRX 전 종목 — fetch 실패해도 부분 성공 허용
     market_counts: dict[str, int] = {}
+    all_rows: list[tuple[str, str, str]] = []
     for market in ("KOSPI", "KOSDAQ"):
+        try:
             listing = _fetch_market_listing(market)
             market_counts[market] = len(listing)
             for code, name in listing:
-            rows.append((code, name, market))
+                all_rows.append((code, name, market))
+            logger.info("seed_symbols: KRX %s fetched (%d)", market, len(listing))
+        except Exception as e:  # noqa: BLE001
+            logger.error("seed_symbols: KRX %s fetch failed — skip market: %s", market, repr(e)[:300])
+            market_counts[market] = 0
 
-    engine = get_engine()
     inserted = updated = 0
-    seed_marked = 0
+    if all_rows:
+        engine = get_engine()
+        try:
             with engine.begin() as conn:
-        for code, name, market in rows:
+                for code, name, market in all_rows:
                     is_seed = code in SEED_CODES
                     res = conn.execute(
                         text(
@@ -76,21 +125,8 @@ def seed_symbols() -> SeedReport:
                         inserted += 1
                     else:
                         updated += 1
-            if is_seed:
-                seed_marked += 1
-
-        # SEED_TICKERS 중 KRX 리스팅에 없으면 (상장폐지 등) 그래도 명시적으로 시드 row 보장
-        for t in SEED_TICKERS:
-            conn.execute(
-                text(
-                    """
-                    INSERT INTO symbols (code, name, market, is_seed)
-                    VALUES (:code, :name, :market, TRUE)
-                    ON CONFLICT (code) DO UPDATE SET is_seed = TRUE
-                    """
-                ),
-                {"code": t.code, "name": t.name, "market": t.market},
-            )
+        except Exception as e:  # noqa: BLE001
+            logger.error("seed_symbols: KRX bulk upsert failed (transaction rolled back): %s", repr(e)[:300])
 
     logger.info(
         "seed_symbols done: inserted=%d updated=%d seed_marked=%d markets=%s",

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
@@ -25,9 +27,58 @@ 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
+    #   - SEED 10종목은 매 부팅마다 무조건 upsert (10회 upsert, ms 단위, 네트워크 무관)
+    #     → KRX 접근 실패한 환경에서도 최소 10종목 검색 보장
+    #   - KRX 전 종목 fetch 는 symbols 가 비어있을 때만 (호출 비용 큼)
+    try:
+        from app.fetch.symbols_seed import _upsert_seed_tickers, seed_symbols
+        n_seed = _upsert_seed_tickers()
+        logger.info("bootstrap seed-tickers ensured (%d)", n_seed)
+
+        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 <= n_seed:
+            # symbols 가 SEED 만큼 또는 그 이하 → KRX 전 종목 fetch 시도
+            logger.info("symbols sparse (count=%d) — running KRX listing seed", count)
+            report = seed_symbols()
+            logger.info("bootstrap seed_symbols: %s", report)
+        else:
+            logger.info("symbols already populated (count=%d) — skip KRX listing 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.
+    if os.environ.get("SCHEDULER_DISABLED") == "1":
+        logger.info("scheduler skipped (SCHEDULER_DISABLED=1)")
+    else:
         try:
             start_scheduler()
         except Exception:  # noqa: BLE001
@@ -81,3 +132,30 @@ def health_keys() -> dict[str, object]:
         "dart": dart_mod.ping(),
         # huggingface 는 모델 다운로드 시점에 확인 (별도 ping 호출 비용 회피)
     }
+
+
+@app.get("/health/models")
+def health_models() -> dict[str, object]:
+    """Chronos / LGBM 가용성 진단.
+
+    Chronos: lazy 로드 첫 호출이라 30초~수 분 걸릴 수 있음 (HuggingFace 다운로드).
+    LGBM: 체크포인트 디렉토리 스캔 — retrain 안 돈 cold start 에선 비어있음.
+    """
+    from pathlib import Path
+
+    from app.models import chronos as chronos_mod
+
+    lgbm_dir = Path(os.environ.get("LGBM_MODEL_DIR", "/app/data/models"))
+    lgbm_files: list[str] = []
+    if lgbm_dir.exists():
+        lgbm_files = sorted(p.name for p in lgbm_dir.glob("*.pkl"))
+
+    return {
+        "chronos": chronos_mod.ping(),
+        "lgbm": {
+            "model_dir": str(lgbm_dir),
+            "checkpoint_count": len(lgbm_files),
+            "samples": lgbm_files[:8],  # 너무 많으면 잘라서.
+            "status": "ok" if lgbm_files else "no_checkpoints (cold start, run retrain_weekly)",
+        },
+    }

backend/app/models/chronos.py

@@ -59,8 +59,22 @@ def _load() -> None:
             os.environ.setdefault("HF_TOKEN", token)
 
         device = _resolve_device()
-        # bf16 은 RTX 30xx 이상에서 지원. cpu 에선 fp32.
-        dtype = torch.bfloat16 if device == "cuda" else torch.float32
+        # dtype 선택:
+        # - 이전엔 cuda 면 무조건 bf16 으로 갔는데, torch 2.3.1+cu121 사전빌드 wheel 이
+        #   sm_86 (RTX 3070 Ti) 의 일부 T5 커널 binary 를 빠뜨려서 inference 첫 호출에
+        #   "no kernel image is available for execution on the device" 발생. ping/load
+        #   까지는 통과해서 진단이 까다로웠음 (실제 005930 케이스에서 관측).
+        # - chronos-t5-small 은 46M params 라 fp32 로도 8GB VRAM 에 여유 충분, 속도
+        #   차이도 일봉 30일 예측에선 무시 가능. 호환성 우선해 default 를 fp32 로.
+        # - 드라이버/torch 업그레이드 후 다시 bf16 시험하려면 .env 에
+        #   CHRONOS_DTYPE=bf16 (또는 fp16) 두면 됨.
+        dtype_pref = os.environ.get("CHRONOS_DTYPE", "fp32").lower()
+        if device == "cuda" and dtype_pref == "bf16":
+            dtype = torch.bfloat16
+        elif device == "cuda" and dtype_pref == "fp16":
+            dtype = torch.float16
+        else:
+            dtype = torch.float32
         logger.info("loading Chronos %s on %s (dtype=%s)", MODEL_NAME, device, dtype)
         pipe = ChronosPipeline.from_pretrained(
             MODEL_NAME,
@@ -70,6 +84,26 @@ def _load() -> None:
         _state.update({"loaded": True, "pipe": pipe, "device": device})
 
 
+def _reload_cpu() -> None:
+    """현재 pipeline 을 폐기하고 CPU 로 강제 재로드.
+
+    cuda 환경에서 'no kernel image is available for execution on the device' 같이
+    런타임에야 드러나는 GPU 비호환 에러가 났을 때 자동 폴백용. 한 번 폴백하면
+    다음 호출부터는 CPU 그대로 사용 (재시도 비용 회피)."""
+    global _state
+    import torch
+    from chronos import ChronosPipeline
+    with _lock:
+        logger.warning("falling back to CPU for Chronos (GPU inference failed)")
+        _state.update({"loaded": False, "pipe": None, "device": None})
+        pipe = ChronosPipeline.from_pretrained(
+            MODEL_NAME,
+            device_map="cpu",
+            torch_dtype=torch.float32,
+        )
+        _state.update({"loaded": True, "pipe": pipe, "device": "cpu"})
+
+
 def forecast(
     series: list[float],
     *,
@@ -88,14 +122,31 @@ def forecast(
     import numpy as np
     import torch
 
+    def _do_predict():
         pipe = _state["pipe"]
         context = torch.tensor([float(x) for x in series], dtype=torch.float32)
         with torch.no_grad():
-        samples = pipe.predict(
+            return pipe.predict(
                 context=context,
                 prediction_length=horizon,
                 num_samples=num_samples,
             )
+
+    try:
+        samples = _do_predict()
+    except RuntimeError as exc:
+        # cuda 빌드/드라이버 미스매치는 inference 시점에야 드러나는 경우가 많음.
+        # 'no kernel image is available' / 'CUDA error' 같은 신호 잡아서 CPU 로 폴백.
+        msg = str(exc)
+        if _state.get("device") == "cuda" and (
+            "no kernel image" in msg
+            or "CUDA error" in msg
+            or "CUBLAS" in msg
+        ):
+            _reload_cpu()
+            samples = _do_predict()
+        else:
+            raise
     # samples: (1, num_samples, prediction_length)
     arr = samples[0].cpu().float().numpy()
     q10 = np.quantile(arr, 0.10, axis=0).tolist()

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]:
@@ -83,28 +87,41 @@ def predict(code: str, *, horizons: tuple[int, ...] = (1, 3, 5)) -> EnsemblePred
 
     sources_used: list[str] = []
     cf: ChronosForecast | None = None
+    chronos_err: str | None = None
     try:
         cf = chronos_forecast(closes, horizon=max_h, num_samples=30)
         sources_used.append("chronos")
     except Exception as exc:  # noqa: BLE001
-        logger.warning("chronos forecast failed for %s: %s", code, exc)
+        chronos_err = f"{type(exc).__name__}: {exc}"
+        logger.warning("chronos forecast failed for %s: %s", code, chronos_err)
 
     steps: list[EnsembleStep] = []
+    lgbm_raw: dict[int, LgbmForecast] = {}
     for h in horizons:
         lf: LgbmForecast | None = None
+        lgbm_err: str | None = None
         try:
             lf = lgbm_predict(code, h)
             if lf is not None:
                 sources_used.append(f"lgbm_h{h}")
+                lgbm_raw[h] = lf
+            else:
+                # predict_one 이 None 반환 = 체크포인트 파일 없음 (cold start).
+                lgbm_err = "model checkpoint not found (run retrain_weekly)"
         except Exception as exc:  # noqa: BLE001
-            logger.warning("lgbm predict failed for %s h=%d: %s", code, h, exc)
+            lgbm_err = f"{type(exc).__name__}: {exc}"
+            logger.warning("lgbm predict failed for %s h=%d: %s", code, h, lgbm_err)
 
         # 가중치 (DB 없으면 default 0.6/0.4).
         w = load_weights(code, h)
         wc, wl = w.w_chronos, w.w_lgbm
         # 한쪽이 없으면 다른 쪽 전부.
         if cf is None and lf is None:
-            raise RuntimeError(f"both chronos & lgbm failed for {code} h={h}")
+            # 사용자가 브라우저에서 바로 원인을 보게 두 에러를 그대로 노출.
+            raise RuntimeError(
+                f"both chronos & lgbm failed for {code} h={h}; "
+                f"chronos={chronos_err or 'unknown'}; lgbm={lgbm_err or 'unknown'}"
+            )
         if cf is None:
             wc, wl = 0.0, 1.0
         if lf is None:
@@ -171,4 +188,6 @@ 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)."""
@@ -49,39 +56,22 @@ def _last_trading_date(code: str) -> date | None:
     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")
+def _direction_label(ret: float) -> str:
+    if ret > FLAT_BAND:
+        return "up"
+    if ret < -FLAT_BAND:
+        return "down"
+    return "flat"
 
-    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_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, 'ensemble',
+      (: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)
@@ -99,27 +89,161 @@ def predict_and_store(
       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": now,
+            "predicted_at": predicted_at,
             "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,
+            "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()
-            if row:
-                saved_ids.append(int(row[0]))
+    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.
+
+    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: 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": base_close,
+                "sources_used": pred.sources_used,
+                "direction": step.direction,
+            }
+            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),
     }
 
 

backend/pyproject.toml

@@ -31,7 +31,7 @@ dependencies = [
     "transformers==4.41.2",
     "tokenizers==0.19.1",
     "sentencepiece==0.2.0",
-    "accelerate==0.30.1",
+    "accelerate==0.34.2",
     "chronos-forecasting==1.4.1",
     "scikit-learn==1.5.0",
     "lightgbm==4.3.0",

docker-compose.gpu.yml

@@ -8,5 +8,8 @@ services:
               count: all
               capabilities: [gpu]
     environment:
-      MODEL_DEVICE: cuda
+      # MODEL_DEVICE 는 .env 로 덮어쓰기 가능. GPU 빌드라도 PyTorch/CUDA 호환 문제 (예:
+      # 'no kernel image is available for execution on the device') 발생 시 .env 에
+      # MODEL_DEVICE=cpu 를 두고 `docker compose ... up -d backend` 로 회피.
+      MODEL_DEVICE: ${MODEL_DEVICE:-cuda}
       NVIDIA_VISIBLE_DEVICES: all

restart-ci.bat

@@ -0,0 +1,57 @@
+@echo off
+REM stock_chart_site - SSH/CI 친화 재시작 스크립트
+REM
+REM restart.bat 과의 차이: pause 가 없음. SSH 비대화형 (예: ssh user@host "restart-ci.bat")
+REM 에서 멈추지 않고 끝까지 실행. 에러는 종료 코드로만 알린다.
+REM
+REM 일반 사용 시엔 restart.bat 을 쓰는게 출력 검토에 편하다.
+
+setlocal enabledelayedexpansion
+cd /d "%~dp0"
+
+echo === stock_chart_site restart-ci ===
+
+where docker >nul 2>&1
+if errorlevel 1 (
+    echo [ERROR] docker not found
+    exit /b 1
+)
+docker info >nul 2>&1
+if errorlevel 1 (
+    echo [ERROR] Docker Desktop not running
+    exit /b 1
+)
+
+set USE_GPU=0
+where nvidia-smi >nul 2>&1
+if not errorlevel 1 (
+    nvidia-smi >nul 2>&1
+    if not errorlevel 1 set USE_GPU=1
+)
+
+if "%USE_GPU%"=="1" (
+    echo [GPU] using GPU profile
+    set COMPOSE_FILES=-f docker-compose.yml -f docker-compose.gpu.yml
+) else (
+    echo [CPU] using CPU profile
+    set COMPOSE_FILES=-f docker-compose.yml
+)
+
+for /f %%i in ('docker compose %COMPOSE_FILES% ps --status running --quiet backend web 2^>nul ^| find /v /c ""') do set RUN_COUNT=%%i
+if "%RUN_COUNT%"=="0" (
+    echo [ERROR] backend/web not running. run build.bat first.
+    exit /b 1
+)
+
+echo === docker compose up -d --force-recreate --no-deps backend web ===
+docker compose %COMPOSE_FILES% up -d --force-recreate --no-deps backend web
+if errorlevel 1 (
+    echo [ERROR] restart failed
+    exit /b 1
+)
+
+echo === status ===
+docker compose %COMPOSE_FILES% ps
+
+endlocal
+exit /b 0

restart.bat

@@ -0,0 +1,94 @@
+@echo off
+REM stock_chart_site - Windows 재시작 스크립트
+REM
+REM build.bat 와의 차이:
+REM   - build.bat: 이미지 재빌드 포함. Dockerfile / pyproject.toml / package*.json /
+REM                compose 설정 등 의존성/이미지 구성이 바뀌었을 때 사용.
+REM   - restart.bat: 재빌드 없이 컨테이너만 재시작. backend/app/ 또는 web/app/ 안의
+REM                  코드만 바뀐 경우. docker-compose.yml 의 바인드 마운트 (./backend:/app,
+REM                  ./web:/app) 덕에 새 코드가 즉시 컨테이너 안에서 보이고, 재시작으로
+REM                  lifespan (부팅 시드 등) 도 다시 돌릴 수 있다.
+REM
+REM 즉 일반적으로 git pull 후:
+REM   - pyproject.toml / Dockerfile / package*.json 변경 있음 → build.bat
+REM   - app/ 코드만 변경                                       → restart.bat
+
+setlocal enabledelayedexpansion
+cd /d "%~dp0"
+
+echo === stock_chart_site restart ===
+
+REM 1) Docker 확인
+where docker >nul 2>&1
+if errorlevel 1 (
+    echo [ERROR] docker 명령을 찾을 수 없습니다. Docker Desktop 설치/실행을 확인하세요.
+    pause
+    exit /b 1
+)
+docker info >nul 2>&1
+if errorlevel 1 (
+    echo [ERROR] Docker Desktop이 실행 중이 아닙니다.
+    pause
+    exit /b 1
+)
+
+REM 2) GPU 감지 (build.bat 과 동일 — compose 파일 조합 일치 위해)
+set USE_GPU=0
+where nvidia-smi >nul 2>&1
+if not errorlevel 1 (
+    nvidia-smi >nul 2>&1
+    if not errorlevel 1 set USE_GPU=1
+)
+
+if "%USE_GPU%"=="1" (
+    echo [GPU] NVIDIA GPU detected. Using GPU profile.
+    set COMPOSE_FILES=-f docker-compose.yml -f docker-compose.gpu.yml
+) else (
+    echo [CPU] NVIDIA GPU not detected. Using CPU profile.
+    set COMPOSE_FILES=-f docker-compose.yml
+)
+
+REM 3) backend/web 컨테이너 살아있는지 확인 — 없으면 build.bat 안내
+REM    (db 까지 포함해서 세면 db 만 떠있어도 통과돼버려서 부정확)
+for /f %%i in ('docker compose %COMPOSE_FILES% ps --status running --quiet backend web 2^>nul ^| find /v /c ""') do set RUN_COUNT=%%i
+if "%RUN_COUNT%"=="0" (
+    echo [INFO] 실행 중인 backend/web 컨테이너가 없습니다. 처음이거나 down 된 상태입니다.
+    echo        build.bat 으로 빌드 + 기동하세요.
+    pause
+    exit /b 1
+)
+
+REM 4) backend + web 만 재기동 — db 는 건드리지 않음 (--no-deps).
+REM
+REM    왜 `restart` 가 아니라 `up -d --force-recreate` 인가:
+REM      - `docker compose restart` 는 기존 컨테이너를 stop/start 만 한다. 그래서
+REM        `.env` 변경 (예: KIS_APP_KEY 갱신) 이 반영되지 않는다. env_file 은
+REM        컨테이너 "생성" 시점에만 읽힌다.
+REM      - `up -d --force-recreate` 는 새 컨테이너 인스턴스를 만들어서 env_file 을
+REM        다시 읽는다. 이게 사용자가 .env 만 고치고 restart.bat 돌렸을 때 직관에 맞는다.
+REM      - `--no-deps` 로 db 는 절대 건드리지 않음. db 는 postgres_data 볼륨이 영속이라
+REM        재기동할 이유 없고, depends_on.condition: service_healthy 와 무관하게 안전.
+echo.
+echo === docker compose up -d --force-recreate --no-deps backend web ===
+docker compose %COMPOSE_FILES% up -d --force-recreate --no-deps backend web
+if errorlevel 1 (
+    echo [ERROR] restart 실패.
+    pause
+    exit /b 1
+)
+
+echo.
+echo === 상태 ===
+docker compose %COMPOSE_FILES% ps
+
+echo.
+echo 접속:
+echo   Web      http://localhost:3000
+echo   Backend  http://localhost:8000/health
+echo   DB ext   http://localhost:8000/health/db
+echo.
+echo 로그 보기:  docker compose logs -f backend
+echo 정지:       docker compose down
+echo.
+pause
+endlocal

web/app/[code]/page.tsx

@@ -8,33 +8,63 @@ import { PredictionPanel } from "../../components/PredictionPanel";
 import { StockChart } from "../../components/StockChart";
 import {
   api,
+  type ChartInterval,
   type ChartPayload,
   type LatestPredictionResponse,
 } from "../../lib/api";
 
+const INTERVALS: { label: string; value: ChartInterval; defaultDays: number }[] = [
+  { label: "10분", value: "10m", defaultDays: 1 },
+  { label: "일", value: "1d", defaultDays: 180 },
+  { label: "주", value: "1w", defaultDays: 365 * 2 },
+  { label: "월", value: "1mo", defaultDays: 365 * 5 },
+];
+
 export default function CodePage({ params }: { params: { code: string } }) {
   const { code } = params;
   const [chart, setChart] = useState<ChartPayload | null>(null);
   const [prediction, setPrediction] = useState<LatestPredictionResponse | null>(null);
   const [err, setErr] = useState<string | null>(null);
+  const [interval, setIntervalKind] = useState<ChartInterval>("1d");
   const [days, setDays] = useState(180);
 
+  // interval 바꾸면 days 도 그 interval 에 맞는 기본값으로 (사용자가 명시적으로 다시 고를 수 있게).
+  function pickInterval(v: ChartInterval) {
+    const meta = INTERVALS.find((i) => i.value === v)!;
+    setIntervalKind(v);
+    setDays(meta.defaultDays);
+  }
+
+  // 초기/주기적 차트 로드. 10분봉이면 60초마다 폴링 — 백엔드가 캐시-then-fetch 로
+  // 10분 이내면 DB 만 읽고, 넘었으면 KIS 호출. 폴링 부담은 낮음.
   useEffect(() => {
     let alive = true;
     setErr(null);
     setChart(null);
+
+    const load = () => {
       api
-      .getChart(code, days)
+        .getChart(code, days, interval)
         .then((c) => {
           if (alive) setChart(c);
         })
         .catch((e) => {
           if (alive) setErr(e instanceof Error ? e.message : String(e));
         });
+    };
+    load();
+
+    if (interval === "10m") {
+      const h = window.setInterval(load, 60_000);
+      return () => {
+        alive = false;
+        window.clearInterval(h);
+      };
+    }
     return () => {
       alive = false;
     };
-  }, [code, days]);
+  }, [code, days, interval]);
 
   useEffect(() => {
     let alive = true;
@@ -57,6 +87,23 @@ export default function CodePage({ params }: { params: { code: string } }) {
         <Link href="/" className="text-xs text-zinc-500 hover:text-zinc-300">
           ← 검색으로
         </Link>
+        <div className="flex items-center gap-2">
+          <div className="flex overflow-hidden rounded-md border border-zinc-700 text-xs">
+            {INTERVALS.map((it) => (
+              <button
+                key={it.value}
+                onClick={() => pickInterval(it.value)}
+                className={
+                  interval === it.value
+                    ? "bg-emerald-700 px-3 py-1 text-white"
+                    : "bg-zinc-900 px-3 py-1 text-zinc-300 hover:bg-zinc-800"
+                }
+              >
+                {it.label}
+              </button>
+            ))}
+          </div>
+          {interval !== "10m" && (
             <select
               value={days}
               onChange={(e) => setDays(Number(e.target.value))}
@@ -65,18 +112,30 @@ export default function CodePage({ params }: { params: { code: string } }) {
               <option value={60}>최근 3개월</option>
               <option value={180}>최근 6개월</option>
               <option value={365}>최근 1년</option>
-          <option value={1095}>최근 3년</option>
+              <option value={365 * 2}>최근 2년</option>
+              <option value={365 * 5}>최근 5년</option>
+              <option value={365 * 10}>최근 10년</option>
             </select>
+          )}
+        </div>
       </div>
 
       {chart && (
-        <div className="mb-4">
+        <div className="mb-4 flex items-baseline justify-between">
           <h1 className="text-2xl font-semibold text-zinc-100">
             {chart.name}{" "}
             <span className="text-sm font-normal text-zinc-500">
               {chart.code} · {chart.market}
             </span>
           </h1>
+          {interval === "10m" && (
+            <div className="text-xs text-zinc-500">
+              실시간 10분봉 · 60초마다 갱신
+              {chart.intraday_status && (
+                <span className="ml-2 text-zinc-600">[{chart.intraday_status}]</span>
+              )}
+            </div>
+          )}
         </div>
       )}
 

web/components/PredictionPanel.tsx

@@ -42,16 +42,39 @@ function normalizeFromPredictResponse(
   };
 }
 
+const HORIZON_PRESETS: { label: string; value: number[] }[] = [
+  { label: "단기 (1·3·5)", value: [1, 3, 5] },
+  { label: "중기 (1·5·10)", value: [1, 5, 10] },
+  { label: "장기 (5·10·20)", value: [5, 10, 20] },
+];
+
 export function PredictionPanel({ code, initial, onResult }: Props) {
   const [pred, setPred] = useState<LatestPredictionResponse | null>(initial ?? null);
   const [loading, setLoading] = useState(false);
   const [err, setErr] = useState<string | null>(null);
+  const [presetIdx, setPresetIdx] = useState(0);
+  const [customRaw, setCustomRaw] = useState("");
+  const [useCustom, setUseCustom] = useState(false);
+
+  function effectiveHorizons(): number[] {
+    if (useCustom) {
+      // 백엔드 predict.py 가 1~30 만 허용 (모델 학습/검증 범위와 일치).
+      // 프론트에서 동일 cap 으로 끊어서 400 안 나게 한다.
+      const parsed = customRaw
+        .split(",")
+        .map((s) => Number(s.trim()))
+        .filter((n) => Number.isFinite(n) && n >= 1 && n <= 30);
+      if (parsed.length > 0) return Array.from(new Set(parsed)).sort((a, b) => a - b);
+    }
+    return HORIZON_PRESETS[presetIdx].value;
+  }
 
   async function runPredict() {
     setLoading(true);
     setErr(null);
     try {
-      const r = await api.predict(code);
+      const horizons = effectiveHorizons();
+      const r = await api.predict(code, horizons);
       const normalized = normalizeFromPredictResponse(code, r);
       setPred(normalized);
       onResult(normalized);
@@ -82,6 +105,49 @@ export function PredictionPanel({ code, initial, onResult }: Props) {
         </button>
       </div>
 
+      <div className="mb-3 flex flex-wrap items-center gap-2 text-xs">
+        <span className="text-zinc-500">예측 거래일:</span>
+        {HORIZON_PRESETS.map((p, i) => (
+          <button
+            key={p.label}
+            onClick={() => {
+              setUseCustom(false);
+              setPresetIdx(i);
+            }}
+            className={
+              !useCustom && presetIdx === i
+                ? "rounded-full bg-emerald-700 px-3 py-1 text-white"
+                : "rounded-full border border-zinc-700 px-3 py-1 text-zinc-300 hover:border-zinc-500"
+            }
+          >
+            {p.label}
+          </button>
+        ))}
+        <label
+          className={
+            useCustom
+              ? "flex items-center gap-1 rounded-full bg-emerald-700 px-3 py-1 text-white"
+              : "flex items-center gap-1 rounded-full border border-zinc-700 px-3 py-1 text-zinc-300 hover:border-zinc-500"
+          }
+        >
+          <input
+            type="checkbox"
+            checked={useCustom}
+            onChange={(e) => setUseCustom(e.target.checked)}
+            className="h-3 w-3"
+          />
+          직접
+          <input
+            type="text"
+            placeholder="1~30, 예: 1,2,3,7"
+            value={customRaw}
+            onChange={(e) => setCustomRaw(e.target.value)}
+            onFocus={() => setUseCustom(true)}
+            className="ml-1 w-28 rounded border border-zinc-700 bg-zinc-900 px-1 py-0.5 text-xs text-zinc-100"
+          />
+        </label>
+      </div>
+
       {err && <div className="mb-3 text-xs text-red-400">에러: {err}</div>}
 
       {pred?.found ? (

web/components/StockChart.tsx

@@ -16,12 +16,26 @@ type Props = {
   prediction?: LatestPredictionResponse | null;
 };
 
-function dateToUtcTs(d: string): UTCTimestamp {
-  // 'YYYY-MM-DD' → UTC midnight epoch seconds
+// 'YYYY-MM-DD' 또는 'YYYY-MM-DDTHH:MM:SS' (KST naive, 백엔드가 +09:00 시각의 wall-clock 을
+// 그대로 ISO 로 직렬화) 를 UTCTimestamp 로. lightweight-charts 는 timestamp 가 UTC 라고
+// 가정하지만, 우리는 KST wall-clock 을 UTC 인 척 넣는다 — timeScale 의 표시도 KST 그대로
+// 나와서 한국 사용자에겐 가장 직관적.
+function isoToUtcTs(s: string): UTCTimestamp {
+  if (s.length <= 10) {
     return (Date.UTC(
-    Number(d.slice(0, 4)),
-    Number(d.slice(5, 7)) - 1,
-    Number(d.slice(8, 10)),
+      Number(s.slice(0, 4)),
+      Number(s.slice(5, 7)) - 1,
+      Number(s.slice(8, 10)),
+    ) / 1000) as UTCTimestamp;
+  }
+  // datetime: YYYY-MM-DDTHH:MM:SS
+  return (Date.UTC(
+    Number(s.slice(0, 4)),
+    Number(s.slice(5, 7)) - 1,
+    Number(s.slice(8, 10)),
+    Number(s.slice(11, 13)),
+    Number(s.slice(14, 16)),
+    Number(s.slice(17, 19) || "0"),
   ) / 1000) as UTCTimestamp;
 }
 
@@ -33,7 +47,9 @@ export function StockChart({ chart, prediction }: Props) {
   const predLowRef = useRef<ISeriesApi<"Line"> | null>(null);
   const predHighRef = useRef<ISeriesApi<"Line"> | null>(null);
 
-  // create chart once
+  const isIntraday = chart.interval === "10m";
+
+  // create chart once (interval 바뀌면 timeVisible 토글 위해 의존성에 isIntraday 포함 — 재생성)
   useEffect(() => {
     if (!containerRef.current) return;
     const c = createChart(containerRef.current, {
@@ -46,7 +62,11 @@ export function StockChart({ chart, prediction }: Props) {
         horzLines: { color: "#1f2937" },
       },
       rightPriceScale: { borderColor: "#374151" },
-      timeScale: { borderColor: "#374151", timeVisible: false },
+      timeScale: {
+        borderColor: "#374151",
+        timeVisible: isIntraday,
+        secondsVisible: false,
+      },
       autoSize: true,
     });
     const candle = c.addCandlestickSeries({
@@ -67,28 +87,30 @@ export function StockChart({ chart, prediction }: Props) {
       predLowRef.current = null;
       predHighRef.current = null;
     };
-  }, []);
+  }, [isIntraday]);
 
-  // push candle data
+  // push candle data + today marker
   useEffect(() => {
     if (!candleRef.current) return;
     const data: CandlestickData[] = chart.ohlcv
       .filter((p) => p.open !== null && p.high !== null && p.low !== null && p.close !== null)
       .map((p) => ({
-        time: dateToUtcTs(p.date),
+        time: isoToUtcTs(p.date),
         open: p.open as number,
         high: p.high as number,
         low: p.low as number,
         close: p.close as number,
       }));
     candleRef.current.setData(data);
+    // 오늘 표시는 차트 본체 위가 아니라 컨테이너 아래 캡션 (return JSX) 으로 옮김.
+    // lightweight-charts 의 timeScale tick 자체에 라벨을 끼울 공식 API 가 없어서,
+    // 시각적으로 동일한 위치 (시간축 바로 아래) 에 별도 div 로 렌더.
     chartRef.current?.timeScale().fitContent();
-  }, [chart]);
+  }, [chart, isIntraday]);
 
-  // push prediction overlay
+  // push prediction overlay (10분봉에서는 표시 안 함 — 예측은 일봉 기준)
   useEffect(() => {
     if (!chartRef.current) return;
-    // remove previous overlay
     if (predRef.current) {
       chartRef.current.removeSeries(predRef.current);
       predRef.current = null;
@@ -101,6 +123,7 @@ export function StockChart({ chart, prediction }: Props) {
       chartRef.current.removeSeries(predHighRef.current);
       predHighRef.current = null;
     }
+    if (isIntraday) return;
     if (!prediction || !prediction.found || !prediction.steps?.length) return;
     const baseDate = prediction.base_date!;
     const baseClose = prediction.base_close;
@@ -108,22 +131,22 @@ export function StockChart({ chart, prediction }: Props) {
     const sorted = [...prediction.steps].sort((a, b) => a.horizon - b.horizon);
 
     const med: LineData[] = [
-      { time: dateToUtcTs(baseDate), value: baseClose },
+      { time: isoToUtcTs(baseDate), value: baseClose },
       ...sorted
         .filter((s) => s.point_close !== null)
-        .map((s) => ({ time: dateToUtcTs(s.target_date), value: s.point_close as number })),
+        .map((s) => ({ time: isoToUtcTs(s.target_date), value: s.point_close as number })),
     ];
     const lo: LineData[] = [
-      { time: dateToUtcTs(baseDate), value: baseClose },
+      { time: isoToUtcTs(baseDate), value: baseClose },
       ...sorted
         .filter((s) => s.ci_low !== null)
-        .map((s) => ({ time: dateToUtcTs(s.target_date), value: s.ci_low as number })),
+        .map((s) => ({ time: isoToUtcTs(s.target_date), value: s.ci_low as number })),
     ];
     const hi: LineData[] = [
-      { time: dateToUtcTs(baseDate), value: baseClose },
+      { time: isoToUtcTs(baseDate), value: baseClose },
       ...sorted
         .filter((s) => s.ci_high !== null)
-        .map((s) => ({ time: dateToUtcTs(s.target_date), value: s.ci_high as number })),
+        .map((s) => ({ time: isoToUtcTs(s.target_date), value: s.ci_high as number })),
     ];
 
     const medLine = chartRef.current.addLineSeries({
@@ -157,11 +180,31 @@ export function StockChart({ chart, prediction }: Props) {
     predLowRef.current = loLine;
     predHighRef.current = hiLine;
     chartRef.current.timeScale().fitContent();
-  }, [prediction]);
+  }, [prediction, isIntraday]);
+
+  // 오늘 라벨 — 차트 본체에 마커 대신 시간축 바로 아래에 작은 캡션으로.
+  // 10분봉은 데이터 자체가 오늘 하루라 굳이 라벨 불필요.
+  const todayLabel =
+    !isIntraday && chart.today
+      ? new Date(chart.today + "T00:00:00").toLocaleDateString("ko-KR", {
+          year: "numeric",
+          month: "2-digit",
+          day: "2-digit",
+          weekday: "short",
+        })
+      : null;
 
   return (
-    <div className="h-[460px] w-full rounded-md border border-zinc-800 bg-zinc-900/30 p-2">
+    <div className="w-full rounded-md border border-zinc-800 bg-zinc-900/30 p-2">
+      <div className="h-[460px] w-full">
         <div ref={containerRef} className="h-full w-full" />
       </div>
+      {todayLabel && (
+        <div className="mt-1 flex items-center justify-end gap-2 px-2 text-xs text-zinc-400">
+          <span className="inline-block h-2 w-2 rounded-full bg-amber-400" />
+          <span>오늘 · {todayLabel}</span>
+        </div>
+      )}
+    </div>
   );
 }

web/lib/api.ts

@@ -1,8 +1,31 @@
 // Backend API client.
-// NEXT_PUBLIC_API_BASE 는 docker-compose 에서 http://localhost:8000 으로 주입됨.
+//
+// API 베이스 해석 우선순위:
+//   1) NEXT_PUBLIC_API_BASE 가 localhost/127.0.0.1 이 아닌 명시값 → 그대로 사용
+//      (예: 프로덕션 https://api.example.com)
+//   2) 브라우저 환경 → window.location.hostname:8000 (LAN 접속도 자동 대응)
+//   3) SSR 폴백 → http://localhost:8000
+//
+// docker-compose 가 NEXT_PUBLIC_API_BASE=http://localhost:8000 을 주입하는 경우가 흔한데,
+// LAN 의 다른 PC 에서 http://<host>:3000 으로 접속하면 inline 된 localhost 가 그쪽 PC 의
+// localhost 를 가리켜 깨진다. 그래서 localhost/127.0.0.1 값은 신뢰하지 않고 페이지 host 로
+// 폴백.
 
-const RAW_BASE = process.env.NEXT_PUBLIC_API_BASE ?? "http://localhost:8000";
-export const API_BASE = RAW_BASE.replace(/\/$/, "");
+function resolveApiBase(): string {
+  const raw = process.env.NEXT_PUBLIC_API_BASE;
+  const env = raw && raw.length > 0 ? raw.replace(/\/$/, "") : "";
+  const envIsLocal = !env || /\/\/(localhost|127\.0\.0\.1)(?::|$)/.test(env);
+  if (typeof window !== "undefined") {
+    if (envIsLocal) {
+      return `${window.location.protocol}//${window.location.hostname}:8000`;
+    }
+    return env;
+  }
+  // SSR
+  return env || "http://localhost:8000";
+}
+
+export const API_BASE = resolveApiBase();
 
 export type Symbol = {
   code: string;
@@ -19,6 +42,7 @@ export type SymbolSearch = {
 };
 
 export type OhlcvPoint = {
+  // 1d/1w/1mo: 'YYYY-MM-DD'  /  10m: 'YYYY-MM-DDTHH:MM:SS' (KST naive ISO)
   date: string;
   open: number | null;
   high: number | null;
@@ -27,6 +51,8 @@ export type OhlcvPoint = {
   volume: number | null;
 };
 
+export type ChartInterval = "10m" | "1d" | "1w" | "1mo";
+
 export type SentimentPoint = {
   date: string;
   n_articles: number;
@@ -45,7 +71,10 @@ export type ChartPayload = {
   code: string;
   name: string;
   market: string;
+  interval: ChartInterval;
+  intraday_status: string | null;
   range: { from: string; to: string };
+  today: string;
   ohlcv: OhlcvPoint[];
   sentiment: SentimentPoint[];
   trading_value: TradingValuePoint[];
@@ -72,6 +101,7 @@ export type PredictResponse = {
   sources_used: string[];
   steps: PredictionStep[];
   saved_prediction_ids: number[];
+  saved_shadow_ids?: { chronos: number[]; lgbm: number[] };
   user_triggered: boolean;
 };
 
@@ -154,13 +184,17 @@ export const api = {
       `/api/symbols/search?q=${encodeURIComponent(q)}&limit=${limit}&seed_only=${seedOnly}`,
     ),
   getSymbol: (code: string) => getJson<Symbol>(`/api/symbols/${encodeURIComponent(code)}`),
-  getChart: (code: string, days = 180) =>
-    getJson<ChartPayload>(`/api/chart/${encodeURIComponent(code)}?days=${days}`),
-  predict: (code: string, horizons = "1,3,5") =>
-    getJson<PredictResponse>(
-      `/api/predict/${encodeURIComponent(code)}?horizons=${encodeURIComponent(horizons)}`,
-      { method: "POST" },
+  getChart: (code: string, days = 180, interval: ChartInterval = "1d") =>
+    getJson<ChartPayload>(
+      `/api/chart/${encodeURIComponent(code)}?days=${days}&interval=${encodeURIComponent(interval)}`,
     ),
+  predict: (code: string, horizons: string | number[] = "1,3,5") => {
+    const h = Array.isArray(horizons) ? horizons.join(",") : horizons;
+    return getJson<PredictResponse>(
+      `/api/predict/${encodeURIComponent(code)}?horizons=${encodeURIComponent(h)}`,
+      { method: "POST" },
+    );
+  },
   latestPrediction: (code: string) =>
     getJson<LatestPredictionResponse>(`/api/predict/${encodeURIComponent(code)}/latest`),
   metrics: (code: string, windowDays = 30) =>

web/next-env.d.ts

@@ -2,4 +2,4 @@
 /// <reference types="next/image-types/global" />
 
 // NOTE: This file should not be edited
-// see https://nextjs.org/docs/basic-features/typescript for more information.
+// see https://nextjs.org/docs/app/building-your-application/configuring/typescript for more information.

web/package-lock.json

@@ -9,7 +9,7 @@
       "version": "0.0.1",
       "dependencies": {
         "lightweight-charts": "4.1.7",
-        "next": "14.2.3",
+        "next": "^14.2.33",
         "react": "18.3.1",
         "react-dom": "18.3.1"
       },
@@ -19,7 +19,7 @@
         "@types/react-dom": "18.3.0",
         "autoprefixer": "10.4.19",
         "eslint": "8.57.0",
-        "eslint-config-next": "14.2.3",
+        "eslint-config-next": "^14.2.33",
         "postcss": "8.4.38",
         "tailwindcss": "3.4.4",
         "typescript": "5.4.5"
@@ -260,23 +260,23 @@
       }
     },
     "node_modules/@next/env": {
-      "version": "14.2.3",
-      "resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.3.tgz",
-      "integrity": "sha512-W7fd7IbkfmeeY2gXrzJYDx8D2lWKbVoTIj1o1ScPHNzvp30s1AuoEFSdr39bC5sjxJaxTtq3OTCZboNp0lNWHA=="
+      "version": "14.2.35",
+      "resolved": "https://registry.npmjs.org/@next/env/-/env-14.2.35.tgz",
+      "integrity": "sha512-DuhvCtj4t9Gwrx80dmz2F4t/zKQ4ktN8WrMwOuVzkJfBilwAwGr6v16M5eI8yCuZ63H9TTuEU09Iu2HqkzFPVQ=="
     },
     "node_modules/@next/eslint-plugin-next": {
-      "version": "14.2.3",
-      "resolved": "https://registry.npmjs.org/@next/eslint-plugin-next/-/eslint-plugin-next-14.2.3.tgz",
-      "integrity": "sha512-L3oDricIIjgj1AVnRdRor21gI7mShlSwU/1ZGHmqM3LzHhXXhdkrfeNY5zif25Bi5Dd7fiJHsbhoZCHfXYvlAw==",
+      "version": "14.2.35",
+      "resolved": "https://registry.npmjs.org/@next/eslint-plugin-next/-/eslint-plugin-next-14.2.35.tgz",
+      "integrity": "sha512-Jw9A3ICz2183qSsqwi7fgq4SBPiNfmOLmTPXKvlnzstUwyvBrtySiY+8RXJweNAs9KThb1+bYhZh9XWcNOr2zQ==",
       "dev": true,
       "dependencies": {
         "glob": "10.3.10"
       }
     },
     "node_modules/@next/swc-darwin-arm64": {
-      "version": "14.2.3",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.3.tgz",
-      "integrity": "sha512-3pEYo/RaGqPP0YzwnlmPN2puaF2WMLM3apt5jLW2fFdXD9+pqcoTzRk+iZsf8ta7+quAe4Q6Ms0nR0SFGFdS1A==",
+      "version": "14.2.33",
+      "resolved": "https://registry.npmjs.org/@next/swc-darwin-arm64/-/swc-darwin-arm64-14.2.33.tgz",
+      "integrity": "sha512-HqYnb6pxlsshoSTubdXKu15g3iivcbsMXg4bYpjL2iS/V6aQot+iyF4BUc2qA/J/n55YtvE4PHMKWBKGCF/+wA==",
       "cpu": [
         "arm64"
       ],
@@ -289,9 +289,9 @@
       }
     },
     "node_modules/@next/swc-darwin-x64": {
-      "version": "14.2.3",
-      "resolved": "https://registry.npmjs.org/@next/swc-darwin-x64/-/swc-darwin-x64-14.2.3.tgz",
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-        "@typescript-eslint/parser": "^5.4.2 || ^6.0.0 || 7.0.0 - 7.2.0",
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@@ -3713,12 +3813,11 @@
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-      "deprecated": "This version has a security vulnerability. Please upgrade to a patched version. See https://nextjs.org/blog/security-update-2025-12-11 for more details.",
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-        "@next/swc-win32-x64-msvc": "14.2.3"
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web/package.json

@@ -11,7 +11,7 @@
     "check": "npm run typecheck && npm run lint"
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+    "eslint-config-next": "^14.2.33"
   }
 }