feat(phase-2): KR-FinBERT 감성 스코어링 + 일별 집계 뷰

- backend/app/nlp/finbert.py: snunlp/KR-FinBert-SC 어댑터.
  - score = P(pos) - P(neg) ∈ [-1, +1], label = argmax (neg/neu/pos)
  - 768d mean-pooled last hidden state → news.embedding (VECTOR) 저장
  - settings.huggingface_token 인증, lazy singleton, cuda/cpu auto
- backend/app/nlp/score_news.py: news 테이블에서 sentiment_score IS NULL
  행을 배치 스코어 → UPDATE (... embedding=(:e)::vector). 종목 필터 + limit 옵션.
- backend/app/db/migrations/002_sentiment_view.sql: v_sentiment_daily 뷰.
  종목·KST 일별 n_articles, mean_score, pos/neg/neu_ratio, weighted_score
  (naver_finance 1.0 / google_rss 0.7 / dart 0.5).
- backend/app/db/migrate.py: 이미 실행 중인 DB 에 새 SQL 마이그레이션 적용용
  CLI. 모든 SQL 파일은 idempotent.
- refresh_one.py: refresh 끝에 종목당 200건까지 finbert 스코어, finbert
  SourceStatus 를 RefreshReport 에 추가.
- daily_batch.py: 모든 종목 처리 후 score_pending_news(limit=2000) 로 mop-up.

모델 캐시는 docker-compose hf_cache 볼륨(/root/.cache/huggingface).

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

backend/app/db/migrate.py

@@ -0,0 +1,53 @@
+"""Manual migration runner.
+
+docker-entrypoint-initdb.d 는 fresh DB 첫 기동 때만 동작. 이미 동작 중인 DB 에
+새 마이그레이션을 적용하려면 이 스크립트로:
+
+    python -m app.db.migrate
+
+모든 SQL 파일은 idempotent (CREATE IF NOT EXISTS / CREATE OR REPLACE) 여야 함.
+"""
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+from sqlalchemy import text
+
+from app.db.connection import get_engine
+
+logger = logging.getLogger(__name__)
+
+MIGRATIONS_DIR = Path(__file__).parent / "migrations"
+
+
+def apply_all() -> dict[str, str]:
+    """migrations/ 안 .sql 들을 이름순으로 적용. 결과: {filename: 'ok'|'failed: ...'}."""
+    eng = get_engine()
+    results: dict[str, str] = {}
+    files = sorted(MIGRATIONS_DIR.glob("*.sql"))
+    if not files:
+        logger.warning("no migration files in %s", MIGRATIONS_DIR)
+        return results
+    for path in files:
+        sql = path.read_text(encoding="utf-8")
+        # psql meta-command 제거 (\set ON_ERROR_STOP 등)
+        cleaned = "\n".join(
+            ln for ln in sql.splitlines() if not ln.strip().startswith("\\")
+        )
+        try:
+            with eng.begin() as conn:
+                conn.execute(text(cleaned))
+            results[path.name] = "ok"
+            logger.info("applied %s", path.name)
+        except Exception as exc:  # noqa: BLE001
+            results[path.name] = f"failed: {exc}"
+            logger.exception("migration %s failed", path.name)
+    return results
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(name)s: %(message)s")
+    out = apply_all()
+    for k, v in out.items():
+        print(f"{k}: {v}")

backend/app/db/migrations/002_sentiment_view.sql

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+-- Phase 2: 일별 종목별 감성 집계 뷰.
+-- weighted_score : 소스별 가중치 적용
+--   naver_finance 1.0 (가장 직접적인 종목 페이지 뉴스)
+--   google_rss    0.7 (관련성 노이즈 있음)
+--   dart          0.5 (공시는 short title 만으로는 감성이 약함)
+
+\set ON_ERROR_STOP on
+
+CREATE OR REPLACE VIEW v_sentiment_daily AS
+SELECT
+  code,
+  (published_at AT TIME ZONE 'Asia/Seoul')::date                AS date,
+  COUNT(*)                                                       AS n_articles,
+  AVG(sentiment_score)::REAL                                     AS mean_score,
+  AVG(CASE WHEN sentiment_label = 'positive' THEN 1.0 ELSE 0.0 END)::REAL AS pos_ratio,
+  AVG(CASE WHEN sentiment_label = 'negative' THEN 1.0 ELSE 0.0 END)::REAL AS neg_ratio,
+  AVG(CASE WHEN sentiment_label = 'neutral'  THEN 1.0 ELSE 0.0 END)::REAL AS neu_ratio,
+  AVG(
+    sentiment_score * CASE source
+      WHEN 'naver_finance' THEN 1.0
+      WHEN 'google_rss'    THEN 0.7
+      WHEN 'dart'          THEN 0.5
+      ELSE 0.6
+    END
+  )::REAL                                                        AS weighted_score
+FROM news
+WHERE sentiment_score IS NOT NULL
+  AND code IS NOT NULL
+GROUP BY code, (published_at AT TIME ZONE 'Asia/Seoul')::date;
+
+COMMENT ON VIEW v_sentiment_daily IS
+  'Phase 2: KR-FinBERT 점수를 종목·일(KST) 단위로 집계. Phase 4 LGBM 피처 + UI 차트 보조 데이터로 사용.';