docs(ai_news): mark scraper.py deprecated (Phase 1 transition)

stock-lab/app/screener/ai_news/analyzer.py

@@ -27,6 +27,25 @@ def _clamp(x: float, lo: float = -10.0, hi: float = 10.0) -> float:
     return max(lo, min(hi, x))
 
 
+def _format_news_block(news: List[Dict[str, Any]]) -> str:
+    """news dict 리스트 → prompt 에 들어가는 텍스트 블록.
+
+    summary 가 있으면 title 다음 줄에 indent 해서 포함 (최대 200자).
+    pub_date 가 있으면 title 앞에 표시.
+    """
+    lines: List[str] = []
+    for n in news:
+        date = (n.get("pub_date") or "").strip()
+        title = (n.get("title") or "").strip()
+        summary = (n.get("summary") or "").strip()
+        prefix = f"[{date}] " if date else ""
+        if summary:
+            lines.append(f"- {prefix}{title}\n  {summary[:200]}")
+        else:
+            lines.append(f"- {prefix}{title}")
+    return "\n".join(lines)
+
+
 async def score_sentiment(
     llm,
     ticker: str,
@@ -36,7 +55,7 @@ async def score_sentiment(
     model: str = DEFAULT_MODEL,
 ) -> Dict[str, Any]:
     """Returns {ticker, score_raw, reason, news_count, tokens_input, tokens_output, model}."""
-    news_block = "\n".join(f"- {n['title']}" for n in news)
+    news_block = _format_news_block(news)
     prompt = PROMPT_TEMPLATE.format(
         name=name or ticker, ticker=ticker,
         n=len(news), news_block=news_block,

stock-lab/app/screener/ai_news/articles_source.py

@@ -0,0 +1,70 @@
+"""기존 articles 테이블에서 종목별 뉴스 매핑."""
+
+from __future__ import annotations
+
+import datetime as dt
+import logging
+import sqlite3
+from typing import Any, Dict, List, Tuple
+
+log = logging.getLogger(__name__)
+
+
+def gather_articles_for_tickers(
+    conn: sqlite3.Connection,
+    tickers: List[str],
+    asof: dt.date,
+    *,
+    window_days: int = 1,
+    max_per_ticker: int = 5,
+) -> Tuple[Dict[str, List[Dict[str, Any]]], Dict[str, int]]:
+    """articles 에서 ticker.name substring 매칭으로 종목별 뉴스 dict 반환.
+
+    Returns:
+        (
+          {ticker: [{"title": str, "summary": str, "press": str, "pub_date": str}, ...]},
+          {"total_articles": int, "matched_pairs": int, "hit_tickers": int},
+        )
+    """
+    out: Dict[str, List[Dict[str, Any]]] = {t: [] for t in tickers}
+    stats = {"total_articles": 0, "matched_pairs": 0, "hit_tickers": 0}
+
+    if not tickers:
+        return out, stats
+
+    cutoff = (asof - dt.timedelta(days=window_days)).isoformat()
+
+    placeholders = ",".join("?" * len(tickers))
+    name_rows = conn.execute(
+        f"SELECT ticker, name FROM krx_master WHERE ticker IN ({placeholders})",
+        tickers,
+    ).fetchall()
+    # 2글자 미만 회사명은 false positive 위험으로 제외
+    name_map = {r[0]: r[1] for r in name_rows if r[1] and len(r[1]) >= 2}
+
+    articles = conn.execute(
+        "SELECT title, summary, press, pub_date, crawled_at "
+        "FROM articles WHERE crawled_at >= ? ORDER BY crawled_at DESC",
+        (cutoff,),
+    ).fetchall()
+    stats["total_articles"] = len(articles)
+
+    for a in articles:
+        title = (a[0] or "").strip()
+        summary = (a[1] or "").strip()
+        haystack = title + " " + summary
+        for ticker, name in name_map.items():
+            if name not in haystack:
+                continue
+            if len(out[ticker]) >= max_per_ticker:
+                continue
+            out[ticker].append({
+                "title": title,
+                "summary": summary,
+                "press": a[2] or "",
+                "pub_date": a[3] or "",
+            })
+            stats["matched_pairs"] += 1
+
+    stats["hit_tickers"] = sum(1 for arts in out.values() if arts)
+    return out, stats

stock-lab/app/screener/ai_news/pipeline.py

@@ -8,19 +8,17 @@ import logging
 import os
 import sqlite3
 import time
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List
 
-import httpx
-
-from . import scraper as _scraper
+from . import scraper as _scraper  # legacy, kept for backward import
 from . import analyzer as _analyzer
+from . import articles_source  # 신규
 
 log = logging.getLogger(__name__)
 
 DEFAULT_TOP_N = 100
 DEFAULT_CONCURRENCY = 10
 DEFAULT_NEWS_PER_TICKER = 5
-DEFAULT_RATE_LIMIT_SEC = 0.2
 
 
 def _top_market_cap_tickers(conn: sqlite3.Connection, n: int) -> List[str]:
@@ -33,10 +31,6 @@ def _top_market_cap_tickers(conn: sqlite3.Connection, n: int) -> List[str]:
     return [r[0] for r in rows]
 
 
-def _make_http():
-    return httpx.AsyncClient(timeout=10.0, headers=_scraper.NAVER_HEADERS)
-
-
 def _make_llm():
     """Anthropic AsyncClient — env에 ANTHROPIC_API_KEY 필수."""
     from anthropic import AsyncAnthropic
@@ -44,47 +38,40 @@ def _make_llm():
 
 
 async def _process_one(
-    ticker: str, name: str, sem: asyncio.Semaphore,
-    http_client, llm, news_per_ticker: int, rate_limit_sec: float, model: str,
+    ticker: str, name: str, articles: List[Dict[str, Any]],
+    sem: asyncio.Semaphore, llm, model: str,
 ) -> Dict[str, Any]:
     async with sem:
-        if rate_limit_sec > 0:
-            await asyncio.sleep(rate_limit_sec)
-        news = await _scraper.fetch_news(http_client, ticker, n=news_per_ticker)
-        if not news:
-            return {
-                "ticker": ticker, "score_raw": 0.0, "reason": "no news",
-                "news_count": 0, "tokens_input": 0, "tokens_output": 0,
-                "model": model,
-            }
         return await _analyzer.score_sentiment(
-            llm, ticker, news, name=name, model=model,
+            llm, ticker, articles, name=name, model=model,
         )
 
 
 def _upsert_news_sentiment(
-    conn: sqlite3.Connection, asof: dt.date, rows: List[Dict[str, Any]]
+    conn: sqlite3.Connection, asof: dt.date,
+    rows: List[Dict[str, Any]], *, source: str = "articles",
 ) -> None:
     iso = asof.isoformat()
     data = [
         (
             r["ticker"], iso, r["score_raw"], r["reason"], r["news_count"],
-            r["tokens_input"], r["tokens_output"], r["model"],
+            r["tokens_input"], r["tokens_output"], r["model"], source,
         )
         for r in rows
     ]
     conn.executemany(
         """INSERT INTO news_sentiment
              (ticker, date, score_raw, reason, news_count,
-              tokens_input, tokens_output, model)
-           VALUES (?, ?, ?, ?, ?, ?, ?, ?)
+              tokens_input, tokens_output, model, source)
+           VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
            ON CONFLICT(ticker, date) DO UPDATE SET
              score_raw=excluded.score_raw,
              reason=excluded.reason,
              news_count=excluded.news_count,
              tokens_input=excluded.tokens_input,
              tokens_output=excluded.tokens_output,
-             model=excluded.model
+             model=excluded.model,
+             source=excluded.source
         """,
         data,
     )
@@ -97,11 +84,10 @@ async def refresh_daily(
     *,
     top_n: int = DEFAULT_TOP_N,
     concurrency: int = DEFAULT_CONCURRENCY,
-    news_per_ticker: int = DEFAULT_NEWS_PER_TICKER,
-    rate_limit_sec: float = DEFAULT_RATE_LIMIT_SEC,
+    max_news_per_ticker: int = DEFAULT_NEWS_PER_TICKER,
+    window_days: int = 1,
     model: str = _analyzer.DEFAULT_MODEL,
 ) -> Dict[str, Any]:
-    """Returns summary dict with top_pos/top_neg/token totals/failures."""
     started = time.time()
     tickers = _top_market_cap_tickers(conn, n=top_n)
     name_map = {
@@ -111,16 +97,20 @@ async def refresh_daily(
         ).fetchall()
     } if tickers else {}
 
-    sem = asyncio.Semaphore(concurrency)
+    articles_by_ticker, mapping_stats = articles_source.gather_articles_for_tickers(
+        conn, tickers, asof,
+        window_days=window_days,
+        max_per_ticker=max_news_per_ticker,
+    )
 
-    async with _make_http() as http_client, _make_llm() as llm:
-        tasks = [
-            _process_one(
-                t, name_map.get(t, t), sem, http_client, llm,
-                news_per_ticker, rate_limit_sec, model,
-            )
-            for t in tickers
-        ]
+    sem = asyncio.Semaphore(concurrency)
+    async with _make_llm() as llm:
+        tasks = []
+        for t in tickers:
+            arts = articles_by_ticker.get(t, [])
+            if not arts:
+                continue  # 매핑 0 — score 미생성
+            tasks.append(_process_one(t, name_map.get(t, t), arts, sem, llm, model))
         raw_results = await asyncio.gather(*tasks, return_exceptions=True)
 
     successes: List[Dict[str, Any]] = []
@@ -132,7 +122,7 @@ async def refresh_daily(
             successes.append(r)
 
     if successes:
-        _upsert_news_sentiment(conn, asof, successes)
+        _upsert_news_sentiment(conn, asof, successes, source="articles")
 
     top_pos = sorted(successes, key=lambda r: -r["score_raw"])[:5]
     top_neg = sorted(successes, key=lambda r: r["score_raw"])[:5]
@@ -147,4 +137,5 @@ async def refresh_daily(
         "top_pos": top_pos,
         "top_neg": top_neg,
         "model": model,
+        "mapping": mapping_stats,
     }

stock-lab/app/screener/ai_news/scraper.py

@@ -1,4 +1,11 @@
-"""네이버 finance 종목 뉴스 스크래핑."""
+"""[DEPRECATED] 네이버 finance 종목 뉴스 스크래핑.
+
+본 모듈은 ai_news Phase 1 (2026-05-14) 에서 더 이상 파이프라인에서 사용되지 않음.
+데이터 소스는 stock-lab 의 articles 테이블 (ai_news/articles_source.py) 로 전환됨.
+
+삭제 시점: Phase 2 (DART 도입) 결정 후. IC 검증 4주 누적 후 노드 활성화
+여부에 따라 본 모듈을 (a) 완전 삭제 또는 (b) ensemble fallback 으로 재활용.
+"""
 
 from __future__ import annotations
 

stock-lab/app/screener/ai_news/telegram.py

@@ -37,6 +37,7 @@ def build_message(
     top_neg: List[Dict[str, Any]],
     tokens_input: int,
     tokens_output: int,
+    mapping: Dict[str, int] | None = None,
 ) -> str:
     lines: List[str] = [
         f"🌅 *AI 뉴스 분석* \\({_escape(asof)} 08:00\\)",
@@ -57,9 +58,16 @@ def build_message(
         lines.append(_escape("- (없음)"))
 
     cost = _cost_won(tokens_input, tokens_output)
+    mapping_part = ""
+    if mapping:
+        mapping_part = (
+            f"매핑 {mapping['hit_tickers']}/100 ticker "
+            f"\\({mapping['matched_pairs']}쌍 / articles {mapping['total_articles']}건\\) · "
+        )
     lines += [
         "",
-        f"_분석: 시총 상위 100종목 · 토큰 {tokens_input:,} in / {tokens_output:,} out · "
+        f"_분석: 시총 상위 100종목 · {mapping_part}"
+        f"토큰 {tokens_input:,} in / {tokens_output:,} out · "
         f"약 ₩{cost:,}_",
     ]
     return "\n".join(lines)

stock-lab/app/screener/router.py

@@ -309,6 +309,7 @@ async def post_refresh_news_sentiment(asof: Optional[str] = None):
         top_pos=summary["top_pos"], top_neg=summary["top_neg"],
         tokens_input=summary["tokens_input"],
         tokens_output=summary["tokens_output"],
+        mapping=summary.get("mapping"),
     )
     return summary
 

stock-lab/app/screener/schema.py

@@ -115,6 +115,22 @@ CREATE TABLE IF NOT EXISTS screener_results (
 );
 CREATE INDEX IF NOT EXISTS idx_results_run_rank ON screener_results(run_id, rank);
 
+-- articles 테이블 (도메스틱/해외 뉴스 원본).
+-- 메인 app.db.init_db() 에서도 생성하지만, 테스트 환경 및 단독 screener 컨텍스트
+-- (ai_news.articles_source 등)에서도 참조 가능하도록 idempotent 하게 보장한다.
+CREATE TABLE IF NOT EXISTS articles (
+  id          INTEGER PRIMARY KEY AUTOINCREMENT,
+  hash        TEXT UNIQUE NOT NULL,
+  category    TEXT DEFAULT 'domestic',
+  title       TEXT NOT NULL,
+  link        TEXT,
+  summary     TEXT,
+  press       TEXT,
+  pub_date    TEXT,
+  crawled_at  TEXT
+);
+CREATE INDEX IF NOT EXISTS idx_articles_crawled ON articles(crawled_at DESC);
+
 CREATE TABLE IF NOT EXISTS news_sentiment (
   ticker          TEXT NOT NULL,
   date            TEXT NOT NULL,
@@ -124,6 +140,7 @@ CREATE TABLE IF NOT EXISTS news_sentiment (
   tokens_input    INTEGER NOT NULL DEFAULT 0,
   tokens_output   INTEGER NOT NULL DEFAULT 0,
   model           TEXT NOT NULL DEFAULT 'claude-haiku-4-5-20251001',
+  source          TEXT NOT NULL DEFAULT 'articles',
   created_at      TEXT NOT NULL DEFAULT (datetime('now','localtime')),
   PRIMARY KEY (ticker, date)
 );
@@ -158,6 +175,15 @@ def ensure_screener_schema(conn: sqlite3.Connection) -> None:
                 "UPDATE screener_settings SET weights_json=?, node_params_json=? WHERE id=1",
                 (json.dumps(w), json.dumps(p)),
             )
+    # news_sentiment.source 컬럼 1회 추가 (기존 운영 환경)
+    cols = {r[1] for r in conn.execute(
+        "PRAGMA table_info(news_sentiment)"
+    ).fetchall()}
+    if "source" not in cols:
+        conn.execute(
+            "ALTER TABLE news_sentiment "
+            "ADD COLUMN source TEXT NOT NULL DEFAULT 'articles'"
+        )
     existing = conn.execute("SELECT id FROM screener_settings WHERE id=1").fetchone()
     if existing is None:
         now = datetime.now(timezone.utc).isoformat()

stock-lab/tests/test_ai_news_analyzer.py

@@ -17,7 +17,10 @@ def _mk_llm(content_text: str, in_tokens: int = 100, out_tokens: int = 20):
     return llm
 
 
-NEWS = [{"title": "삼성전자, HBM 양산"}, {"title": "메모리 가격 반등"}]
+NEWS = [
+    {"title": "삼성전자, HBM 양산", "summary": "1분기 영업이익 사상 최대", "pub_date": "2026-05-14"},
+    {"title": "메모리 가격 반등", "summary": "", "pub_date": "2026-05-14"},
+]
 
 
 @pytest.mark.asyncio
@@ -53,3 +56,15 @@ async def test_score_sentiment_clamps_negative_out_of_range():
     llm = _mk_llm(json.dumps({"score": -42.0, "reason": "초악재"}))
     out = await analyzer.score_sentiment(llm, "005930", NEWS)
     assert out["score_raw"] == -10.0
+
+
+@pytest.mark.asyncio
+async def test_score_sentiment_includes_summary_in_prompt():
+    """summary 가 있으면 prompt 에 포함, 없으면 title 만."""
+    llm = _mk_llm(json.dumps({"score": 5.0, "reason": "ok"}))
+    await analyzer.score_sentiment(llm, "005930", NEWS, name="삼성전자")
+    call = llm.messages.create.call_args
+    user_msg = call.kwargs["messages"][0]["content"]
+    assert "1분기 영업이익 사상 최대" in user_msg  # summary 포함
+    assert "삼성전자, HBM 양산" in user_msg  # title 포함
+    assert "2026-05-14" in user_msg  # pub_date 포함

stock-lab/tests/test_ai_news_articles_source.py

@@ -0,0 +1,108 @@
+import datetime as dt
+import sqlite3
+import pytest
+
+from app.screener.ai_news import articles_source
+from app.screener.schema import ensure_screener_schema
+
+
+@pytest.fixture
+def conn():
+    c = sqlite3.connect(":memory:")
+    c.row_factory = sqlite3.Row
+    ensure_screener_schema(c)
+    yield c
+    c.close()
+
+
+def _seed_master(conn, ticker, name):
+    conn.execute(
+        "INSERT INTO krx_master (ticker, name, market, market_cap, updated_at) "
+        "VALUES (?, ?, 'KOSPI', 1000000000, datetime('now'))",
+        (ticker, name),
+    )
+
+
+def _seed_article(conn, title, summary="", crawled_at="2026-05-14T07:30:00"):
+    import hashlib
+    h = hashlib.md5(f"{title}|x".encode()).hexdigest()
+    conn.execute(
+        "INSERT INTO articles (hash, title, summary, link, press, pub_date, crawled_at) "
+        "VALUES (?, ?, ?, '', '', '2026-05-14', ?)",
+        (h, title, summary, crawled_at),
+    )
+
+
+ASOF = dt.date(2026, 5, 14)
+
+
+def test_single_ticker_match_in_title(conn):
+    _seed_master(conn, "005930", "삼성전자")
+    _seed_article(conn, "삼성전자, HBM 양산 가시화")
+    conn.commit()
+    out, stats = articles_source.gather_articles_for_tickers(
+        conn, ["005930"], ASOF, window_days=1, max_per_ticker=5,
+    )
+    assert len(out["005930"]) == 1
+    assert out["005930"][0]["title"] == "삼성전자, HBM 양산 가시화"
+    assert stats["matched_pairs"] == 1
+    assert stats["hit_tickers"] == 1
+
+
+def test_single_ticker_match_in_summary(conn):
+    _seed_master(conn, "005930", "삼성전자")
+    _seed_article(conn, "메모리 시장 회복세", summary="삼성전자가 1분기 어닝 서프라이즈")
+    conn.commit()
+    out, _ = articles_source.gather_articles_for_tickers(
+        conn, ["005930"], ASOF, window_days=1, max_per_ticker=5,
+    )
+    assert len(out["005930"]) == 1
+
+
+def test_multi_ticker_match(conn):
+    _seed_master(conn, "005930", "삼성전자")
+    _seed_master(conn, "000660", "SK하이닉스")
+    _seed_article(conn, "삼성전자와 SK하이닉스, 메모리 양산 경쟁")
+    conn.commit()
+    out, stats = articles_source.gather_articles_for_tickers(
+        conn, ["005930", "000660"], ASOF, window_days=1, max_per_ticker=5,
+    )
+    assert len(out["005930"]) == 1
+    assert len(out["000660"]) == 1
+    assert stats["matched_pairs"] == 2
+    assert stats["hit_tickers"] == 2
+
+
+def test_no_match_returns_empty_list(conn):
+    _seed_master(conn, "005930", "삼성전자")
+    _seed_article(conn, "엔비디아 실적 발표", summary="AI 칩 수요 견조")
+    conn.commit()
+    out, stats = articles_source.gather_articles_for_tickers(
+        conn, ["005930"], ASOF, window_days=1, max_per_ticker=5,
+    )
+    assert out["005930"] == []
+    assert stats["matched_pairs"] == 0
+    assert stats["hit_tickers"] == 0
+
+
+def test_max_per_ticker_caps_results(conn):
+    _seed_master(conn, "005930", "삼성전자")
+    for i in range(6):
+        _seed_article(conn, f"삼성전자 뉴스 #{i}", crawled_at=f"2026-05-14T0{i}:00:00")
+    conn.commit()
+    out, _ = articles_source.gather_articles_for_tickers(
+        conn, ["005930"], ASOF, window_days=1, max_per_ticker=5,
+    )
+    assert len(out["005930"]) == 5
+
+
+def test_window_days_filters_old_articles(conn):
+    _seed_master(conn, "005930", "삼성전자")
+    _seed_article(conn, "삼성전자 최신 뉴스", crawled_at="2026-05-14T07:00:00")
+    _seed_article(conn, "삼성전자 오래된 뉴스", crawled_at="2026-05-01T07:00:00")
+    conn.commit()
+    out, _ = articles_source.gather_articles_for_tickers(
+        conn, ["005930"], ASOF, window_days=1, max_per_ticker=5,
+    )
+    assert len(out["005930"]) == 1
+    assert "최신" in out["005930"][0]["title"]

stock-lab/tests/test_ai_news_pipeline.py

@@ -26,12 +26,15 @@ def conn():
 
 @pytest.mark.asyncio
 async def test_refresh_daily_happy_path(conn):
-    """3종목 mini integration — 각 종목별로 scraper/analyzer mock."""
+    """3종목 mini integration — articles_source mock + analyzer mock."""
     asof = dt.date(2026, 5, 13)
-    fake_news = [{"title": "헤드라인"}]
 
-    async def fake_fetch(client, ticker, n):
-        return fake_news
+    fake_articles_by_ticker = {
+        "005930": [{"title": "삼성 뉴스", "summary": "", "press": "", "pub_date": ""}],
+        "000660": [{"title": "SK 뉴스", "summary": "", "press": "", "pub_date": ""}],
+        "373220": [{"title": "LG 뉴스", "summary": "", "press": "", "pub_date": ""}],
+    }
+    fake_stats = {"total_articles": 3, "matched_pairs": 3, "hit_tickers": 3}
 
     scores_by_ticker = {
         "005930": 7.5, "000660": 4.0, "373220": -6.0,
@@ -43,43 +46,40 @@ async def test_refresh_daily_happy_path(conn):
             "tokens_input": 100, "tokens_output": 20, "model": model,
         }
 
-    with patch.object(pipeline, "_scraper") as ms, \
+    with patch.object(pipeline, "articles_source") as mas, \
          patch.object(pipeline, "_analyzer") as ma, \
-         patch.object(pipeline, "_make_llm") as ml, \
-         patch.object(pipeline, "_make_http") as mh:
-        ms.fetch_news = fake_fetch
+         patch.object(pipeline, "_make_llm") as ml:
+        mas.gather_articles_for_tickers = MagicMock(
+            return_value=(fake_articles_by_ticker, fake_stats)
+        )
         ma.score_sentiment = fake_score
         ml.return_value.__aenter__.return_value = AsyncMock()
         ml.return_value.__aexit__.return_value = None
-        mh.return_value.__aenter__.return_value = AsyncMock()
-        mh.return_value.__aexit__.return_value = None
-        result = await pipeline.refresh_daily(conn, asof, concurrency=3, rate_limit_sec=0)
+        result = await pipeline.refresh_daily(conn, asof, concurrency=3)
 
     assert result["asof"] == "2026-05-13"
     assert result["updated"] == 3
     assert result["failures"] == []
-    assert len(result["top_pos"]) == 3
-    assert result["top_pos"][0]["ticker"] == "005930"  # 가장 큰 점수
-    assert result["top_neg"][0]["ticker"] == "373220"  # 가장 작은 점수
-    assert result["tokens_input"] == 300
-    assert result["tokens_output"] == 60
+    assert result["top_pos"][0]["ticker"] == "005930"
+    assert result["top_neg"][0]["ticker"] == "373220"
+    assert result["mapping"] == fake_stats
 
-    # DB upsert 확인
-    rows = conn.execute("SELECT ticker, score_raw FROM news_sentiment WHERE date=?",
-                        ("2026-05-13",)).fetchall()
+    rows = conn.execute("SELECT ticker, score_raw, source FROM news_sentiment "
+                        "WHERE date=?", ("2026-05-13",)).fetchall()
     assert len(rows) == 3
-    by_ticker = {r["ticker"]: r["score_raw"] for r in rows}
-    assert by_ticker["005930"] == 7.5
-    assert by_ticker["373220"] == -6.0
+    assert all(r["source"] == "articles" for r in rows)
 
 
 @pytest.mark.asyncio
 async def test_refresh_daily_failures_isolated(conn):
-    """한 종목이 예외 던져도 나머지 종목은 정상 처리."""
     asof = dt.date(2026, 5, 13)
 
-    async def fake_fetch(client, ticker, n):
-        return [{"title": "h"}]
+    fake_articles_by_ticker = {
+        "005930": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
+        "000660": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
+        "373220": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
+    }
+    fake_stats = {"total_articles": 3, "matched_pairs": 3, "hit_tickers": 3}
 
     async def fake_score(llm, ticker, news, *, name=None, model="m"):
         if ticker == "000660":
@@ -89,22 +89,57 @@ async def test_refresh_daily_failures_isolated(conn):
             "tokens_input": 100, "tokens_output": 20, "model": model,
         }
 
-    with patch.object(pipeline, "_scraper") as ms, \
+    with patch.object(pipeline, "articles_source") as mas, \
          patch.object(pipeline, "_analyzer") as ma, \
-         patch.object(pipeline, "_make_llm") as ml, \
-         patch.object(pipeline, "_make_http") as mh:
-        ms.fetch_news = fake_fetch
+         patch.object(pipeline, "_make_llm") as ml:
+        mas.gather_articles_for_tickers = MagicMock(
+            return_value=(fake_articles_by_ticker, fake_stats)
+        )
         ma.score_sentiment = fake_score
         ml.return_value.__aenter__.return_value = AsyncMock()
         ml.return_value.__aexit__.return_value = None
-        mh.return_value.__aenter__.return_value = AsyncMock()
-        mh.return_value.__aexit__.return_value = None
-        result = await pipeline.refresh_daily(conn, asof, concurrency=3, rate_limit_sec=0)
+        result = await pipeline.refresh_daily(conn, asof, concurrency=3)
 
     assert result["updated"] == 2
     assert len(result["failures"]) == 1
 
 
+@pytest.mark.asyncio
+async def test_refresh_daily_no_match_ticker_skipped(conn):
+    """매핑 0인 ticker 는 LLM 호출 skip + news_sentiment 행 미생성."""
+    asof = dt.date(2026, 5, 13)
+
+    fake_articles_by_ticker = {
+        "005930": [{"title": "삼성", "summary": "", "press": "", "pub_date": ""}],
+        "000660": [],  # 매핑 없음
+        "373220": [],  # 매핑 없음
+    }
+    fake_stats = {"total_articles": 1, "matched_pairs": 1, "hit_tickers": 1}
+
+    async def fake_score(llm, ticker, news, *, name=None, model="m"):
+        return {
+            "ticker": ticker, "score_raw": 5.0, "reason": "r",
+            "news_count": 1, "tokens_input": 100, "tokens_output": 20,
+            "model": model,
+        }
+
+    with patch.object(pipeline, "articles_source") as mas, \
+         patch.object(pipeline, "_analyzer") as ma, \
+         patch.object(pipeline, "_make_llm") as ml:
+        mas.gather_articles_for_tickers = MagicMock(
+            return_value=(fake_articles_by_ticker, fake_stats)
+        )
+        ma.score_sentiment = fake_score
+        ml.return_value.__aenter__.return_value = AsyncMock()
+        ml.return_value.__aexit__.return_value = None
+        result = await pipeline.refresh_daily(conn, asof, concurrency=3)
+
+    assert result["updated"] == 1
+    rows = conn.execute("SELECT ticker FROM news_sentiment "
+                        "WHERE date=?", ("2026-05-13",)).fetchall()
+    assert {r["ticker"] for r in rows} == {"005930"}
+
+
 def test_top_market_cap_tickers(conn):
     out = pipeline._top_market_cap_tickers(conn, n=2)
     assert out == ["005930", "000660"]

stock-lab/tests/test_ai_news_router.py

@@ -20,17 +20,17 @@ def test_refresh_news_sentiment_weekday_invokes_pipeline():
         "asof": "2026-05-13", "updated": 3, "failures": [],
         "duration_sec": 1.0, "tokens_input": 100, "tokens_output": 20,
         "top_pos": [], "top_neg": [], "model": "m",
+        "mapping": {"total_articles": 5, "matched_pairs": 8, "hit_tickers": 3},
     }
     with patch("app.screener.router._ai_pipeline") as mp, \
          patch("app.screener.router._ai_telegram") as mt:
         mp.refresh_daily = AsyncMock(return_value=fake_summary)
-        mt.build_message = lambda **kw: "BUILT_TEXT"
+        mt.build_message = lambda **kw: f"TEXT_with_mapping={kw.get('mapping')}"
         client = TestClient(app)
         resp = client.post(
             "/api/stock/screener/snapshot/refresh-news-sentiment?asof=2026-05-13"
         )
     assert resp.status_code == 200
     body = resp.json()
-    assert body["asof"] == "2026-05-13"
-    assert body["updated"] == 3
-    assert body["telegram_text"] == "BUILT_TEXT"
+    assert body["mapping"]["hit_tickers"] == 3
+    assert "mapping=" in body["telegram_text"]

stock-lab/tests/test_ai_news_telegram.py

@@ -53,3 +53,27 @@ def test_build_message_empty_lists():
     # 빈 리스트라도 헤더는 있어야 함
     assert "호재 Top" in msg
     assert "악재 Top" in msg
+
+
+def test_build_message_includes_mapping_line():
+    msg = tg.build_message(
+        asof="2026-05-14",
+        top_pos=[_row("005930", 8.5, "HBM 호재")],
+        top_neg=[],
+        tokens_input=1000, tokens_output=200,
+        mapping={"total_articles": 35, "matched_pairs": 50, "hit_tickers": 42},
+    )
+    assert "매핑" in msg
+    assert "42" in msg
+    assert "50" in msg
+    assert "35" in msg
+
+
+def test_build_message_without_mapping_omits_line():
+    msg = tg.build_message(
+        asof="2026-05-14",
+        top_pos=[],
+        top_neg=[],
+        tokens_input=1000, tokens_output=200,
+    )
+    assert "매핑" not in msg