refactor: rename stock-lab → stock (graduation)

- git mv stock-lab/ → stock/
- docker-compose.yml: 서비스 키 + container_name + build.context +
  frontend.depends_on + agent-office STOCK_LAB_URL → STOCK_URL
- agent-office/app: config.py, service_proxy.py, agents/stock.py, tests/
  STOCK_LAB_URL → STOCK_URL
- nginx/default.conf: proxy_pass http://stock-lab → http://stock (3 lines)
- CLAUDE.md / README.md / STATUS.md / scripts/ 문구 갱신
- stock/ 내부 자기 참조 갱신

lab 네이밍 정책 (feedback_lab_naming.md) graduation.
API URL / Python import / DB 파일명 변경 없음.

stock/app/screener/ai_news/pipeline.py

@@ -0,0 +1,141 @@
+"""ai_news refresh pipeline — 시총 상위 N종목 병렬 처리."""
+
+from __future__ import annotations
+
+import asyncio
+import datetime as dt
+import logging
+import os
+import sqlite3
+import time
+from typing import Any, Dict, List
+
+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
+
+
+def _top_market_cap_tickers(conn: sqlite3.Connection, n: int) -> List[str]:
+    rows = conn.execute(
+        "SELECT ticker FROM krx_master "
+        "WHERE market_cap IS NOT NULL AND is_preferred=0 AND is_spac=0 "
+        "ORDER BY market_cap DESC LIMIT ?",
+        (n,),
+    ).fetchall()
+    return [r[0] for r in rows]
+
+
+def _make_llm():
+    """Anthropic AsyncClient — env에 ANTHROPIC_API_KEY 필수."""
+    from anthropic import AsyncAnthropic
+    return AsyncAnthropic(api_key=os.environ["ANTHROPIC_API_KEY"])
+
+
+async def _process_one(
+    ticker: str, name: str, articles: List[Dict[str, Any]],
+    sem: asyncio.Semaphore, llm, model: str,
+) -> Dict[str, Any]:
+    async with sem:
+        return await _analyzer.score_sentiment(
+            llm, ticker, articles, name=name, model=model,
+        )
+
+
+def _upsert_news_sentiment(
+    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"], source,
+        )
+        for r in rows
+    ]
+    conn.executemany(
+        """INSERT INTO news_sentiment
+             (ticker, date, score_raw, reason, news_count,
+              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,
+             source=excluded.source
+        """,
+        data,
+    )
+    conn.commit()
+
+
+async def refresh_daily(
+    conn: sqlite3.Connection,
+    asof: dt.date,
+    *,
+    top_n: int = DEFAULT_TOP_N,
+    concurrency: int = DEFAULT_CONCURRENCY,
+    max_news_per_ticker: int = DEFAULT_NEWS_PER_TICKER,
+    window_days: int = 1,
+    model: str = _analyzer.DEFAULT_MODEL,
+) -> Dict[str, Any]:
+    started = time.time()
+    tickers = _top_market_cap_tickers(conn, n=top_n)
+    name_map = {
+        r[0]: r[1] for r in conn.execute(
+            f"SELECT ticker, name FROM krx_master WHERE ticker IN "
+            f"({','.join('?' * len(tickers))})", tickers,
+        ).fetchall()
+    } if tickers else {}
+
+    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,
+    )
+
+    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]] = []
+    failures: List[str] = []
+    for r in raw_results:
+        if isinstance(r, BaseException):
+            failures.append(repr(r))
+        elif isinstance(r, dict):
+            successes.append(r)
+
+    if 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]
+
+    return {
+        "asof": asof.isoformat(),
+        "updated": len(successes),
+        "failures": failures,
+        "duration_sec": round(time.time() - started, 2),
+        "tokens_input": sum(r["tokens_input"] for r in successes),
+        "tokens_output": sum(r["tokens_output"] for r in successes),
+        "top_pos": top_pos,
+        "top_neg": top_neg,
+        "model": model,
+        "mapping": mapping_stats,
+    }