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.
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145
stock/tests/test_ai_news_pipeline.py
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145
stock/tests/test_ai_news_pipeline.py
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import datetime as dt
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import sqlite3
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from app.screener.ai_news import pipeline
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from app.screener.schema import ensure_screener_schema
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@pytest.fixture
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def conn():
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c = sqlite3.connect(":memory:")
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c.row_factory = sqlite3.Row
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ensure_screener_schema(c)
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# 시총 상위 3종목 시드
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c.execute("INSERT INTO krx_master (ticker, name, market, market_cap, updated_at) "
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"VALUES (?, ?, 'KOSPI', ?, datetime('now'))", ("005930", "삼성전자", 9_000_000))
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c.execute("INSERT INTO krx_master (ticker, name, market, market_cap, updated_at) "
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"VALUES (?, ?, 'KOSPI', ?, datetime('now'))", ("000660", "SK하이닉스", 8_000_000))
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c.execute("INSERT INTO krx_master (ticker, name, market, market_cap, updated_at) "
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"VALUES (?, ?, 'KOSPI', ?, datetime('now'))", ("373220", "LG에너지솔루션", 7_000_000))
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c.commit()
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yield c
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c.close()
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@pytest.mark.asyncio
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async def test_refresh_daily_happy_path(conn):
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"""3종목 mini integration — articles_source mock + analyzer mock."""
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asof = dt.date(2026, 5, 13)
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fake_articles_by_ticker = {
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"005930": [{"title": "삼성 뉴스", "summary": "", "press": "", "pub_date": ""}],
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"000660": [{"title": "SK 뉴스", "summary": "", "press": "", "pub_date": ""}],
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"373220": [{"title": "LG 뉴스", "summary": "", "press": "", "pub_date": ""}],
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}
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fake_stats = {"total_articles": 3, "matched_pairs": 3, "hit_tickers": 3}
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scores_by_ticker = {
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"005930": 7.5, "000660": 4.0, "373220": -6.0,
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}
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async def fake_score(llm, ticker, news, *, name=None, model="m"):
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return {
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"ticker": ticker, "score_raw": scores_by_ticker[ticker],
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"reason": f"r{ticker}", "news_count": 1,
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"tokens_input": 100, "tokens_output": 20, "model": model,
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}
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with patch.object(pipeline, "articles_source") as mas, \
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patch.object(pipeline, "_analyzer") as ma, \
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patch.object(pipeline, "_make_llm") as ml:
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mas.gather_articles_for_tickers = MagicMock(
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return_value=(fake_articles_by_ticker, fake_stats)
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)
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ma.score_sentiment = fake_score
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ml.return_value.__aenter__.return_value = AsyncMock()
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ml.return_value.__aexit__.return_value = None
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result = await pipeline.refresh_daily(conn, asof, concurrency=3)
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assert result["asof"] == "2026-05-13"
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assert result["updated"] == 3
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assert result["failures"] == []
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assert result["top_pos"][0]["ticker"] == "005930"
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assert result["top_neg"][0]["ticker"] == "373220"
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assert result["mapping"] == fake_stats
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rows = conn.execute("SELECT ticker, score_raw, source FROM news_sentiment "
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"WHERE date=?", ("2026-05-13",)).fetchall()
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assert len(rows) == 3
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assert all(r["source"] == "articles" for r in rows)
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@pytest.mark.asyncio
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async def test_refresh_daily_failures_isolated(conn):
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asof = dt.date(2026, 5, 13)
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fake_articles_by_ticker = {
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"005930": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
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"000660": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
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"373220": [{"title": "h", "summary": "", "press": "", "pub_date": ""}],
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}
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fake_stats = {"total_articles": 3, "matched_pairs": 3, "hit_tickers": 3}
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async def fake_score(llm, ticker, news, *, name=None, model="m"):
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if ticker == "000660":
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raise RuntimeError("llm exploded")
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return {
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"ticker": ticker, "score_raw": 5.0, "reason": "r", "news_count": 1,
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"tokens_input": 100, "tokens_output": 20, "model": model,
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}
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with patch.object(pipeline, "articles_source") as mas, \
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patch.object(pipeline, "_analyzer") as ma, \
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patch.object(pipeline, "_make_llm") as ml:
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mas.gather_articles_for_tickers = MagicMock(
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return_value=(fake_articles_by_ticker, fake_stats)
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)
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ma.score_sentiment = fake_score
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ml.return_value.__aenter__.return_value = AsyncMock()
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ml.return_value.__aexit__.return_value = None
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result = await pipeline.refresh_daily(conn, asof, concurrency=3)
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assert result["updated"] == 2
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assert len(result["failures"]) == 1
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@pytest.mark.asyncio
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async def test_refresh_daily_no_match_ticker_skipped(conn):
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"""매핑 0인 ticker 는 LLM 호출 skip + news_sentiment 행 미생성."""
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asof = dt.date(2026, 5, 13)
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fake_articles_by_ticker = {
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"005930": [{"title": "삼성", "summary": "", "press": "", "pub_date": ""}],
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"000660": [], # 매핑 없음
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"373220": [], # 매핑 없음
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}
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fake_stats = {"total_articles": 1, "matched_pairs": 1, "hit_tickers": 1}
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async def fake_score(llm, ticker, news, *, name=None, model="m"):
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return {
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"ticker": ticker, "score_raw": 5.0, "reason": "r",
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"news_count": 1, "tokens_input": 100, "tokens_output": 20,
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"model": model,
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}
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with patch.object(pipeline, "articles_source") as mas, \
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patch.object(pipeline, "_analyzer") as ma, \
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patch.object(pipeline, "_make_llm") as ml:
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mas.gather_articles_for_tickers = MagicMock(
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return_value=(fake_articles_by_ticker, fake_stats)
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)
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ma.score_sentiment = fake_score
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ml.return_value.__aenter__.return_value = AsyncMock()
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ml.return_value.__aexit__.return_value = None
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result = await pipeline.refresh_daily(conn, asof, concurrency=3)
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assert result["updated"] == 1
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rows = conn.execute("SELECT ticker FROM news_sentiment "
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"WHERE date=?", ("2026-05-13",)).fetchall()
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assert {r["ticker"] for r in rows} == {"005930"}
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def test_top_market_cap_tickers(conn):
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out = pipeline._top_market_cap_tickers(conn, n=2)
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assert out == ["005930", "000660"]
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