fix(ai_news): set weight=0 and add Spearman IC validation harness

검증 전 gradient 차단 + IC 측정 인프라.

- schema.py: DEFAULT_WEIGHTS["ai_news"] 0.8 → 0.0
  + 1회성 migration: 기존 운영 row 의 0.8 값 자동 reset
  (사용자가 명시 조정한 다른 값은 그대로 유지)
- ai_news/validation.py: compute_ic() — 일자별 score_raw × forward
  return Spearman 상관, ic_mean/ic_std/ic_per_day 반환, verdict 분류
  (skip/weak/strong)
- router.py: GET /api/stock/screener/ai-news/ic?days=30&horizon=1
- 단위 테스트 5개: empty DB, strong +IC, random ≈0 IC, min_news_count
  필터, horizon=5

배경: adversarial review 결과 — ai_news 가중치 0.8 이 검증 없이 출시됨.
4주+ 데이터 누적 후 IC > 0.05 확인 전까지 데이터 수집은 계속하되
가중합 영향만 차단. 운영 DB row 의 0.8 → 0.0 자동 reset 도 같은 의도.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

stock-lab/tests/test_ai_news_validation.py

@@ -0,0 +1,120 @@
+"""Tests for ai_news validation harness (Spearman IC)."""
+
+import datetime as dt
+import sqlite3
+import pytest
+
+from app.screener.ai_news import validation
+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_sentiment(conn, date, ticker, score, news_count=3):
+    conn.execute(
+        "INSERT INTO news_sentiment (ticker, date, score_raw, reason, news_count, "
+        "tokens_input, tokens_output, model) "
+        "VALUES (?, ?, ?, 'r', ?, 100, 20, 'm')",
+        (ticker, date, score, news_count),
+    )
+
+
+def _seed_price(conn, ticker, date, close):
+    conn.execute(
+        "INSERT INTO krx_daily_prices (ticker, date, close) VALUES (?, ?, ?)",
+        (ticker, date, close),
+    )
+
+
+def test_empty_db_returns_skip(conn):
+    out = validation.compute_ic(conn, days=30, horizon=1, asof_today=dt.date(2026, 5, 14))
+    assert out["ic_count"] == 0
+    assert out["verdict"] == "skip"
+    assert out["ic_mean"] is None
+
+
+def test_strong_positive_ic(conn):
+    """5종목 × 12일 — 점수가 높을수록 다음날 수익률 높게 시드 → IC ≈ +1.
+
+    score 가 변하지 않는 ticker × day-wise close 로 정확한 monotonic 관계 시드.
+    """
+    base_date = dt.date(2026, 5, 1)
+    # 가격 13일치 시드 (day0..day12). ticker별 base 다르고 (score-기반) day마다 다른 close.
+    for i, ticker in enumerate(["A", "B", "C", "D", "E"]):
+        score = i * 2.0 - 4.0  # ticker별 score 고정 (-4, -2, 0, +2, +4)
+        # day 0 close=100, day n close=100+(score × n)
+        for day in range(13):
+            d = (base_date + dt.timedelta(days=day)).isoformat()
+            _seed_price(conn, ticker, d, 100.0 + score * day)
+            if day < 12:
+                _seed_sentiment(conn, d, ticker, score)
+    conn.commit()
+
+    out = validation.compute_ic(conn, days=30, horizon=1, asof_today=dt.date(2026, 5, 14))
+    assert out["ic_count"] >= 10
+    assert out["ic_mean"] > 0.5
+    assert out["verdict"] == "strong"
+
+
+def test_zero_ic_random_data(conn):
+    """점수와 수익률이 무관 → IC ≈ 0."""
+    import random
+    random.seed(42)
+    base_date = dt.date(2026, 5, 1)
+    for ticker in ["A", "B", "C", "D", "E", "F", "G"]:
+        for day in range(13):
+            d = (base_date + dt.timedelta(days=day)).isoformat()
+            _seed_price(conn, ticker, d, 100.0 + random.uniform(-5, 5))
+            if day < 12:
+                _seed_sentiment(conn, d, ticker, random.uniform(-10, 10))
+    conn.commit()
+
+    out = validation.compute_ic(conn, days=30, horizon=1, asof_today=dt.date(2026, 5, 14))
+    assert out["ic_count"] >= 10
+    assert abs(out["ic_mean"]) < 0.3  # 약한 신호 — verdict는 weak 가능
+    assert out["verdict"] in ("weak", "strong")  # 시드에 따라 약간 흔들림
+
+
+def test_min_news_count_filter(conn):
+    """news_count < min_news_count 인 row 는 제외."""
+    _seed_sentiment(conn, "2026-05-13", "A", 5.0, news_count=0)
+    _seed_sentiment(conn, "2026-05-13", "B", -5.0, news_count=3)
+    _seed_price(conn, "A", "2026-05-13", 100.0)
+    _seed_price(conn, "A", "2026-05-14", 105.0)
+    _seed_price(conn, "B", "2026-05-13", 100.0)
+    _seed_price(conn, "B", "2026-05-14", 95.0)
+    conn.commit()
+
+    out = validation.compute_ic(
+        conn, days=30, horizon=1, min_news_count=1,
+        asof_today=dt.date(2026, 5, 14),
+    )
+    # A 가 필터됨 → 1종목만 남으면 Spearman 계산 불가 (< 5) → skip
+    assert out["ic_count"] == 0
+
+
+def test_horizon_5_days(conn):
+    """horizon=5 면 close[date+5] / close[date] - 1 사용."""
+    base_date = dt.date(2026, 5, 1)
+    for day in range(20):
+        d = (base_date + dt.timedelta(days=day)).isoformat()
+        for i, ticker in enumerate(["A", "B", "C", "D", "E"]):
+            _seed_sentiment(conn, d, ticker, i * 2.0 - 4.0)
+    # 가격: A=오름, B=오름, C=평, D=내림, E=내림
+    for day in range(25):
+        d = (base_date + dt.timedelta(days=day)).isoformat()
+        for i, ticker in enumerate(["A", "B", "C", "D", "E"]):
+            slope = i - 2  # -2 ~ +2
+            _seed_price(conn, ticker, d, 100.0 + slope * day)
+    conn.commit()
+
+    out = validation.compute_ic(conn, days=30, horizon=5, asof_today=dt.date(2026, 5, 25))
+    assert out["horizon_days"] == 5
+    assert out["ic_count"] > 0