feat(stock): webai_cache module (TTLCache for SP-A2)

3개의 TTLCache (portfolio 120s · news 600s · screener 180s) +
헬퍼 함수. screener key는 mode + top_n + weights canonical hash로
분기. 다음 커밋에서 /api/webai/portfolio·news-sentiment·screener/run
3 endpoint에 적용.

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

stock/app/webai_cache.py

@@ -0,0 +1,68 @@
+"""SP-A2 — NAS stock의 /api/webai/* 엔드포인트 in-memory TTLCache.
+
+web-ai 측 캐시(stock_client._TTL)가 miss됐을 때도 NAS에서 같은 데이터를
+KIS·LLM 재호출 없이 즉시 반환하기 위한 2-layer 캐시의 server 측.
+V1+V2가 동시 호출해도 NAS는 1회만 계산.
+
+TTL 정책 (spec §10 SP-A2):
+- portfolio:  120s (web-ai TTL 180s 보다 짧게 — 변경 감지 가능)
+- news:       600s (sentiment는 일 단위)
+- screener:   180s
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+from typing import Any, Optional
+
+from cachetools import TTLCache
+
+
+PORTFOLIO_CACHE: TTLCache = TTLCache(maxsize=1, ttl=120.0)
+NEWS_CACHE: TTLCache = TTLCache(maxsize=10, ttl=600.0)
+SCREENER_CACHE: TTLCache = TTLCache(maxsize=10, ttl=180.0)
+
+
+# ----- portfolio -----
+
+def cache_get_portfolio() -> Optional[Any]:
+    return PORTFOLIO_CACHE.get("result")
+
+
+def cache_set_portfolio(value: Any) -> None:
+    PORTFOLIO_CACHE["result"] = value
+
+
+# ----- news-sentiment -----
+
+def _news_key(date: Optional[str]) -> str:
+    return date if date else "latest"
+
+
+def cache_get_news(date: Optional[str]) -> Optional[Any]:
+    return NEWS_CACHE.get(_news_key(date))
+
+
+def cache_set_news(date: Optional[str], value: Any) -> None:
+    NEWS_CACHE[_news_key(date)] = value
+
+
+# ----- screener -----
+
+def _screener_key(mode: str, top_n: int, weights: Optional[dict]) -> str:
+    """mode + top_n + weights canonical hash. weights 객체 동등성을 키로."""
+    if weights is None:
+        w_repr = "none"
+    else:
+        # canonical: sorted keys → md5 hex (긴 weights도 짧은 키로)
+        canon = json.dumps(weights, sort_keys=True, ensure_ascii=False)
+        w_repr = hashlib.md5(canon.encode("utf-8")).hexdigest()[:12]
+    return f"{mode}:{top_n}:{w_repr}"
+
+
+def cache_get_screener(mode: str, top_n: int, weights: Optional[dict]) -> Optional[Any]:
+    return SCREENER_CACHE.get(_screener_key(mode, top_n, weights))
+
+
+def cache_set_screener(mode: str, top_n: int, weights: Optional[dict], value: Any) -> None:
+    SCREENER_CACHE[_screener_key(mode, top_n, weights)] = value