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/nodes/rs_rating.py

@@ -0,0 +1,48 @@
+"""RS Rating — IBD 가중 (3m=2,6m=1,9m=1,12m=1)."""
+
+import pandas as pd
+
+from .base import ScoreNode, percentile_rank
+
+
+_PERIOD_TO_DAYS = {"3m": 63, "6m": 126, "9m": 189, "12m": 252}
+
+
+class RsRating(ScoreNode):
+    name  = "rs_rating"
+    label = "RS Rating (시장 대비 상대강도)"
+    default_params = {"weights": {"3m": 2, "6m": 1, "9m": 1, "12m": 1}}
+    param_schema = {
+        "type": "object",
+        "properties": {
+            "weights": {"type": "object"}
+        },
+    }
+
+    def compute(self, ctx, params: dict) -> pd.Series:
+        weights: dict = params.get("weights", self.default_params["weights"])
+        prices = ctx.prices
+        kospi = ctx.kospi
+        if prices.empty or kospi.empty:
+            return pd.Series(dtype=float)
+
+        ordered = prices.sort_values("date")
+
+        def _excess_for_ticker(g: pd.DataFrame) -> float:
+            closes = g.set_index("date")["close"]
+            total = 0.0
+            wsum = 0.0
+            for period, w in weights.items():
+                k = _PERIOD_TO_DAYS.get(period, 0)
+                if len(closes) <= k or len(kospi) <= k:
+                    continue
+                r_stock = closes.iloc[-1] / closes.iloc[-(k + 1)] - 1
+                r_market = kospi.iloc[-1] / kospi.iloc[-(k + 1)] - 1
+                total += w * (r_stock - r_market)
+                wsum += w
+            return total / wsum if wsum else float("nan")
+
+        raw = ordered.groupby("ticker", group_keys=False).apply(
+            _excess_for_ticker, include_groups=False
+        )
+        return percentile_rank(raw).fillna(50.0)