From 3ded781059b351bf31a84b458b1d0ee82edf9cd8 Mon Sep 17 00:00:00 2001
From: gahusb <bgg8988@gmail.com>
Date: Tue, 12 May 2026 09:02:28 +0900
Subject: [PATCH] =?UTF-8?q?feat(stock-lab):=20RsRating=20=EB=85=B8?=
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---
 stock-lab/app/screener/nodes/rs_rating.py     | 48 +++++++++++++++++++
 .../app/test_screener_nodes_rs_rating.py      | 25 ++++++++++
 2 files changed, 73 insertions(+)
 create mode 100644 stock-lab/app/screener/nodes/rs_rating.py
 create mode 100644 stock-lab/app/test_screener_nodes_rs_rating.py

diff --git a/stock-lab/app/screener/nodes/rs_rating.py b/stock-lab/app/screener/nodes/rs_rating.py
new file mode 100644
index 0000000..895002d
--- /dev/null
+++ b/stock-lab/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)
diff --git a/stock-lab/app/test_screener_nodes_rs_rating.py b/stock-lab/app/test_screener_nodes_rs_rating.py
new file mode 100644
index 0000000..ddbcaf0
--- /dev/null
+++ b/stock-lab/app/test_screener_nodes_rs_rating.py
@@ -0,0 +1,25 @@
+import datetime as dt
+import pandas as pd
+
+from app.screener.engine import ScreenContext
+from app.screener.nodes.rs_rating import RsRating
+from app.screener._test_fixtures import make_master, make_prices, make_flow, make_kospi
+
+
+def _ctx(master, prices, flow, kospi):
+    return ScreenContext(master=master, prices=prices, flow=flow,
+                         kospi=kospi, asof=dt.date(2026, 5, 12))
+
+
+def test_outperformer_gets_higher_score():
+    asof = dt.date(2026, 5, 12)
+    master = make_master(["UP", "DN"])
+    up = make_prices(["UP"], days=260, asof=asof, trend_pct=0.3)
+    dn = make_prices(["DN"], days=260, asof=asof, trend_pct=-0.1)
+    prices = pd.concat([up, dn], ignore_index=True)
+    flow = make_flow(["UP", "DN"], days=260, asof=asof)
+    kospi = make_kospi(days=260, asof=asof, trend_pct=0.0)
+
+    out = RsRating().compute(_ctx(master, prices, flow, kospi),
+                             RsRating.default_params)
+    assert out["UP"] > out["DN"]