From 804fdcba2619c0605aafc08960f90e9db49c09b5 Mon Sep 17 00:00:00 2001
From: gahusb <bgg8988@gmail.com>
Date: Tue, 12 May 2026 08:19:44 +0900
Subject: [PATCH] =?UTF-8?q?feat(stock-lab):=20ForeignBuy=20=EB=85=B8?=
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---
 stock-lab/app/screener/nodes/foreign_buy.py   | 33 +++++++++++++++++++
 .../app/test_screener_nodes_foreign_buy.py    | 32 ++++++++++++++++++
 2 files changed, 65 insertions(+)
 create mode 100644 stock-lab/app/screener/nodes/foreign_buy.py
 create mode 100644 stock-lab/app/test_screener_nodes_foreign_buy.py

diff --git a/stock-lab/app/screener/nodes/foreign_buy.py b/stock-lab/app/screener/nodes/foreign_buy.py
new file mode 100644
index 0000000..f035a96
--- /dev/null
+++ b/stock-lab/app/screener/nodes/foreign_buy.py
@@ -0,0 +1,33 @@
+"""외국인 N일 누적 순매수 강도 (시총 대비)."""
+
+import pandas as pd
+
+from .base import ScoreNode, percentile_rank
+
+
+class ForeignBuy(ScoreNode):
+    name  = "foreign_buy"
+    label = "외국인 누적 순매수"
+    default_params = {"window_days": 5}
+    param_schema = {
+        "type": "object",
+        "properties": {
+            "window_days": {"type": "integer", "minimum": 1, "maximum": 60, "default": 5}
+        },
+    }
+
+    def compute(self, ctx, params: dict) -> pd.Series:
+        window = int(params.get("window_days", 5))
+        flow = ctx.flow
+        if flow.empty:
+            return pd.Series(dtype=float)
+
+        last_dates = (
+            flow.sort_values("date").groupby("ticker").tail(window)
+        )
+        net_sum = last_dates.groupby("ticker")["foreign_net"].sum()
+
+        market_cap = ctx.master["market_cap"].fillna(0).reindex(net_sum.index)
+        raw = (net_sum / market_cap.replace(0, pd.NA)).astype(float)
+
+        return percentile_rank(raw).fillna(50.0)
diff --git a/stock-lab/app/test_screener_nodes_foreign_buy.py b/stock-lab/app/test_screener_nodes_foreign_buy.py
new file mode 100644
index 0000000..f795f2e
--- /dev/null
+++ b/stock-lab/app/test_screener_nodes_foreign_buy.py
@@ -0,0 +1,32 @@
+import datetime as dt
+import pandas as pd
+
+from app.screener.engine import ScreenContext
+from app.screener.nodes.foreign_buy import ForeignBuy
+from app.screener._test_fixtures import make_master, make_prices, make_flow
+
+
+def _ctx(master, prices, flow):
+    return ScreenContext(master=master, prices=prices, flow=flow,
+                         kospi=pd.Series(dtype=float, name="kospi"),
+                         asof=dt.date(2026, 5, 12))
+
+
+def test_higher_foreign_buy_gets_higher_score():
+    asof = dt.date(2026, 5, 12)
+    master = make_master(["A", "B"])
+    prices = make_prices(["A", "B"], days=30, asof=asof)
+    flow = make_flow(["A", "B"], days=30, asof=asof,
+                     foreign_per_day={"A": 100_000_000, "B": 0})
+    out = ForeignBuy().compute(_ctx(master, prices, flow), {"window_days": 5})
+    assert out["A"] > out["B"]
+    assert 0 <= out.min() <= out.max() <= 100
+
+
+def test_all_zero_returns_50():
+    asof = dt.date(2026, 5, 12)
+    master = make_master(["A", "B"])
+    prices = make_prices(["A", "B"], days=30, asof=asof)
+    flow = make_flow(["A", "B"], days=30, asof=asof, foreign_per_day={"A": 0, "B": 0})
+    out = ForeignBuy().compute(_ctx(master, prices, flow), {"window_days": 5})
+    assert (out == 50.0).all()