feat(stock-lab): MaAlignment 노드 — 이평선 정배열 5조건 룰 점수

stock-lab/app/screener/nodes/ma_alignment.py

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+"""이평선 정배열 점수 — 5개 조건 충족 개수 / 5 × 100."""
+
+import pandas as pd
+
+from .base import ScoreNode
+
+
+class MaAlignment(ScoreNode):
+    name  = "ma_alignment"
+    label = "이평선 정배열"
+    default_params = {"ma_periods": [50, 150, 200]}
+    param_schema = {
+        "type": "object",
+        "properties": {
+            "ma_periods": {"type": "array", "items": {"type": "integer"}}
+        },
+    }
+
+    def compute(self, ctx, params: dict) -> pd.Series:
+        ma_periods = params.get("ma_periods", self.default_params["ma_periods"])
+        if len(ma_periods) != 3:
+            raise ValueError("ma_periods must have 3 entries (short, medium, long)")
+        ma_s, ma_m, ma_l = (int(x) for x in ma_periods)
+
+        prices = ctx.prices
+        if prices.empty:
+            return pd.Series(dtype=float)
+
+        ordered = prices.sort_values("date")
+        min_history = max(252, ma_l)
+
+        def _score(s: pd.Series) -> float:
+            closes = s.astype(float).reset_index(drop=True)
+            if len(closes) < min_history:
+                return float("nan")
+            close = closes.iloc[-1]
+            ma_short  = closes.rolling(ma_s).mean().iloc[-1]
+            ma_medium = closes.rolling(ma_m).mean().iloc[-1]
+            ma_long   = closes.rolling(ma_l).mean().iloc[-1]
+            low52 = closes.iloc[-252:].min()
+            conds = [
+                close > ma_short,
+                ma_short  > ma_medium,
+                ma_medium > ma_long,
+                close > ma_long,
+                close >= low52 * 1.25,
+            ]
+            return sum(conds) / 5 * 100.0
+
+        raw = ordered.groupby("ticker", group_keys=False)["close"].apply(_score)
+        return raw.fillna(0.0)

stock-lab/app/test_screener_nodes_ma_alignment.py

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+import datetime as dt
+import pandas as pd
+
+from app.screener.engine import ScreenContext
+from app.screener.nodes.ma_alignment import MaAlignment
+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_strong_uptrend_returns_100():
+    asof = dt.date(2026, 5, 12)
+    master = make_master(["UP"])
+    prices = make_prices(["UP"], days=260, asof=asof, start_close=50000, trend_pct=0.2)
+    flow = make_flow(["UP"], days=260, asof=asof)
+    out = MaAlignment().compute(_ctx(master, prices, flow), MaAlignment.default_params)
+    assert out["UP"] == 100.0
+
+
+def test_downtrend_returns_low():
+    asof = dt.date(2026, 5, 12)
+    master = make_master(["DN"])
+    prices = make_prices(["DN"], days=260, asof=asof, start_close=100000, trend_pct=-0.1)
+    flow = make_flow(["DN"], days=260, asof=asof)
+    out = MaAlignment().compute(_ctx(master, prices, flow), MaAlignment.default_params)
+    assert out["DN"] <= 20.0