feat(stock-lab): VolumeSurge 노드 — log(최근/평균) 거래량 급증

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

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+"""거래량 급증 — log1p(recent/baseline)."""
+
+import numpy as np
+import pandas as pd
+
+from .base import ScoreNode, percentile_rank
+
+
+class VolumeSurge(ScoreNode):
+    name  = "volume_surge"
+    label = "거래량 급증"
+    default_params = {"baseline_days": 20, "eval_days": 3}
+    param_schema = {
+        "type": "object",
+        "properties": {
+            "baseline_days": {"type": "integer", "minimum": 5, "maximum": 60, "default": 20},
+            "eval_days":     {"type": "integer", "minimum": 1, "maximum": 10, "default": 3},
+        },
+    }
+
+    def compute(self, ctx, params: dict) -> pd.Series:
+        baseline = int(params.get("baseline_days", 20))
+        eval_d   = int(params.get("eval_days", 3))
+        prices = ctx.prices
+        if prices.empty:
+            return pd.Series(dtype=float)
+
+        ordered = prices.sort_values("date")
+        last_recent = ordered.groupby("ticker").tail(eval_d).groupby("ticker")["volume"].mean()
+        last_baseline = (
+            ordered.groupby("ticker")
+                   .tail(baseline + eval_d)
+                   .groupby("ticker")
+                   .head(baseline)
+                   .groupby("ticker")["volume"]
+                   .mean()
+        )
+        ratio = last_recent / last_baseline.replace(0, pd.NA)
+        raw = np.log1p(ratio.astype(float))
+        return percentile_rank(raw).fillna(50.0)