feat(screener): AiNewsSentiment ScoreNode (percentile_rank + min_news_count)

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

@@ -0,0 +1,36 @@
+"""AI 뉴스 호재/악재 점수 노드.
+
+ScreenContext.news_sentiment (DataFrame: ticker, score_raw, news_count) 를
+min_news_count 로 필터한 뒤 percentile_rank 로 0~100 변환.
+"""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from .base import ScoreNode, percentile_rank
+
+
+class AiNewsSentiment(ScoreNode):
+    name  = "ai_news"
+    label = "AI 뉴스 호재/악재"
+    default_params = {"min_news_count": 1}
+    param_schema = {
+        "type": "object",
+        "properties": {
+            "min_news_count": {
+                "type": "integer", "minimum": 0, "default": 1,
+                "description": "최소 분석 뉴스 수. 미만이면 점수 미산출.",
+            },
+        },
+    }
+
+    def compute(self, ctx, params: dict) -> pd.Series:
+        df = getattr(ctx, "news_sentiment", None)
+        if df is None or df.empty:
+            return pd.Series(dtype=float)
+        min_news = int(params.get("min_news_count", 1))
+        df = df[df["news_count"] >= min_news]
+        if df.empty:
+            return pd.Series(dtype=float)
+        return percentile_rank(df.set_index("ticker")["score_raw"])