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"])

stock-lab/tests/test_ai_news_node.py

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+import datetime as dt
+import pandas as pd
+import pytest
+from app.screener.nodes.ai_news import AiNewsSentiment
+
+
+class FakeCtx:
+    def __init__(self, df=None):
+        self.news_sentiment = df
+        self.asof = dt.date(2026, 5, 13)
+
+
+def test_compute_empty_context():
+    out = AiNewsSentiment().compute(FakeCtx(None), {"min_news_count": 1})
+    assert out.empty
+
+
+def test_compute_with_data_percentile_ranks():
+    df = pd.DataFrame([
+        {"ticker": "A", "score_raw": -5.0, "news_count": 3},
+        {"ticker": "B", "score_raw":  0.0, "news_count": 3},
+        {"ticker": "C", "score_raw":  8.0, "news_count": 3},
+    ])
+    out = AiNewsSentiment().compute(FakeCtx(df), {"min_news_count": 1})
+    assert len(out) == 3
+    # percentile rank: A (lowest) < B < C (highest)
+    assert out.loc["A"] < out.loc["B"] < out.loc["C"]
+    # all within [0, 100]
+    assert (out >= 0).all() and (out <= 100).all()
+
+
+def test_compute_filters_by_min_news_count():
+    df = pd.DataFrame([
+        {"ticker": "A", "score_raw": -5.0, "news_count": 0},  # 필터됨
+        {"ticker": "B", "score_raw":  0.0, "news_count": 2},
+        {"ticker": "C", "score_raw":  8.0, "news_count": 5},
+    ])
+    out = AiNewsSentiment().compute(FakeCtx(df), {"min_news_count": 1})
+    assert "A" not in out.index
+    assert "B" in out.index
+    assert "C" in out.index
+
+
+def test_compute_all_filtered_returns_empty():
+    df = pd.DataFrame([
+        {"ticker": "A", "score_raw": 5.0, "news_count": 0},
+    ])
+    out = AiNewsSentiment().compute(FakeCtx(df), {"min_news_count": 1})
+    assert out.empty
+
+
+def test_metadata():
+    n = AiNewsSentiment()
+    assert n.name == "ai_news"
+    assert "AI" in n.label or "뉴스" in n.label
+    assert n.default_params == {"min_news_count": 1}
+    assert "min_news_count" in n.param_schema["properties"]