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