346 lines
12 KiB
Python
346 lines
12 KiB
Python
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"""Tests for summarize helpers."""
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent / "scripts"))
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from datetime import datetime
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import summarize
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from summarize import (
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MoverContext,
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SectorCluster,
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WatchpointsData,
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build_watchpoints_data,
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classify_move_type,
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detect_sector_clusters,
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format_watchpoints,
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get_index_change,
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match_headline_to_symbol,
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)
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class FixedDateTime(datetime):
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@classmethod
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def now(cls, tz=None):
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return cls(2026, 1, 1, 15, 0)
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def test_generate_briefing_auto_time_evening(capsys, monkeypatch):
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def fake_market_news(*_args, **_kwargs):
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return {
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"headlines": [
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{"source": "CNBC", "title": "Headline one", "link": "https://example.com/1"},
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{"source": "Yahoo", "title": "Headline two", "link": "https://example.com/2"},
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{"source": "CNBC", "title": "Headline three", "link": "https://example.com/3"},
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],
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"markets": {
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"us": {
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"name": "US Markets",
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"indices": {
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"^GSPC": {"name": "S&P 500", "data": {"price": 100, "change_percent": 1.0}},
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},
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}
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},
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}
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def fake_summary(*_args, **_kwargs):
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return "OK"
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monkeypatch.setattr(summarize, "get_market_news", fake_market_news)
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monkeypatch.setattr(summarize, "get_portfolio_news", lambda *_a, **_k: None)
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monkeypatch.setattr(summarize, "summarize_with_claude", fake_summary)
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monkeypatch.setattr(summarize, "datetime", FixedDateTime)
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args = type(
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"Args",
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(),
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{
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"lang": "de",
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"style": "briefing",
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"time": None,
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"model": "claude",
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"json": False,
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"research": False,
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"deadline": None,
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"fast": False,
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"llm": False,
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"debug": False,
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},
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)()
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summarize.generate_briefing(args)
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stdout = capsys.readouterr().out
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assert "Börsen Abend-Briefing" in stdout
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# --- Tests for watchpoints feature (Issue #92) ---
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class TestGetIndexChange:
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def test_extracts_sp500_change(self):
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market_data = {
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"markets": {
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"us": {
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"indices": {
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"^GSPC": {"data": {"change_percent": -1.5}}
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}
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}
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}
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}
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assert get_index_change(market_data) == -1.5
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def test_returns_zero_on_missing_data(self):
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assert get_index_change({}) == 0.0
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assert get_index_change({"markets": {}}) == 0.0
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assert get_index_change({"markets": {"us": {}}}) == 0.0
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class TestMatchHeadlineToSymbol:
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def test_exact_symbol_match_dollar(self):
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headlines = [{"title": "Breaking: $NVDA surges on AI demand"}]
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result = match_headline_to_symbol("NVDA", "NVIDIA Corporation", headlines)
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assert result is not None
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assert "NVDA" in result["title"]
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def test_exact_symbol_match_parens(self):
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headlines = [{"title": "Tesla (TSLA) reports record deliveries"}]
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result = match_headline_to_symbol("TSLA", "Tesla Inc", headlines)
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assert result is not None
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def test_exact_symbol_match_word_boundary(self):
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headlines = [{"title": "AAPL announces new product line"}]
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result = match_headline_to_symbol("AAPL", "Apple Inc", headlines)
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assert result is not None
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def test_company_name_match(self):
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headlines = [{"title": "Apple announces record iPhone sales"}]
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result = match_headline_to_symbol("AAPL", "Apple Inc", headlines)
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assert result is not None
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def test_no_match_returns_none(self):
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headlines = [{"title": "Fed raises interest rates"}]
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result = match_headline_to_symbol("NVDA", "NVIDIA Corporation", headlines)
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assert result is None
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def test_avoids_partial_symbol_match(self):
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# "APP" should not match "application"
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headlines = [{"title": "New application launches today"}]
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result = match_headline_to_symbol("APP", "AppLovin Corp", headlines)
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assert result is None
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def test_empty_headlines(self):
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result = match_headline_to_symbol("NVDA", "NVIDIA", [])
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assert result is None
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class TestDetectSectorClusters:
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def test_detects_cluster_three_stocks_same_direction(self):
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movers = [
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{"symbol": "NVDA", "change_pct": -5.0},
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{"symbol": "AMD", "change_pct": -4.0},
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{"symbol": "INTC", "change_pct": -3.0},
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]
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portfolio_meta = {
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"NVDA": {"category": "Tech"},
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"AMD": {"category": "Tech"},
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"INTC": {"category": "Tech"},
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}
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clusters = detect_sector_clusters(movers, portfolio_meta)
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assert len(clusters) == 1
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assert clusters[0].category == "Tech"
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assert clusters[0].direction == "down"
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assert len(clusters[0].stocks) == 3
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def test_no_cluster_if_less_than_three(self):
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movers = [
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{"symbol": "NVDA", "change_pct": -5.0},
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{"symbol": "AMD", "change_pct": -4.0},
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]
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portfolio_meta = {
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"NVDA": {"category": "Tech"},
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"AMD": {"category": "Tech"},
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}
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clusters = detect_sector_clusters(movers, portfolio_meta)
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assert len(clusters) == 0
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def test_no_cluster_if_mixed_direction(self):
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movers = [
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{"symbol": "NVDA", "change_pct": 5.0},
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{"symbol": "AMD", "change_pct": -4.0},
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{"symbol": "INTC", "change_pct": 3.0},
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]
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portfolio_meta = {
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"NVDA": {"category": "Tech"},
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"AMD": {"category": "Tech"},
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"INTC": {"category": "Tech"},
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}
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clusters = detect_sector_clusters(movers, portfolio_meta)
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assert len(clusters) == 0
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class TestClassifyMoveType:
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def test_earnings_with_keyword(self):
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headline = {"title": "Company beats Q3 earnings expectations"}
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result = classify_move_type(headline, False, 5.0, 0.1)
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assert result == "earnings"
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def test_sector_cluster(self):
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result = classify_move_type(None, True, -3.0, -0.5)
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assert result == "sector"
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def test_market_wide(self):
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result = classify_move_type(None, False, -2.0, -2.0)
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assert result == "market_wide"
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def test_company_specific_with_headline(self):
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headline = {"title": "Company announces acquisition"}
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result = classify_move_type(headline, False, 3.0, 0.1)
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assert result == "company_specific"
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def test_company_specific_large_move_no_headline(self):
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result = classify_move_type(None, False, 8.0, 0.1)
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assert result == "company_specific"
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def test_unknown_small_move_no_context(self):
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result = classify_move_type(None, False, 1.5, 0.2)
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assert result == "unknown"
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class TestFormatWatchpoints:
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def test_formats_sector_cluster(self):
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cluster = SectorCluster(
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category="Tech",
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stocks=[
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MoverContext("NVDA", -5.0, 100.0, "Tech", None, "sector", None),
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MoverContext("AMD", -4.0, 80.0, "Tech", None, "sector", None),
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MoverContext("INTC", -3.0, 30.0, "Tech", None, "sector", None),
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],
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avg_change=-4.0,
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direction="down",
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vs_index=-3.5,
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)
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data = WatchpointsData(
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movers=[],
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sector_clusters=[cluster],
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index_change=-0.5,
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market_wide=False,
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)
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result = format_watchpoints(data, "en", {})
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assert "Tech" in result
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assert "-4.0%" in result
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assert "vs Index" in result
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def test_formats_individual_mover_with_headline(self):
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mover = MoverContext(
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symbol="NVDA",
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change_pct=5.0,
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price=100.0,
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category="Tech",
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matched_headline={"title": "NVIDIA reports record revenue"},
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move_type="company_specific",
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vs_index=4.5,
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)
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data = WatchpointsData(
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movers=[mover],
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sector_clusters=[],
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index_change=0.5,
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market_wide=False,
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)
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result = format_watchpoints(data, "en", {})
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assert "NVDA" in result
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assert "+5.0%" in result
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assert "record revenue" in result
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def test_formats_market_wide_move_english(self):
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data = WatchpointsData(
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movers=[],
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sector_clusters=[],
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index_change=-2.0,
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market_wide=True,
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)
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result = format_watchpoints(data, "en", {})
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assert "Market-wide move" in result
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assert "S&P 500 fell 2.0%" in result
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def test_formats_market_wide_move_german(self):
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data = WatchpointsData(
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movers=[],
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sector_clusters=[],
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index_change=2.5,
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market_wide=True,
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)
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result = format_watchpoints(data, "de", {})
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assert "Breite Marktbewegung" in result
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assert "stieg 2.5%" in result
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def test_uses_label_fallbacks(self):
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mover = MoverContext(
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symbol="XYZ",
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change_pct=1.5,
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price=50.0,
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category="Other",
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matched_headline=None,
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move_type="unknown",
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vs_index=1.0,
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)
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data = WatchpointsData(
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movers=[mover],
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sector_clusters=[],
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index_change=0.5,
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market_wide=False,
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)
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labels = {"no_catalyst": " -- no news"}
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result = format_watchpoints(data, "en", labels)
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assert "XYZ" in result
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assert "no news" in result
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class TestBuildWatchpointsData:
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def test_builds_complete_data_structure(self):
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movers = [
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{"symbol": "NVDA", "change_pct": -5.0, "price": 100.0},
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{"symbol": "AMD", "change_pct": -4.0, "price": 80.0},
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{"symbol": "INTC", "change_pct": -3.0, "price": 30.0},
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{"symbol": "AAPL", "change_pct": 2.0, "price": 150.0},
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]
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headlines = [
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{"title": "NVIDIA reports weak guidance"},
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{"title": "Apple announces new product"},
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]
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portfolio_meta = {
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"NVDA": {"category": "Tech", "name": "NVIDIA Corporation"},
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"AMD": {"category": "Tech", "name": "Advanced Micro Devices"},
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"INTC": {"category": "Tech", "name": "Intel Corporation"},
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"AAPL": {"category": "Tech", "name": "Apple Inc"},
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}
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index_change = -0.5
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result = build_watchpoints_data(movers, headlines, portfolio_meta, index_change)
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# Should detect Tech sector cluster (3 losers)
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assert len(result.sector_clusters) == 1
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assert result.sector_clusters[0].category == "Tech"
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assert result.sector_clusters[0].direction == "down"
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# All movers should be present
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assert len(result.movers) == 4
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# NVDA should have matched headline
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nvda_mover = next(m for m in result.movers if m.symbol == "NVDA")
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assert nvda_mover.matched_headline is not None
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assert "guidance" in nvda_mover.matched_headline["title"]
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# vs_index should be calculated
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assert nvda_mover.vs_index == -5.0 - (-0.5) # -4.5
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def test_handles_empty_movers(self):
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result = build_watchpoints_data([], [], {}, 0.0)
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assert result.movers == []
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assert result.sector_clusters == []
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assert result.market_wide is False
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def test_detects_market_wide_move(self):
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result = build_watchpoints_data([], [], {}, -2.0)
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assert result.market_wide is True
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