Celes Renata
c85c0068a2
- Replace all datetime.utcnow() with datetime.now(tz=timezone.utc) across 8 files - Fix 12 failing tests to match current implementation behavior - Fix pytest_plugins in non-top-level conftest (moved to root conftest.py) - Auto-fix 189 lint issues (import sorting, unused imports) - Add CI/CD pipeline infrastructure (ARC, ArgoCD, Kargo manifests) - Add values-beta.yaml and values-paper.yaml for staged deployments - Update GitHub Actions workflow to use self-hosted-gremlin runners - Add integration-test job to CI pipeline Result: 1596 passed, 0 failed, 0 warnings
351 lines
13 KiB
Python
351 lines
13 KiB
Python
"""Tests for sector and market rollup aggregation.
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Tests the pure rollup logic (no DB required).
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Requirements: 6.3, 6.4, 6.5
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"""
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from datetime import datetime, timezone
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from services.aggregation.rollups import (
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CompanyTrendRow,
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_build_rollup_disagreement,
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_derive_rollup_direction,
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rollup_trends,
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)
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from services.shared.schemas import TrendDirection, TrendWindow
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NOW = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
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def _make_trend(
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ticker: str = "AAPL",
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sector: str = "Technology",
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window: str = "7d",
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direction: str = "bullish",
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strength: float = 0.6,
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confidence: float = 0.8,
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contradiction: float = 0.1,
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catalysts: list[str] | None = None,
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risks: list[str] | None = None,
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supporting: list[str] | None = None,
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opposing: list[str] | None = None,
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) -> CompanyTrendRow:
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return CompanyTrendRow(
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entity_id=ticker,
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sector=sector,
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window=window,
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trend_direction=direction,
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trend_strength=strength,
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confidence=confidence,
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contradiction_score=contradiction,
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dominant_catalysts=catalysts or ["earnings"],
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material_risks=risks or [],
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top_supporting_evidence=supporting or ["doc-1"],
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top_opposing_evidence=opposing or [],
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)
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# ---------------------------------------------------------------------------
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# rollup_trends
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# ---------------------------------------------------------------------------
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def test_rollup_empty():
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summary = rollup_trends([], "sector", "Technology", "7d", NOW)
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assert summary.entity_type == "sector"
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assert summary.entity_id == "Technology"
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assert summary.trend_direction == TrendDirection.NEUTRAL
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assert summary.trend_strength == 0.0
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assert summary.confidence == 0.0
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def test_rollup_single_bullish():
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trends = [_make_trend("AAPL", direction="bullish", strength=0.7, confidence=0.9)]
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summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
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assert summary.trend_direction == TrendDirection.BULLISH
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assert summary.trend_strength > 0
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assert summary.confidence > 0
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assert summary.window == TrendWindow.SEVEN_DAY
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def test_rollup_mixed_directions():
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trends = [
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_make_trend("AAPL", direction="bullish", strength=0.6, confidence=0.8),
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_make_trend("MSFT", direction="bearish", strength=0.6, confidence=0.8),
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]
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summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
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# Equal and opposite → neutral or mixed
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assert summary.trend_direction in (TrendDirection.NEUTRAL, TrendDirection.MIXED)
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def test_rollup_confidence_weighted():
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"""Higher-confidence company should dominate the rollup direction."""
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trends = [
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_make_trend("AAPL", direction="bullish", strength=0.8, confidence=0.95),
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_make_trend("MSFT", direction="bearish", strength=0.3, confidence=0.2),
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]
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summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
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assert summary.trend_direction == TrendDirection.BULLISH
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def test_rollup_catalysts_aggregated():
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trends = [
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_make_trend("AAPL", catalysts=["earnings", "product"], confidence=0.8),
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_make_trend("MSFT", catalysts=["product", "macro"], confidence=0.6),
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]
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summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
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# "product" appears in both → should be top catalyst
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assert "product" in summary.dominant_catalysts
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def test_rollup_risks_deduplicated():
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trends = [
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_make_trend("AAPL", risks=["regulatory risk", "supply chain"], confidence=0.8),
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_make_trend("MSFT", risks=["Regulatory Risk", "tariffs"], confidence=0.6),
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]
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summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
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risk_lower = [r.lower() for r in summary.material_risks]
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assert risk_lower.count("regulatory risk") == 1
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def test_rollup_evidence_collected():
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trends = [
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_make_trend("AAPL", supporting=["doc-1", "doc-2"], opposing=["doc-3"]),
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_make_trend("MSFT", supporting=["doc-4"], opposing=["doc-5"]),
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]
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summary = rollup_trends(trends, "market", "all", "7d", NOW)
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assert "doc-1" in summary.top_supporting_evidence
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assert "doc-4" in summary.top_supporting_evidence
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assert "doc-3" in summary.top_opposing_evidence
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def test_rollup_market_entity_type():
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trends = [_make_trend("AAPL"), _make_trend("JPM", sector="Financials")]
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summary = rollup_trends(trends, "market", "all", "7d", NOW)
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assert summary.entity_type == "market"
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assert summary.entity_id == "all"
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# ---------------------------------------------------------------------------
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# _derive_rollup_direction
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# ---------------------------------------------------------------------------
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def test_derive_direction_bullish():
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assert _derive_rollup_direction(0.5, 0.0) == TrendDirection.BULLISH
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def test_derive_direction_bearish():
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assert _derive_rollup_direction(-0.5, 0.0) == TrendDirection.BEARISH
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def test_derive_direction_neutral():
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assert _derive_rollup_direction(0.05, 0.0) == TrendDirection.NEUTRAL
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def test_derive_direction_mixed_high_contradiction():
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assert _derive_rollup_direction(0.1, 0.2) == TrendDirection.MIXED
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# ---------------------------------------------------------------------------
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# _build_rollup_disagreement
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# ---------------------------------------------------------------------------
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def test_disagreement_no_conflict():
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trends = [
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_make_trend("AAPL", direction="bullish"),
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_make_trend("MSFT", direction="bullish"),
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]
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details = _build_rollup_disagreement(trends, "Technology")
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assert details == []
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def test_disagreement_with_conflict():
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trends = [
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_make_trend("AAPL", direction="bullish", confidence=0.8),
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_make_trend("MSFT", direction="bearish", confidence=0.7),
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]
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details = _build_rollup_disagreement(trends, "Technology")
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assert len(details) == 1
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assert details[0].dimension == "company_direction"
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assert "AAPL" in details[0].positive_doc_ids
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assert "MSFT" in details[0].negative_doc_ids
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# ---------------------------------------------------------------------------
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# Macro rollup integration (Requirements 6.1, 6.2, 6.3)
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# ---------------------------------------------------------------------------
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from services.aggregation.rollups import (
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SECTOR_CONCENTRATION_THRESHOLD,
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SectorMacroImpact,
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compute_sector_macro_concentration,
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)
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def _make_sector_macro(
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sector: str = "Technology",
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total_impact: float = 1.0,
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avg_impact: float = 0.5,
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company_count: int = 2,
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net_direction: float = -1.0,
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event_ids: list[str] | None = None,
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) -> SectorMacroImpact:
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return SectorMacroImpact(
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sector=sector,
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total_impact=total_impact,
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avg_impact=avg_impact,
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company_count=company_count,
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net_direction=net_direction,
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event_ids=event_ids or ["evt-1"],
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)
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def test_rollup_no_macro_unchanged():
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"""Without macro data, rollup output is identical to original behavior."""
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trends = [_make_trend("AAPL", direction="bullish", strength=0.7, confidence=0.9)]
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without_macro = rollup_trends(trends, "sector", "Technology", "7d", NOW)
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with_none = rollup_trends(trends, "sector", "Technology", "7d", NOW, macro_impacts=None)
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with_empty = rollup_trends(trends, "sector", "Technology", "7d", NOW, macro_impacts={})
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assert without_macro.trend_strength == with_none.trend_strength
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assert without_macro.trend_strength == with_empty.trend_strength
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assert without_macro.confidence == with_none.confidence
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assert without_macro.confidence == with_empty.confidence
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def test_sector_rollup_with_macro_adjusts_strength():
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"""Sector rollup with macro data should adjust strength."""
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trends = [
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_make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8),
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_make_trend("MSFT", sector="Technology", direction="bullish", strength=0.4, confidence=0.7),
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]
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macro = {"Technology": _make_sector_macro("Technology", total_impact=2.0, avg_impact=0.6, company_count=2)}
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without = rollup_trends(trends, "sector", "Technology", "7d", NOW)
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with_macro = rollup_trends(trends, "sector", "Technology", "7d", NOW, macro_impacts=macro)
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# Macro should increase strength
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assert with_macro.trend_strength >= without.trend_strength
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def test_sector_rollup_macro_no_match_unchanged():
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"""Sector rollup with macro data for a different sector is unchanged."""
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trends = [_make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8)]
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macro = {"Financials": _make_sector_macro("Financials")}
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without = rollup_trends(trends, "sector", "Technology", "7d", NOW)
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with_macro = rollup_trends(trends, "sector", "Technology", "7d", NOW, macro_impacts=macro)
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assert without.trend_strength == with_macro.trend_strength
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assert without.confidence == with_macro.confidence
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def test_market_rollup_with_macro_adjusts():
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"""Market rollup with macro data should adjust strength and confidence."""
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trends = [
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_make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8),
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_make_trend("JPM", sector="Financials", direction="bearish", strength=0.4, confidence=0.7),
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]
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macro = {
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"Technology": _make_sector_macro("Technology", total_impact=1.5, avg_impact=0.5, company_count=1),
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"Financials": _make_sector_macro("Financials", total_impact=0.5, avg_impact=0.3, company_count=1),
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}
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without = rollup_trends(trends, "market", "all", "7d", NOW)
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with_macro = rollup_trends(trends, "market", "all", "7d", NOW, macro_impacts=macro)
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# With macro data, at least one of strength or confidence should differ
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differs = (
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with_macro.trend_strength != without.trend_strength
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or with_macro.confidence != without.confidence
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)
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assert differs
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def test_market_rollup_disproportionate_sector_surfaced():
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"""When one sector has >60% of macro impact, it appears in risks or catalysts."""
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trends = [
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_make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8),
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_make_trend("JPM", sector="Financials", direction="bullish", strength=0.4, confidence=0.7),
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]
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# Technology has 90% of total macro impact
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macro = {
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"Technology": _make_sector_macro("Technology", total_impact=9.0, avg_impact=0.9, company_count=1, net_direction=-1.0),
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"Financials": _make_sector_macro("Financials", total_impact=1.0, avg_impact=0.1, company_count=1, net_direction=0.5),
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}
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summary = rollup_trends(trends, "market", "all", "7d", NOW, macro_impacts=macro)
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# Technology should appear in material_risks (negative direction) or dominant_catalysts
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all_labels = summary.material_risks + summary.dominant_catalysts
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tech_found = any("Technology" in label for label in all_labels)
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assert tech_found, f"Expected Technology in risks/catalysts, got: {all_labels}"
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def test_market_rollup_no_disproportionate_sector():
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"""When no sector has >60% of macro impact, no macro labels are surfaced."""
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trends = [
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_make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8),
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_make_trend("JPM", sector="Financials", direction="bullish", strength=0.4, confidence=0.7),
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]
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# Even split: 50/50
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macro = {
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"Technology": _make_sector_macro("Technology", total_impact=5.0, avg_impact=0.5, company_count=1),
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"Financials": _make_sector_macro("Financials", total_impact=5.0, avg_impact=0.5, company_count=1),
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}
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summary = rollup_trends(trends, "market", "all", "7d", NOW, macro_impacts=macro)
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all_labels = summary.material_risks + summary.dominant_catalysts
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macro_labels = [l for l in all_labels if l.startswith("Macro:")]
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assert len(macro_labels) == 0
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# ---------------------------------------------------------------------------
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# compute_sector_macro_concentration
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# ---------------------------------------------------------------------------
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def test_concentration_empty():
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assert compute_sector_macro_concentration({}) == []
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def test_concentration_single_sector():
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impacts = {"Technology": _make_sector_macro("Technology", total_impact=5.0)}
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result = compute_sector_macro_concentration(impacts)
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assert len(result) == 1
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assert result[0] == ("Technology", 1.0)
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def test_concentration_multiple_sectors():
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impacts = {
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"Technology": _make_sector_macro("Technology", total_impact=7.0),
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"Financials": _make_sector_macro("Financials", total_impact=3.0),
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}
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result = compute_sector_macro_concentration(impacts)
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assert result[0][0] == "Technology"
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assert abs(result[0][1] - 0.7) < 0.01
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assert result[1][0] == "Financials"
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assert abs(result[1][1] - 0.3) < 0.01
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def test_concentration_threshold_boundary():
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"""Exactly at 60% should not be considered disproportionate (>60% required)."""
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impacts = {
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"Technology": _make_sector_macro("Technology", total_impact=6.0),
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"Financials": _make_sector_macro("Financials", total_impact=4.0),
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}
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result = compute_sector_macro_concentration(impacts)
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# 60% is exactly at threshold, not above it
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assert result[0][1] <= SECTOR_CONCENTRATION_THRESHOLD
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def test_concentration_above_threshold():
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impacts = {
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"Technology": _make_sector_macro("Technology", total_impact=7.0),
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"Financials": _make_sector_macro("Financials", total_impact=3.0),
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}
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result = compute_sector_macro_concentration(impacts)
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assert result[0][1] > SECTOR_CONCENTRATION_THRESHOLD
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