feat: competitive intelligence & historical pattern matching layer

tests/test_rollups.py

@@ -171,3 +171,180 @@ def test_disagreement_with_conflict():
     assert details[0].dimension == "company_direction"
     assert "AAPL" in details[0].positive_doc_ids
     assert "MSFT" in details[0].negative_doc_ids
+
+
+# ---------------------------------------------------------------------------
+# Macro rollup integration (Requirements 6.1, 6.2, 6.3)
+# ---------------------------------------------------------------------------
+
+from services.aggregation.rollups import (
+    SectorMacroImpact,
+    compute_sector_macro_concentration,
+    SECTOR_CONCENTRATION_THRESHOLD,
+)
+
+
+def _make_sector_macro(
+    sector: str = "Technology",
+    total_impact: float = 1.0,
+    avg_impact: float = 0.5,
+    company_count: int = 2,
+    net_direction: float = -1.0,
+    event_ids: list[str] | None = None,
+) -> SectorMacroImpact:
+    return SectorMacroImpact(
+        sector=sector,
+        total_impact=total_impact,
+        avg_impact=avg_impact,
+        company_count=company_count,
+        net_direction=net_direction,
+        event_ids=event_ids or ["evt-1"],
+    )
+
+
+def test_rollup_no_macro_unchanged():
+    """Without macro data, rollup output is identical to original behavior."""
+    trends = [_make_trend("AAPL", direction="bullish", strength=0.7, confidence=0.9)]
+    without_macro = rollup_trends(trends, "sector", "Technology", "7d", NOW)
+    with_none = rollup_trends(trends, "sector", "Technology", "7d", NOW, macro_impacts=None)
+    with_empty = rollup_trends(trends, "sector", "Technology", "7d", NOW, macro_impacts={})
+    assert without_macro.trend_strength == with_none.trend_strength
+    assert without_macro.trend_strength == with_empty.trend_strength
+    assert without_macro.confidence == with_none.confidence
+    assert without_macro.confidence == with_empty.confidence
+
+
+def test_sector_rollup_with_macro_adjusts_strength():
+    """Sector rollup with macro data should adjust strength."""
+    trends = [
+        _make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8),
+        _make_trend("MSFT", sector="Technology", direction="bullish", strength=0.4, confidence=0.7),
+    ]
+    macro = {"Technology": _make_sector_macro("Technology", total_impact=2.0, avg_impact=0.6, company_count=2)}
+
+    without = rollup_trends(trends, "sector", "Technology", "7d", NOW)
+    with_macro = rollup_trends(trends, "sector", "Technology", "7d", NOW, macro_impacts=macro)
+
+    # Macro should increase strength
+    assert with_macro.trend_strength >= without.trend_strength
+
+
+def test_sector_rollup_macro_no_match_unchanged():
+    """Sector rollup with macro data for a different sector is unchanged."""
+    trends = [_make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8)]
+    macro = {"Financials": _make_sector_macro("Financials")}
+
+    without = rollup_trends(trends, "sector", "Technology", "7d", NOW)
+    with_macro = rollup_trends(trends, "sector", "Technology", "7d", NOW, macro_impacts=macro)
+
+    assert without.trend_strength == with_macro.trend_strength
+    assert without.confidence == with_macro.confidence
+
+
+def test_market_rollup_with_macro_adjusts():
+    """Market rollup with macro data should adjust strength and confidence."""
+    trends = [
+        _make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8),
+        _make_trend("JPM", sector="Financials", direction="bearish", strength=0.4, confidence=0.7),
+    ]
+    macro = {
+        "Technology": _make_sector_macro("Technology", total_impact=1.5, avg_impact=0.5, company_count=1),
+        "Financials": _make_sector_macro("Financials", total_impact=0.5, avg_impact=0.3, company_count=1),
+    }
+
+    without = rollup_trends(trends, "market", "all", "7d", NOW)
+    with_macro = rollup_trends(trends, "market", "all", "7d", NOW, macro_impacts=macro)
+
+    # With macro data, at least one of strength or confidence should differ
+    differs = (
+        with_macro.trend_strength != without.trend_strength
+        or with_macro.confidence != without.confidence
+    )
+    assert differs
+
+
+def test_market_rollup_disproportionate_sector_surfaced():
+    """When one sector has >60% of macro impact, it appears in risks or catalysts."""
+    trends = [
+        _make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8),
+        _make_trend("JPM", sector="Financials", direction="bullish", strength=0.4, confidence=0.7),
+    ]
+    # Technology has 90% of total macro impact
+    macro = {
+        "Technology": _make_sector_macro("Technology", total_impact=9.0, avg_impact=0.9, company_count=1, net_direction=-1.0),
+        "Financials": _make_sector_macro("Financials", total_impact=1.0, avg_impact=0.1, company_count=1, net_direction=0.5),
+    }
+
+    summary = rollup_trends(trends, "market", "all", "7d", NOW, macro_impacts=macro)
+
+    # Technology should appear in material_risks (negative direction) or dominant_catalysts
+    all_labels = summary.material_risks + summary.dominant_catalysts
+    tech_found = any("Technology" in label for label in all_labels)
+    assert tech_found, f"Expected Technology in risks/catalysts, got: {all_labels}"
+
+
+def test_market_rollup_no_disproportionate_sector():
+    """When no sector has >60% of macro impact, no macro labels are surfaced."""
+    trends = [
+        _make_trend("AAPL", sector="Technology", direction="bullish", strength=0.5, confidence=0.8),
+        _make_trend("JPM", sector="Financials", direction="bullish", strength=0.4, confidence=0.7),
+    ]
+    # Even split: 50/50
+    macro = {
+        "Technology": _make_sector_macro("Technology", total_impact=5.0, avg_impact=0.5, company_count=1),
+        "Financials": _make_sector_macro("Financials", total_impact=5.0, avg_impact=0.5, company_count=1),
+    }
+
+    summary = rollup_trends(trends, "market", "all", "7d", NOW, macro_impacts=macro)
+
+    all_labels = summary.material_risks + summary.dominant_catalysts
+    macro_labels = [l for l in all_labels if l.startswith("Macro:")]
+    assert len(macro_labels) == 0
+
+
+# ---------------------------------------------------------------------------
+# compute_sector_macro_concentration
+# ---------------------------------------------------------------------------
+
+
+def test_concentration_empty():
+    assert compute_sector_macro_concentration({}) == []
+
+
+def test_concentration_single_sector():
+    impacts = {"Technology": _make_sector_macro("Technology", total_impact=5.0)}
+    result = compute_sector_macro_concentration(impacts)
+    assert len(result) == 1
+    assert result[0] == ("Technology", 1.0)
+
+
+def test_concentration_multiple_sectors():
+    impacts = {
+        "Technology": _make_sector_macro("Technology", total_impact=7.0),
+        "Financials": _make_sector_macro("Financials", total_impact=3.0),
+    }
+    result = compute_sector_macro_concentration(impacts)
+    assert result[0][0] == "Technology"
+    assert abs(result[0][1] - 0.7) < 0.01
+    assert result[1][0] == "Financials"
+    assert abs(result[1][1] - 0.3) < 0.01
+
+
+def test_concentration_threshold_boundary():
+    """Exactly at 60% should not be considered disproportionate (>60% required)."""
+    impacts = {
+        "Technology": _make_sector_macro("Technology", total_impact=6.0),
+        "Financials": _make_sector_macro("Financials", total_impact=4.0),
+    }
+    result = compute_sector_macro_concentration(impacts)
+    # 60% is exactly at threshold, not above it
+    assert result[0][1] <= SECTOR_CONCENTRATION_THRESHOLD
+
+
+def test_concentration_above_threshold():
+    impacts = {
+        "Technology": _make_sector_macro("Technology", total_impact=7.0),
+        "Financials": _make_sector_macro("Financials", total_impact=3.0),
+    }
+    result = compute_sector_macro_concentration(impacts)
+    assert result[0][1] > SECTOR_CONCENTRATION_THRESHOLD