phase 14-15: docker build validation and helm deployment

tests/test_rollups.py

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+"""Tests for sector and market rollup aggregation.
+
+Tests the pure rollup logic (no DB required).
+
+Requirements: 6.3, 6.4, 6.5
+"""
+from datetime import datetime, timezone
+
+from services.aggregation.rollups import (
+    CompanyTrendRow,
+    rollup_trends,
+    _build_rollup_disagreement,
+    _derive_rollup_direction,
+)
+from services.shared.schemas import TrendDirection, TrendWindow
+
+NOW = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
+
+
+def _make_trend(
+    ticker: str = "AAPL",
+    sector: str = "Technology",
+    window: str = "7d",
+    direction: str = "bullish",
+    strength: float = 0.6,
+    confidence: float = 0.8,
+    contradiction: float = 0.1,
+    catalysts: list[str] | None = None,
+    risks: list[str] | None = None,
+    supporting: list[str] | None = None,
+    opposing: list[str] | None = None,
+) -> CompanyTrendRow:
+    return CompanyTrendRow(
+        entity_id=ticker,
+        sector=sector,
+        window=window,
+        trend_direction=direction,
+        trend_strength=strength,
+        confidence=confidence,
+        contradiction_score=contradiction,
+        dominant_catalysts=catalysts or ["earnings"],
+        material_risks=risks or [],
+        top_supporting_evidence=supporting or ["doc-1"],
+        top_opposing_evidence=opposing or [],
+    )
+
+
+# ---------------------------------------------------------------------------
+# rollup_trends
+# ---------------------------------------------------------------------------
+
+
+def test_rollup_empty():
+    summary = rollup_trends([], "sector", "Technology", "7d", NOW)
+    assert summary.entity_type == "sector"
+    assert summary.entity_id == "Technology"
+    assert summary.trend_direction == TrendDirection.NEUTRAL
+    assert summary.trend_strength == 0.0
+    assert summary.confidence == 0.0
+
+
+def test_rollup_single_bullish():
+    trends = [_make_trend("AAPL", direction="bullish", strength=0.7, confidence=0.9)]
+    summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
+    assert summary.trend_direction == TrendDirection.BULLISH
+    assert summary.trend_strength > 0
+    assert summary.confidence > 0
+    assert summary.window == TrendWindow.SEVEN_DAY
+
+
+def test_rollup_mixed_directions():
+    trends = [
+        _make_trend("AAPL", direction="bullish", strength=0.6, confidence=0.8),
+        _make_trend("MSFT", direction="bearish", strength=0.6, confidence=0.8),
+    ]
+    summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
+    # Equal and opposite → neutral or mixed
+    assert summary.trend_direction in (TrendDirection.NEUTRAL, TrendDirection.MIXED)
+
+
+def test_rollup_confidence_weighted():
+    """Higher-confidence company should dominate the rollup direction."""
+    trends = [
+        _make_trend("AAPL", direction="bullish", strength=0.8, confidence=0.95),
+        _make_trend("MSFT", direction="bearish", strength=0.3, confidence=0.2),
+    ]
+    summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
+    assert summary.trend_direction == TrendDirection.BULLISH
+
+
+def test_rollup_catalysts_aggregated():
+    trends = [
+        _make_trend("AAPL", catalysts=["earnings", "product"], confidence=0.8),
+        _make_trend("MSFT", catalysts=["product", "macro"], confidence=0.6),
+    ]
+    summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
+    # "product" appears in both → should be top catalyst
+    assert "product" in summary.dominant_catalysts
+
+
+def test_rollup_risks_deduplicated():
+    trends = [
+        _make_trend("AAPL", risks=["regulatory risk", "supply chain"], confidence=0.8),
+        _make_trend("MSFT", risks=["Regulatory Risk", "tariffs"], confidence=0.6),
+    ]
+    summary = rollup_trends(trends, "sector", "Technology", "7d", NOW)
+    risk_lower = [r.lower() for r in summary.material_risks]
+    assert risk_lower.count("regulatory risk") == 1
+
+
+def test_rollup_evidence_collected():
+    trends = [
+        _make_trend("AAPL", supporting=["doc-1", "doc-2"], opposing=["doc-3"]),
+        _make_trend("MSFT", supporting=["doc-4"], opposing=["doc-5"]),
+    ]
+    summary = rollup_trends(trends, "market", "all", "7d", NOW)
+    assert "doc-1" in summary.top_supporting_evidence
+    assert "doc-4" in summary.top_supporting_evidence
+    assert "doc-3" in summary.top_opposing_evidence
+
+
+def test_rollup_market_entity_type():
+    trends = [_make_trend("AAPL"), _make_trend("JPM", sector="Financials")]
+    summary = rollup_trends(trends, "market", "all", "7d", NOW)
+    assert summary.entity_type == "market"
+    assert summary.entity_id == "all"
+
+
+# ---------------------------------------------------------------------------
+# _derive_rollup_direction
+# ---------------------------------------------------------------------------
+
+
+def test_derive_direction_bullish():
+    assert _derive_rollup_direction(0.5, 0.0) == TrendDirection.BULLISH
+
+
+def test_derive_direction_bearish():
+    assert _derive_rollup_direction(-0.5, 0.0) == TrendDirection.BEARISH
+
+
+def test_derive_direction_neutral():
+    assert _derive_rollup_direction(0.05, 0.0) == TrendDirection.NEUTRAL
+
+
+def test_derive_direction_mixed_high_contradiction():
+    assert _derive_rollup_direction(0.1, 0.2) == TrendDirection.MIXED
+
+
+# ---------------------------------------------------------------------------
+# _build_rollup_disagreement
+# ---------------------------------------------------------------------------
+
+
+def test_disagreement_no_conflict():
+    trends = [
+        _make_trend("AAPL", direction="bullish"),
+        _make_trend("MSFT", direction="bullish"),
+    ]
+    details = _build_rollup_disagreement(trends, "Technology")
+    assert details == []
+
+
+def test_disagreement_with_conflict():
+    trends = [
+        _make_trend("AAPL", direction="bullish", confidence=0.8),
+        _make_trend("MSFT", direction="bearish", confidence=0.7),
+    ]
+    details = _build_rollup_disagreement(trends, "Technology")
+    assert len(details) == 1
+    assert details[0].dimension == "company_direction"
+    assert "AAPL" in details[0].positive_doc_ids
+    assert "MSFT" in details[0].negative_doc_ids