phase 14-15: docker build validation and helm deployment

2026-04-11 11:59:45 -07:00
parent 7394d241c9
commit ce10afa034
179 changed files with 32559 additions and 576 deletions
@@ -0,0 +1,283 @@
+"""Tests for recommendation worker — generating recommendations from trend data.
+
+Tests the pure logic functions (no DB required). Async DB functions
+are covered by integration tests.
+"""
+from datetime import datetime, timezone
+
+from services.recommendation.eligibility import evaluate_eligibility
+from services.recommendation.worker import (
+    _extract_risk_classification,
+    build_recommendation,
+    build_thesis,
+    classify_risk,
+)
+from services.shared.schemas import (
+    ActionType,
+    RecommendationMode,
+    TrendDirection,
+    TrendSummary,
+    TrendWindow,
+)
+
+NOW = datetime(2026, 4, 11, 12, 0, 0, tzinfo=timezone.utc)
+
+
+def _make_summary(
+    ticker: str = "AAPL",
+    direction: TrendDirection = TrendDirection.BULLISH,
+    strength: float = 0.5,
+    confidence: float = 0.65,
+    contradiction: float = 0.1,
+    supporting: list[str] | None = None,
+    opposing: list[str] | None = None,
+    catalysts: list[str] | None = None,
+    risks: list[str] | None = None,
+    window: TrendWindow = TrendWindow.SEVEN_DAY,
+) -> TrendSummary:
+    return TrendSummary(
+        entity_type="company",
+        entity_id=ticker,
+        window=window,
+        trend_direction=direction,
+        trend_strength=strength,
+        confidence=confidence,
+        top_supporting_evidence=supporting or ["doc1", "doc2", "doc3"],
+        top_opposing_evidence=opposing or [],
+        dominant_catalysts=catalysts or ["earnings"],
+        material_risks=risks or ["regulatory scrutiny"],
+        contradiction_score=contradiction,
+        generated_at=NOW,
+    )
+
+
+# ---------------------------------------------------------------------------
+# build_thesis
+# ---------------------------------------------------------------------------
+
+
+def test_thesis_contains_ticker_and_direction():
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    thesis = build_thesis(summary, result)
+    assert "AAPL" in thesis
+    assert "bullish" in thesis
+
+
+def test_thesis_includes_catalysts():
+    summary = _make_summary(catalysts=["product", "m_and_a"])
+    result = evaluate_eligibility(summary)
+    thesis = build_thesis(summary, result)
+    assert "product" in thesis
+
+
+def test_thesis_includes_contradiction_note():
+    summary = _make_summary(contradiction=0.3)
+    result = evaluate_eligibility(summary)
+    thesis = build_thesis(summary, result)
+    assert "disagreement" in thesis
+
+
+def test_thesis_includes_risks():
+    summary = _make_summary(risks=["supply chain disruption"])
+    result = evaluate_eligibility(summary)
+    thesis = build_thesis(summary, result)
+    assert "supply chain disruption" in thesis
+
+
+def test_thesis_includes_evidence_counts():
+    summary = _make_summary(
+        supporting=["d1", "d2"],
+        opposing=["d3"],
+    )
+    result = evaluate_eligibility(summary)
+    thesis = build_thesis(summary, result)
+    assert "2 supporting" in thesis
+    assert "1 opposing" in thesis
+
+
+def test_thesis_includes_action():
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    thesis = build_thesis(summary, result)
+    assert "BUY" in thesis
+
+
+# ---------------------------------------------------------------------------
+# classify_risk
+# ---------------------------------------------------------------------------
+
+
+def test_risk_low_for_strong_signal():
+    summary = _make_summary(
+        confidence=0.8,
+        contradiction=0.05,
+        supporting=["d1", "d2", "d3", "d4", "d5"],
+    )
+    result = evaluate_eligibility(summary)
+    assert classify_risk(summary, result) == "low"
+
+
+def test_risk_high_for_weak_signal():
+    summary = _make_summary(
+        confidence=0.36,
+        contradiction=0.55,
+        supporting=["d1"],
+        opposing=[],
+    )
+    result = evaluate_eligibility(summary)
+    risk = classify_risk(summary, result)
+    assert risk in ("high", "very_high")
+
+
+def test_risk_moderate_for_mixed():
+    summary = _make_summary(
+        confidence=0.5,
+        contradiction=0.2,
+        supporting=["d1", "d2"],
+        opposing=["d3"],
+    )
+    result = evaluate_eligibility(summary)
+    assert classify_risk(summary, result) == "moderate"
+
+
+# ---------------------------------------------------------------------------
+# build_recommendation
+# ---------------------------------------------------------------------------
+
+
+def test_build_recommendation_basic():
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result, reference_time=NOW)
+
+    assert rec.ticker == "AAPL"
+    assert rec.action == ActionType.BUY
+    assert rec.confidence == summary.confidence
+    assert rec.time_horizon == "swing_1d_10d"
+    assert rec.generated_at == NOW
+    assert len(rec.evidence_refs) == 3  # 3 supporting + 0 opposing
+    assert rec.model_metadata.provider == "deterministic"
+
+
+def test_build_recommendation_includes_risk_in_thesis():
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result)
+    assert rec.thesis.startswith("[risk:")
+
+
+def test_build_recommendation_with_llm_thesis():
+    """When llm_thesis is provided, it replaces the deterministic body."""
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    llm_text = "Apple exhibits a bullish posture driven by strong earnings."
+    rec = build_recommendation(summary, result, llm_thesis=llm_text)
+    assert llm_text in rec.thesis
+    assert rec.thesis.startswith("[risk:")
+    assert rec.model_metadata.provider == "ollama"
+    assert rec.model_metadata.model_name == "thesis-rewrite"
+
+
+def test_build_recommendation_without_llm_thesis_uses_deterministic():
+    """When llm_thesis is None, the deterministic thesis is used."""
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result)
+    assert rec.model_metadata.provider == "deterministic"
+    assert rec.model_metadata.model_name == "eligibility-v1"
+
+
+def test_build_recommendation_combines_evidence():
+    summary = _make_summary(
+        supporting=["s1", "s2"],
+        opposing=["o1"],
+    )
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result)
+    assert rec.evidence_refs == ["s1", "s2", "o1"]
+
+
+def test_build_recommendation_position_sizing():
+    summary = _make_summary(confidence=0.7)
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result)
+    assert rec.position_sizing.portfolio_pct == result.position_sizing.portfolio_pct
+    assert rec.position_sizing.max_loss_pct == result.position_sizing.max_loss_pct
+
+
+def test_build_recommendation_invalidation_conditions():
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result)
+    assert len(rec.invalidation_conditions) > 0
+
+
+def test_build_recommendation_ineligible_is_informational():
+    """When eligibility fails, mode should be informational (Req 7.4)."""
+    summary = _make_summary(confidence=0.2)
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result)
+    assert rec.mode == RecommendationMode.INFORMATIONAL
+
+
+def test_build_recommendation_sell_action():
+    summary = _make_summary(direction=TrendDirection.BEARISH, strength=0.5)
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result)
+    assert rec.action == ActionType.SELL
+    assert "SELL" in rec.thesis
+
+
+# ---------------------------------------------------------------------------
+# _extract_risk_classification
+# ---------------------------------------------------------------------------
+
+
+def test_extract_risk_classification_from_thesis():
+    assert _extract_risk_classification("[risk:low] Some thesis text") == "low"
+    assert _extract_risk_classification("[risk:very_high] Bad signal") == "very_high"
+
+
+def test_extract_risk_classification_missing_prefix():
+    assert _extract_risk_classification("No risk prefix here") == "moderate"
+
+
+def test_extract_risk_classification_empty():
+    assert _extract_risk_classification("") == "moderate"
+
+
+# ---------------------------------------------------------------------------
+# build_recommendation stores full model metadata
+# ---------------------------------------------------------------------------
+
+
+def test_build_recommendation_model_metadata_deterministic():
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result, reference_time=NOW)
+    assert rec.model_metadata.provider == "deterministic"
+    assert rec.model_metadata.model_name == "eligibility-v1"
+    assert rec.model_metadata.schema_version == "1.0.0"
+
+
+def test_build_recommendation_model_metadata_llm():
+    summary = _make_summary()
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(
+        summary, result, reference_time=NOW,
+        llm_thesis="Rewritten thesis text.",
+    )
+    assert rec.model_metadata.provider == "ollama"
+    assert rec.model_metadata.model_name == "thesis-rewrite"
+    assert rec.model_metadata.prompt_version != ""
+
+
+def test_build_recommendation_risk_classification_in_thesis():
+    """The risk classification should be embedded in the thesis prefix."""
+    summary = _make_summary(confidence=0.8, contradiction=0.05,
+                            supporting=["d1", "d2", "d3", "d4", "d5"])
+    result = evaluate_eligibility(summary)
+    rec = build_recommendation(summary, result, reference_time=NOW)
+    risk = _extract_risk_classification(rec.thesis)
+    assert risk == classify_risk(summary, result)