feat: competitive intelligence & historical pattern matching layer

tests/test_exposure.py

@@ -0,0 +1,174 @@
+"""Tests for exposure profile Pydantic models and endpoint logic."""
+import json
+import uuid
+from datetime import datetime, timezone
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+from pydantic import ValidationError
+
+from services.symbol_registry.exposure import (
+    ExposureProfileCreate,
+    ExposureProfileResponse,
+    VALID_MARKET_POSITION_TIERS,
+    VALID_SOURCES,
+    _row_to_profile,
+)
+
+
+# --- ExposureProfileCreate validation ---
+
+
+def test_create_defaults():
+    p = ExposureProfileCreate()
+    assert p.geographic_revenue_mix == {}
+    assert p.supply_chain_regions == []
+    assert p.key_input_commodities == []
+    assert p.regulatory_jurisdictions == []
+    assert p.market_position_tier == "regional"
+    assert p.export_dependency_pct == 0.0
+    assert p.source == "manual"
+    assert p.confidence == 1.0
+
+
+def test_create_with_full_data():
+    p = ExposureProfileCreate(
+        geographic_revenue_mix={"US": 0.6, "EU": 0.3, "CN": 0.1},
+        supply_chain_regions=["CN", "TW", "KR"],
+        key_input_commodities=["lithium", "cobalt"],
+        regulatory_jurisdictions=["US", "EU"],
+        market_position_tier="global_leader",
+        export_dependency_pct=0.45,
+        source="manual",
+        confidence=0.95,
+    )
+    assert p.geographic_revenue_mix["US"] == 0.6
+    assert len(p.supply_chain_regions) == 3
+    assert p.market_position_tier == "global_leader"
+
+
+def test_create_all_valid_tiers():
+    for tier in VALID_MARKET_POSITION_TIERS:
+        p = ExposureProfileCreate(market_position_tier=tier)
+        assert p.market_position_tier == tier
+
+
+def test_create_rejects_invalid_tier():
+    with pytest.raises(ValidationError):
+        ExposureProfileCreate(market_position_tier="mega_corp")
+
+
+def test_create_all_valid_sources():
+    for src in VALID_SOURCES:
+        p = ExposureProfileCreate(source=src)
+        assert p.source == src
+
+
+def test_create_rejects_invalid_source():
+    with pytest.raises(ValidationError):
+        ExposureProfileCreate(source="guessed")
+
+
+def test_create_rejects_export_dependency_above_1():
+    with pytest.raises(ValidationError):
+        ExposureProfileCreate(export_dependency_pct=1.5)
+
+
+def test_create_rejects_export_dependency_below_0():
+    with pytest.raises(ValidationError):
+        ExposureProfileCreate(export_dependency_pct=-0.1)
+
+
+def test_create_rejects_confidence_above_1():
+    with pytest.raises(ValidationError):
+        ExposureProfileCreate(confidence=1.1)
+
+
+def test_create_rejects_confidence_below_0():
+    with pytest.raises(ValidationError):
+        ExposureProfileCreate(confidence=-0.5)
+
+
+# --- _row_to_profile helper ---
+
+
+def test_row_to_profile_converts_uuids():
+    """UUID fields should be converted to strings."""
+    uid = uuid.uuid4()
+    now = datetime.now(timezone.utc)
+
+    class FakeRecord(dict):
+        pass
+
+    row = FakeRecord(
+        id=uid,
+        company_id=uid,
+        geographic_revenue_mix={"US": 0.5},
+        supply_chain_regions=["US"],
+        key_input_commodities=[],
+        regulatory_jurisdictions=[],
+        market_position_tier="regional",
+        export_dependency_pct=0.0,
+        source="manual",
+        confidence=1.0,
+        version=1,
+        active=True,
+        created_at=now,
+        updated_at=now,
+    )
+    result = _row_to_profile(row)
+    assert result["id"] == str(uid)
+    assert result["company_id"] == str(uid)
+
+
+def test_row_to_profile_parses_json_string():
+    """geographic_revenue_mix stored as JSON string should be parsed."""
+    uid = uuid.uuid4()
+    now = datetime.now(timezone.utc)
+
+    class FakeRecord(dict):
+        pass
+
+    row = FakeRecord(
+        id=uid,
+        company_id=uid,
+        geographic_revenue_mix='{"US": 0.7, "EU": 0.3}',
+        supply_chain_regions=["US"],
+        key_input_commodities=[],
+        regulatory_jurisdictions=[],
+        market_position_tier="regional",
+        export_dependency_pct=0.0,
+        source="manual",
+        confidence=1.0,
+        version=1,
+        active=True,
+        created_at=now,
+        updated_at=now,
+    )
+    result = _row_to_profile(row)
+    assert result["geographic_revenue_mix"] == {"US": 0.7, "EU": 0.3}
+
+
+# --- ExposureProfileResponse model ---
+
+
+def test_response_model_accepts_valid_data():
+    now = datetime.now(timezone.utc)
+    resp = ExposureProfileResponse(
+        id=str(uuid.uuid4()),
+        company_id=str(uuid.uuid4()),
+        geographic_revenue_mix={"US": 0.5, "EU": 0.5},
+        supply_chain_regions=["CN"],
+        key_input_commodities=["oil"],
+        regulatory_jurisdictions=["US"],
+        market_position_tier="multinational",
+        export_dependency_pct=0.3,
+        source="inferred",
+        confidence=0.8,
+        version=3,
+        active=True,
+        created_at=now,
+        updated_at=now,
+    )
+    assert resp.version == 3
+    assert resp.source == "inferred"