test: define labeled component risk contract

tests/test_risk.py

@@ -3,9 +3,16 @@
 from __future__ import annotations
 
 import numpy as np
+import pandas as pd
 import pytest
 
-from quant_engine.risk import component_var, marginal_risk_contribution, risk_contribution
+from quant_engine.risk import (
+    ComponentRiskResult,
+    component_var,
+    labeled_component_risk,
+    marginal_risk_contribution,
+    risk_contribution,
+)
 
 
 def test_risk_contribution_sums_to_one_for_positive_portfolio_variance() -> None:
@@ -52,3 +59,61 @@ def test_risk_functions_reject_covariance_shape_mismatch(function) -> None:
 def test_risk_functions_reject_empty_portfolio(function) -> None:
     with pytest.raises(ValueError, match="at least one asset"):
         function(np.array([]), np.empty((0, 0)))
+
+
+def test_labeled_component_risk_aligns_covariance_and_closes_to_volatility() -> None:
+    weights = pd.Series({"A": 0.25, "B": 0.75}, name="weight")
+    covariance = pd.DataFrame(
+        [[0.09, 0.01], [0.01, 0.04]],
+        index=["B", "A"],
+        columns=["B", "A"],
+    )
+
+    result = labeled_component_risk(weights, covariance)
+
+    aligned = covariance.reindex(index=weights.index, columns=weights.index)
+    expected_volatility = float(np.sqrt(weights @ aligned @ weights))
+    assert isinstance(result, ComponentRiskResult)
+    assert result.component.index.tolist() == ["A", "B"]
+    assert result.portfolio_volatility == pytest.approx(expected_volatility)
+    assert result.component.sum() == pytest.approx(expected_volatility)
+    assert result.percentage.sum() == pytest.approx(1.0)
+
+
+def test_component_risk_groups_actual_asset_contributions_by_label() -> None:
+    weights = pd.Series({"A": 0.2, "B": 0.3, "C": 0.5})
+    covariance = pd.DataFrame(np.diag([0.04, 0.09, 0.16]), index=weights.index, columns=weights.index)
+    groups = pd.Series({"C": "growth", "A": "value", "B": "value"})
+
+    result = labeled_component_risk(weights, covariance)
+    grouped = result.grouped_component(groups)
+
+    assert grouped.index.tolist() == ["growth", "value"]
+    assert grouped.loc["value"] == pytest.approx(
+        result.component.loc["A"] + result.component.loc["B"]
+    )
+    assert grouped.sum() == pytest.approx(result.portfolio_volatility)
+
+
+def test_labeled_component_risk_rejects_asset_label_mismatch() -> None:
+    weights = pd.Series({"A": 0.5, "B": 0.5})
+    covariance = pd.DataFrame(np.eye(2), index=["A", "C"], columns=["A", "C"])
+
+    with pytest.raises(ValueError, match="same asset labels"):
+        labeled_component_risk(weights, covariance)
+
+
+def test_labeled_component_risk_rejects_invalid_covariance() -> None:
+    weights = pd.Series({"A": 0.5, "B": 0.5})
+    asymmetric = pd.DataFrame([[1.0, 0.2], [0.1, 1.0]], index=weights.index, columns=weights.index)
+
+    with pytest.raises(ValueError, match="symmetric"):
+        labeled_component_risk(weights, asymmetric)
+
+
+def test_labeled_component_risk_rejects_zero_variance_portfolio() -> None:
+    weights = pd.Series({"A": 0.5, "B": 0.5})
+    covariance = pd.DataFrame(np.zeros((2, 2)), index=weights.index, columns=weights.index)
+
+    with pytest.raises(ValueError, match="positive portfolio variance"):
+        labeled_component_risk(weights, covariance)