address update of exposure in exposure matrix

cvx/risk/bounds.py

@@ -25,7 +25,10 @@
 @dataclass
 class Bounds(Model):
     m: int = 0
+    """Maximal number of bounds"""
+
     name: str = ""
+    """Name for the bounds, e.g. assets or factors"""
 
     def estimate(self, weights, **kwargs):
         """No estimation for bounds"""

cvx/risk/factor/factor.py

@@ -29,11 +29,11 @@
 class FactorModel(Model):
     """Factor risk model"""
 
-    # number of assets
     assets: int = 0
+    """Maximal number of assets"""
 
-    # number of factors
     k: int = 0
+    """Maximal number of factors"""
 
     def __post_init__(self):
         self.parameter["exposure"] = cvx.Parameter(
@@ -70,8 +70,17 @@ def estimate(self, weights, **kwargs):
         )
 
     def update(self, **kwargs):
+        self.parameter["exposure"].value = np.zeros((self.k, self.assets))
+        self.parameter["chol"].value = np.zeros((self.k, self.k))
+        self.parameter["idiosyncratic_risk"].value = np.zeros(self.assets)
+
+        # get the exposure
         exposure = kwargs["exposure"]
+
+        # extract dimensions
         k, assets = exposure.shape
+        assert k <= self.k
+        assert assets <= self.assets
 
         self.parameter["exposure"].value[:k, :assets] = kwargs["exposure"]
         self.parameter["idiosyncratic_risk"].value[:assets] = kwargs[

cvx/risk/model.py

@@ -26,6 +26,7 @@ class Model(ABC):
     """Abstract risk model"""
 
     parameter: dict[str, cp.Parameter] = field(default_factory=dict)
+    """parameter for the riskmodel"""
 
     @abstractmethod
     def estimate(self, weights, **kwargs):

tests/test_risk/test_factor/test_factor.py

@@ -101,3 +101,32 @@ def test_estimate_risk():
     assert np.all([y.value <= 0.1 + 1e-6])
     # test all entries of y are larger than -0.1
     assert np.all([y.value >= -(0.1 + 1e-6)])
+
+
+def test_dynamic_exposure():
+    model = FactorModel(assets=3, k=2)
+    model.update(
+        exposure=np.array([[1.0, 2.0]]),
+        idiosyncratic_risk=np.array([1.0, 1.0]),
+        cov=np.array([[1.0]]),
+        lower_assets=np.array([0.0]),
+        upper_assets=np.array([1.0]),
+        lower_factors=np.array([0.0]),
+        upper_factors=np.array([1.0]),
+    )
+
+    model.update(
+        exposure=np.array([[1.0]]),
+        idiosyncratic_risk=np.array([1.0]),
+        cov=np.array([[1.0]]),
+        lower_assets=np.array([0.0]),
+        upper_assets=np.array([1.0]),
+        lower_factors=np.array([0.0]),
+        upper_factors=np.array([1.0]),
+    )
+
+    np.testing.assert_array_equal(
+        model.parameter["exposure"].value, np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
+    )
+
+    # assert False