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Raise a clearer error when CLA receives a singular covariance matrix #741

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Raise a clearer error when CLA receives a singular covariance matrix #741

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18 changes: 14 additions & 4 deletions pypfopt/cla.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,6 +192,16 @@ def _compute_lambda(self, covarF_inv, covarFB, meanF, wB, i, bi):
res = float(res[0, 0])
return res, bi

@staticmethod
def _invert(covarF):
try:
return np.linalg.inv(covarF)
except np.linalg.LinAlgError as e:
raise ValueError(
"CLA requires an invertible covariance matrix. "
"The supplied covariance matrix appears to be singular."
) from e

def _get_matrices(self, f):
# Slice covarF,covarFB,covarB,meanF,meanB,wF,wB
covarF = self._reduce_matrix(self.cov_matrix, f, f)
Expand Down Expand Up @@ -339,7 +349,7 @@ def _solve(self):
l_in = None
if len(f) > 1:
covarF, covarFB, meanF, wB = self._get_matrices(f)
covarF_inv = np.linalg.inv(covarF)
covarF_inv = self._invert(covarF)
j = 0
for i in f:
lam, bi = self._compute_lambda(
Expand All @@ -354,7 +364,7 @@ def _solve(self):
b = self._get_b(f)
for i in b:
covarF, covarFB, meanF, wB = self._get_matrices(f + [i])
covarF_inv = np.linalg.inv(covarF)
covarF_inv = self._invert(covarF)
lam, bi = self._compute_lambda(
covarF_inv,
covarFB,
Expand All @@ -371,7 +381,7 @@ def _solve(self):
# 3) compute minimum variance solution
self.ls.append(0)
covarF, covarFB, meanF, wB = self._get_matrices(f)
covarF_inv = np.linalg.inv(covarF)
covarF_inv = self._invert(covarF)
meanF = np.zeros(meanF.shape)
else:
# 4) decide lambda
Expand All @@ -383,7 +393,7 @@ def _solve(self):
self.ls.append(l_out)
f.append(i_out)
covarF, covarFB, meanF, wB = self._get_matrices(f)
covarF_inv = np.linalg.inv(covarF)
covarF_inv = self._invert(covarF)
# 5) compute solution vector
wF, g = self._compute_w(covarF_inv, covarFB, meanF, wB)
for i in range(len(f)):
Expand Down
10 changes: 10 additions & 0 deletions tests/test_cla.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,6 +96,16 @@ def test_cla_two_assets():
assert CLA(mu, cov)


def test_cla_singular_covariance_raises_clear_error():
mu = np.array([0.1, 0.2])
cov = np.array([[0.01, 0.01], [0.01, 0.01]])

cla = CLA(mu, cov)

with pytest.raises(ValueError, match="invertible covariance matrix"):
cla.max_sharpe()


def test_cla_max_sharpe_semicovariance():
df = get_data()
cla = setup_cla()
Expand Down