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Validate CLA covariance invertibility #745

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Validate CLA covariance invertibility #745

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17 changes: 13 additions & 4 deletions pypfopt/cla.py
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Original file line number Diff line number Diff line change
Expand Up @@ -327,6 +327,15 @@ def _eval_sr(self, a, w0, w1):
c = np.dot(np.dot(w.T, self.cov_matrix), w)[0, 0] ** 0.5
return b / c

@staticmethod
def _invert_covariance_matrix(cov_matrix):
try:
return np.linalg.inv(cov_matrix)
except np.linalg.LinAlgError as exc:
raise ValueError(
"CLA requires a positive definite covariance matrix"
) from exc

def _solve(self):
# Compute the turning points,free sets and weights
f, w = self._init_algo()
Expand All @@ -339,7 +348,7 @@ 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_covariance_matrix(covarF)
j = 0
for i in f:
lam, bi = self._compute_lambda(
Expand All @@ -354,7 +363,7 @@ 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_covariance_matrix(covarF)
lam, bi = self._compute_lambda(
covarF_inv,
covarFB,
Expand All @@ -371,7 +380,7 @@ 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_covariance_matrix(covarF)
meanF = np.zeros(meanF.shape)
else:
# 4) decide lambda
Expand All @@ -383,7 +392,7 @@ 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_covariance_matrix(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 @@ -52,6 +52,16 @@ def test_cla_max_sharpe_short():
assert sharpe > long_only_sharpe


def test_cla_max_sharpe_rejects_singular_covariance():
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="positive definite covariance matrix"):
cla.max_sharpe()


def test_cla_custom_bounds():
bounds = [(0.01, 0.13), (0.02, 0.11)] * 10
cla = setup_cla(weight_bounds=bounds)
Expand Down