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Fix CLA max_sharpe with equal expected returns #740

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Fix CLA max_sharpe with equal expected returns #740

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4 changes: 2 additions & 2 deletions pypfopt/cla.py
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Original file line number Diff line number Diff line change
Expand Up @@ -364,8 +364,8 @@ def _solve(self):
self.w[-1][i],
)
if (
self.ls[-1] is None or lam < self.ls[-1]
) and lam > CLA._infnone(l_out):
self.ls[-1] is None or CLA._infnone(lam) < self.ls[-1]
) and CLA._infnone(lam) > CLA._infnone(l_out):
l_out, i_out = lam, i
if (l_in is None or l_in < 0) and (l_out is None or l_out < 0):
# 3) compute minimum variance solution
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13 changes: 13 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 +52,19 @@ def test_cla_max_sharpe_short():
assert sharpe > long_only_sharpe


def test_cla_max_sharpe_equal_returns_matches_min_volatility():
mu = np.array([0.1, 0.1])
S = np.array([[0.01, 0.01], [0.01, 0.02]])

cla = CLA(mu, S)
w = cla.max_sharpe()

min_vol_cla = CLA(mu, S)
min_vol_w = min_vol_cla.min_volatility()
assert w == min_vol_w
np.testing.assert_allclose(cla.weights, min_vol_cla.weights)


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