def test_default_crp_solution(self):
"""
Tests the calculation of CRP weights with default settings.
"""
# Initialize CRP.
crp = CRP()
# Allocates asset prices to CRP.
crp.allocate(self.data, resample_by='M')
# Create np.array of all_weights.
all_weights = np.array(crp.all_weights)
# All weights for the strategy have to be the same.
one_weight = all_weights[0]
# Iterate through all_weights to check that weights equal to the first weight.
for i in range(all_weights.shape[0]):
weights = all_weights[i]
assert (weights >= 0).all()
assert len(weights) == self.data.shape[1]
assert (weights == one_weight).all()
np.testing.assert_almost_equal(np.sum(weights), 1)
def test_bcrp_returns(self):
"""
Tests that BCRP returns are higher than other CRP's.
"""
# Initialize BCRP
bcrp1 = BCRP()
# Allocates asset prices to BCRP.
bcrp1.allocate(self.data, resample_by='M')
# Get final returns for bcrp1.
bcrp1_returns = np.array(bcrp1.portfolio_return)[-1]
# Set an arbitray weight to test.
weight = bcrp1._uniform_weight()
# Initialize CRP.
crp = CRP(weight)
crp.allocate(self.data, resample_by='M')
# Get final returns for CRP.
crp_returns = np.array(crp.portfolio_return)[-1]
# Check that CRP returns are lower than BCRP returns.
np.testing.assert_array_less(crp_returns, bcrp1_returns)
def test_given_allocate_weights_crp_solution(self):
"""
Test calculation of constant rebalanced portfolio weights with weights given in allocate.
"""
# Create user input weights
weights = np.zeros(self.data.shape[1])
# Set 1 on the first stock and 0 on the rest.
weights[0] = 1
# Initialize CRP.
crp = CRP()
# Allocates asset prices to CRP.
crp.allocate(self.data, weights, resample_by='M')
# Create np.array of all_weights.
all_weights = np.array(crp.all_weights)
# All weights for the strategy have to be the same.
one_weight = all_weights[0]
# Iterate through all_weights to check that weights equal to the first weight.
for i in range(all_weights.shape[0]):
weights = all_weights[i]
assert (weights >= 0).all()
assert len(weights) == self.data.shape[1]
assert (weights == one_weight).all()
np.testing.assert_almost_equal(np.sum(weights), 1)