def test_hrp_with_input_as_covariance_matrix(self):
"""
Test HRP when passing a covariance matrix as input.
"""
hrp = HierarchicalRiskParity()
returns = ReturnsEstimation().calculate_returns(asset_prices=self.data)
hrp.allocate(asset_names=self.data.columns,
covariance_matrix=returns.cov())
weights = hrp.weights.values[0]
assert (weights >= 0).all()
assert len(weights) == self.data.shape[1]
np.testing.assert_almost_equal(np.sum(weights), 1)
def test_hcaa_with_input_as_covariance_matrix(self):
"""
Test HCAA when passing a covariance matrix as input
"""
hcaa = HierarchicalClusteringAssetAllocation()
returns = ReturnsEstimation().calculate_returns(asset_prices=self.data)
hcaa.allocate(asset_names=self.data.columns,
covariance_matrix=returns.cov(),
optimal_num_clusters=6,
asset_returns=returns)
weights = hcaa.weights.values[0]
assert (weights >= 0).all()
assert len(weights) == self.data.shape[1]
np.testing.assert_almost_equal(np.sum(weights), 1)
def test_hrp_with_input_as_distance_matrix(self):
"""
Test HRP when passing a distance matrix as input.
"""
hrp = HierarchicalRiskParity()
returns = ReturnsEstimation().calculate_returns(asset_prices=self.data)
covariance = returns.cov()
d_matrix = np.zeros_like(covariance)
diagnoal_sqrt = np.sqrt(np.diag(covariance))
np.fill_diagonal(d_matrix, diagnoal_sqrt)
d_inv = np.linalg.inv(d_matrix)
corr = np.dot(np.dot(d_inv, covariance), d_inv)
corr = pd.DataFrame(corr,
index=covariance.columns,
columns=covariance.columns)
distance_matrix = np.sqrt((1 - corr).round(5) / 2)
hrp.allocate(asset_names=self.data.columns,
covariance_matrix=covariance,
distance_matrix=distance_matrix)
weights = hrp.weights.values[0]
self.assertTrue((weights >= 0).all())
self.assertTrue(len(weights) == self.data.shape[1])
self.assertAlmostEqual(np.sum(weights), 1)