def test_progress_bar(self):
"""
Tests that verbose=True prints out progress bar.
"""
# Initialize OLPS.
olps9 = OLPS()
# Allocates asset prices to OLPS with verbose=True.
olps9.allocate(self.data, resample_by='M', verbose=True)
def test_olps_incorrect_data(self):
"""
Tests ValueError if the user inputted data is not a dataframe.
"""
with self.assertRaises(ValueError):
# Initialize OLPS.
olps3 = OLPS()
# Running alloate will raise ValueError.
olps3.allocate(self.data.values)
def test_olps_index_error(self):
"""
Tests ValueError if the passing dataframe is not indexed by date.
"""
# Initialize OLPS.
olps4 = OLPS()
# Reset index.
data = self.data.reset_index()
with self.assertRaises(ValueError):
# Running allocate will raise ValueError.
olps4.allocate(data)
def test_simplex_all_negatives(self):
"""
Tests case where negative weights have to be projected onto the simplex.
"""
# Initialize OLPS.
olps10 = OLPS()
# Allocates asset prices to OLPS with verbose=True.
olps10.allocate(self.data, resample_by='M')
# Negative weights.
neg_weight = np.array([-10e20, -10e20])
np.testing.assert_almost_equal(olps10._simplex_projection(neg_weight), np.array([0.5, 0.5]))
def test_uniform_weight(self):
"""
Tests that uniform weights return equal allocation of weights.
"""
# Initialize OLPS.
olps6 = OLPS()
# Allocates asset prices to OLPS.
olps6.allocate(self.data, resample_by='M')
# Calculate uniform weights.
olps6_uni_weight = olps6._uniform_weight()
# Calculated weights should be equal.
np.testing.assert_almost_equal(olps6_uni_weight, np.array(olps6.all_weights)[0])
def test_simplex_projection(self):
"""
Tests edge cases where the inputted weights already satisfy the simplex requirements.
"""
# Initialize OLPS.
olps8 = OLPS()
# Allocates asset prices to OLPS.
olps8.allocate(self.data, resample_by='M')
# Initialize uniform weights.
weights = olps8._uniform_weight()
# Project uniform weights to simplex domain.
simplex_weights = olps8._simplex_projection(weights)
# The two weights should be the same value.
np.testing.assert_almost_equal(weights, simplex_weights)
def test_normalize(self):
"""
Tests that weights sum to 1.
"""
# Initialize OLPS.
olps7 = OLPS()
# Allocates asset prices to OLPS.
olps7.allocate(self.data, resample_by='M')
# Test normalization on a random weight.
random_weight = np.ones(3)
# Use method to normalize random_weight.
normalized_weight = olps7._normalize(random_weight)
# Compare normalized value and manually calculated value.
np.testing.assert_almost_equal(normalized_weight, random_weight / 3)
def test_olps_solution(self):
"""
Test the calculation of OLPS weights.
"""
# Initialize OLPS.
olps = OLPS()
# Allocates asset prices to OLPS.
olps.allocate(self.data)
# Create np.array of all_weights.
all_weights = np.array(olps.all_weights)
# Check if all weights sum to 1.
for i in range(all_weights.shape[0]):
weights = all_weights[i]
assert (weights >= 0).all()
assert len(weights) == self.data.shape[1]
np.testing.assert_almost_equal(np.sum(weights), 1)
def test_user_weight(self):
"""
Tests that users can input their own weights for OLPS.
"""
# Initialize user inputted weights.
weight = np.zeros(self.data.shape[1])
weight[0] = 1
# Initialize OLPS.
olps5 = OLPS()
# Allocates asset prices to OLPS.
olps5.allocate(self.data, weights=weight, resample_by='M')
# Create np.array of all_weights.
all_weights = np.array(olps5.all_weights)
# Check if all weights sum to 1.
for i in range(all_weights.shape[0]):
weights = all_weights[i]
assert (weights >= 0).all()
assert len(weights) == self.data.shape[1]
np.testing.assert_almost_equal(np.sum(weights), 1)
def test_olps_weight(self):
"""
Tests that the user inputted weights have matching dimensions as the data's dimensions
and ValueError if the user inputted weights do not sum to one.
"""
# Initialize OLPS.
olps1 = OLPS()
# Raise error if weight does not match data.shape[1].
with self.assertRaises(ValueError):
olps1.allocate(self.data, weights=[1])
with self.assertRaises(AssertionError):
# Initialize OLPS.
olps2 = OLPS()
# Initialize weights that do not sum to 1.
weight = np.zeros(self.data.shape[1])
weight[0], weight[1] = 0.4, 0.4
# Running allocate will raise ValueError.
olps2.allocate(self.data, weight)
def test_null_zero_date(self):
"""
Tests ValueError for data with values of null or zero.
"""
# Create null data.
null_data = self.data.copy()
null_data[:] = np.nan
# Create zero data.
zero_data = self.data.copy()
zero_data[:] = 0
# Initialize OLPS.
olps11 = OLPS()
olps12 = OLPS()
with self.assertRaises(ValueError):
olps11.allocate(null_data)
with self.assertRaises(ValueError):
olps12.allocate(zero_data)