def test_random_wrong_use(self):
""" Random wrong use test """
try:
rnd = RandomDataProvider(seed=1)
# Now, the .run() method is expected, which does the actual data loading
# (and can take seconds or minutes,
# depending on the data volumes, hence not ok in the constructor)
with self.subTest('test RandomDataProvider get_covariance_matrix'):
self.assertRaises(QiskitFinanceError,
rnd.get_covariance_matrix)
with self.subTest('test RandomDataProvider get_similarity_matrix'):
self.assertRaises(QiskitFinanceError,
rnd.get_similarity_matrix)
wiki = WikipediaDataProvider(token=self._quandl_token,
tickers=["GOOG", "AAPL"],
start=datetime.datetime(2016, 1, 1),
end=datetime.datetime(2016, 1, 30))
# Now, the .run() method is expected, which does the actual data loading
with self.subTest(
'test WikipediaDataProvider get_covariance_matrix'):
self.assertRaises(QiskitFinanceError,
wiki.get_covariance_matrix)
with self.subTest(
'test WikipediaDataProvider get_similarity_matrix'):
self.assertRaises(QiskitFinanceError,
wiki.get_similarity_matrix)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_random(self):
""" random test """
similarity = np.array([[1.00000000e+00, 6.2284804e-04], [6.2284804e-04, 1.00000000e+00]])
covariance = np.array([[2.08413157, 0.20842107], [0.20842107, 1.99542187]])
try:
rnd = RandomDataProvider(seed=1)
rnd.run()
with self.subTest('test RandomDataProvider get_covariance_matrix'):
np.testing.assert_array_almost_equal(rnd.get_covariance_matrix(),
covariance, decimal=3)
with self.subTest('test RandomDataProvider get_similarity_matrix'):
np.testing.assert_array_almost_equal(rnd.get_similarity_matrix(),
similarity, decimal=3)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_random(self):
"""random test"""
similarity = np.array([[1.00000000e00, 6.2284804e-04],
[6.2284804e-04, 1.00000000e00]])
covariance = np.array([[2.08413157, 0.20842107],
[0.20842107, 1.99542187]])
rnd = RandomDataProvider(seed=1)
rnd.run()
with self.subTest("test RandomDataProvider get_covariance_matrix"):
np.testing.assert_array_almost_equal(rnd.get_covariance_matrix(),
covariance,
decimal=3)
with self.subTest("test RandomDataProvider get_similarity_matrix"):
np.testing.assert_array_almost_equal(rnd.get_similarity_matrix(),
similarity,
decimal=3)
def setUp(self):
super().setUp()
self.seed = 50
algorithm_globals.random_seed = self.seed
num_assets = 4
stocks = [("TICKER%s" % i) for i in range(num_assets)]
data = RandomDataProvider(tickers=stocks,
start=datetime.datetime(2016, 1, 1),
end=datetime.datetime(2016, 1, 30),
seed=self.seed)
data.run()
self.muu = data.get_period_return_mean_vector()
self.sigma = data.get_period_return_covariance_matrix()
self.risk = 0.5
self.budget = int(num_assets / 2)
self.penalty = num_assets
self.qubit_op, self.offset = portfolio.get_operator(
self.muu, self.sigma, self.risk, self.budget, self.penalty)
def test_random_divide_0(self):
""" Random divide by 0 test """
# This will create data with some 0 values, it should not throw
# divide by 0 errors
try:
seed = 8888
num_assets = 4
stocks = [("TICKER%s" % i) for i in range(num_assets)]
data = RandomDataProvider(tickers=stocks,
start=datetime.datetime(2016, 1, 1),
end=datetime.datetime(2016, 1, 30),
seed=seed)
data.run()
mu_value = data.get_period_return_mean_vector()
sigma_value = data.get_period_return_covariance_matrix()
with self.subTest('test get_period_return_mean_vector is numpy array'):
self.assertIsInstance(mu_value, np.ndarray)
with self.subTest('test get_period_return_covariance_matrix is numpy array'):
self.assertIsInstance(sigma_value, np.ndarray)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_random_divide_0(self):
"""Random divide by 0 test"""
# This will create data with some 0 values, it should not throw
# divide by 0 errors
seed = 8888
num_assets = 4
stocks = [f"TICKER{i}" for i in range(num_assets)]
random_data = RandomDataProvider(
tickers=stocks,
start=datetime.datetime(2016, 1, 1),
end=datetime.datetime(2016, 1, 30),
seed=seed,
)
random_data.run()
mu_value = random_data.get_period_return_mean_vector()
sigma_value = random_data.get_period_return_covariance_matrix()
with self.subTest("test get_period_return_mean_vector is numpy array"):
self.assertIsInstance(mu_value, np.ndarray)
with self.subTest(
"test get_period_return_covariance_matrix is numpy array"):
self.assertIsInstance(sigma_value, np.ndarray)