def test_wikipedia(self):
""" wikipedia test """
try:
wiki = WikipediaDataProvider(token=self._quandl_token,
tickers=["GOOG", "AAPL"],
start=datetime.datetime(2016, 1, 1),
end=datetime.datetime(2016, 1, 30))
wiki.run()
similarity = np.array([[1.00000000e+00, 8.44268222e-05],
[8.44268222e-05, 1.00000000e+00]])
covariance = np.array([[269.60118129, 25.42252332],
[25.42252332, 7.86304499]])
with self.subTest(
'test WikipediaDataProvider get_covariance_matrix'):
np.testing.assert_array_almost_equal(
wiki.get_covariance_matrix(), covariance, decimal=3)
with self.subTest(
'test WikipediaDataProvider get_similarity_matrix'):
np.testing.assert_array_almost_equal(
wiki.get_similarity_matrix(), similarity, decimal=3)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
except QiskitFinanceError as ex:
self.skipTest("Test of WikipediaDataProvider skipped: {}".format(
str(ex)))
def test_wikipedia(self):
""" wikipedia test """
wiki = WikipediaDataProvider(
token="",
tickers=["GOOG", "AAPL"],
stockmarket=StockMarket.NASDAQ,
start=datetime.datetime(2016, 1, 1),
end=datetime.datetime(2016, 1, 30)
)
# can throw QiskitFinanceError
try:
wiki.run()
similarity = np.array([
[1.00000000e+00, 8.44268222e-05],
[8.44268222e-05, 1.00000000e+00]
])
covariance = np.array([
[269.60118129, 25.42252332],
[25.42252332, 7.86304499]
])
np.testing.assert_array_almost_equal(wiki.get_covariance_matrix(),
covariance, decimal=3)
np.testing.assert_array_almost_equal(wiki.get_similarity_matrix(),
similarity, decimal=3)
except QiskitFinanceError:
self.skipTest("Test of WikipediaDataProvider skipped due to the per-day usage limits.")