def test_multiple_talib_with_args(self):
zipline_transforms = [ta.MA(timeperiod=10),
ta.MA(timeperiod=25)]
talib_fn = talib.abstract.MA
algo = TALIBAlgorithm(talib=zipline_transforms)
algo.run(self.source)
# Test if computed values match those computed by pandas rolling mean.
sid = 0
talib_values = np.array([x[sid] for x in
algo.talib_results[zipline_transforms[0]]])
np.testing.assert_array_equal(talib_values,
pd.rolling_mean(self.panel[0]['price'],
10).values)
talib_values = np.array([x[sid] for x in
algo.talib_results[zipline_transforms[1]]])
np.testing.assert_array_equal(talib_values,
pd.rolling_mean(self.panel[0]['price'],
25).values)
for t in zipline_transforms:
talib_result = np.array(algo.talib_results[t][-1])
talib_data = dict()
data = t.window
# TODO: Figure out if we are clobbering the tests by this
# protection against empty windows
if not data:
continue
for key in ['open', 'high', 'low', 'volume']:
if key in data:
talib_data[key] = data[key][0].values
talib_data['close'] = data['price'][0].values
expected_result = talib_fn(talib_data, **t.call_kwargs)[-1]
np.testing.assert_allclose(talib_result, expected_result)
def test_talib_with_minute_data(self):
ma_one_day_minutes = ta.MA(timeperiod=10, bars='minute')
# Assert that the BatchTransform window length is enough to cover
# the amount of minutes in the timeperiod.
# Here, 10 minutes only needs a window length of 1.
self.assertEquals(1, ma_one_day_minutes.window_length)
# With minutes greater than the 390, i.e. one trading day, we should
# have a window_length of two days.
ma_two_day_minutes = ta.MA(timeperiod=490, bars='minute')
self.assertEquals(2, ma_two_day_minutes.window_length)
