test_transforms_talib.py 文件源码

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, identifiers=[0])
        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)