catalyst/tests/test_transforms_talib.py

#
# Copyright 2013 Quantopian, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from datetime import timedelta, datetime
from unittest import TestCase, skip

import numpy as np
import pandas as pd
import pytz
import talib

from zipline.finance.trading import TradingEnvironment
from zipline.test_algorithms import TALIBAlgorithm
from zipline.testing import setup_logger, teardown_logger
import zipline.transforms.ta as ta
import zipline.utils.factory as factory


class TestTALIB(TestCase):

    @classmethod
    def setUpClass(cls):
        cls.env = TradingEnvironment()

    @classmethod
    def tearDownClass(cls):
        del cls.env

    def setUp(self):
        setup_logger(self)
        sim_params = factory.create_simulation_parameters(
            start=datetime(1990, 1, 1, tzinfo=pytz.utc),
            end=datetime(1990, 3, 30, tzinfo=pytz.utc))
        self.source, self.panel = \
            factory.create_test_panel_ohlc_source(sim_params, self.env)

    def tearDown(self):
        teardown_logger(self)

    @skip
    def test_talib_with_default_params(self):
        BLACKLIST = ['make_transform', 'BatchTransform',
                     # TODO: Figure out why MAVP generates a KeyError
                     'MAVP']
        names = [name for name in dir(ta)
                 if name[0].isupper() and name not in BLACKLIST]

        for name in names:
            print(name)
            zipline_transform = getattr(ta, name)(sid=0)
            talib_fn = getattr(talib.abstract, name)

            start = datetime(1990, 1, 1, tzinfo=pytz.utc)
            end = start + timedelta(days=zipline_transform.lookback + 10)
            sim_params = factory.create_simulation_parameters(
                start=start, end=end)
            source, panel = \
                factory.create_test_panel_ohlc_source(sim_params, self.env)

            algo = TALIBAlgorithm(talib=zipline_transform)
            algo.run(source)

            zipline_result = np.array(
                algo.talib_results[zipline_transform][-1])

            talib_data = dict()
            data = zipline_transform.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)

            if isinstance(expected_result, list):
                expected_result = np.array([e[-1] for e in expected_result])
            else:
                expected_result = np.array(expected_result[-1])
            if not (np.all(np.isnan(zipline_result)) and
                    np.all(np.isnan(expected_result))):
                self.assertTrue(np.allclose(zipline_result, expected_result))
            else:
                print('--- NAN')

            # reset generator so next iteration has data
            # self.source, self.panel = \
                # factory.create_test_panel_ohlc_source(self.sim_params)

    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)

    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)

        # TODO: Ensure that the lookback into the datapanel is returning
        # expected results.
        # Requires supplying minute instead of day data to the unit test.
        # When adding test data, should add more minute events than the
        # timeperiod to ensure that lookback is behaving properly.