mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 18:43:15 +08:00
Joe Jevnik
89542e33bd
This data bundle will use the quantopian mirror of the quandl WIKI data instead of downloading from quandl directly. This dramatically improves the speed because we do not pay the rate limiting for quandl and we can send the data in the format zipline expects.
115 lines
3.6 KiB
Python
115 lines
3.6 KiB
Python
#
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# Copyright 2013 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from functools import partial
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import tarfile
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import matplotlib
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from nose_parameterized import parameterized
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import pandas as pd
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from zipline import examples, run_algorithm
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from zipline.data.bundles import register, unregister
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from zipline.testing import test_resource_path
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from zipline.testing.fixtures import WithTmpDir, ZiplineTestCase
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from zipline.testing.predicates import assert_equal
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from zipline.utils.cache import dataframe_cache
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# Otherwise the next line sometimes complains about being run too late.
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_multiprocess_can_split_ = False
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matplotlib.use('Agg')
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class ExamplesTests(WithTmpDir, ZiplineTestCase):
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# some columns contain values with unique ids that will not be the same
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cols_to_check = [
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'algo_volatility',
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'algorithm_period_return',
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'alpha',
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'benchmark_period_return',
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'benchmark_volatility',
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'beta',
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'capital_used',
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'ending_cash',
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'ending_exposure',
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'ending_value',
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'excess_return',
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'gross_leverage',
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'long_exposure',
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'long_value',
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'longs_count',
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'max_drawdown',
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'max_leverage',
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'net_leverage',
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'period_close',
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'period_label',
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'period_open',
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'pnl',
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'portfolio_value',
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'positions',
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'returns',
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'short_exposure',
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'short_value',
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'shorts_count',
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'sortino',
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'starting_cash',
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'starting_exposure',
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'starting_value',
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'trading_days',
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'treasury_period_return',
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]
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@classmethod
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def init_class_fixtures(cls):
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super(ExamplesTests, cls).init_class_fixtures()
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register('test', lambda *args: None)
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cls.add_class_callback(partial(unregister, 'test'))
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with tarfile.open(test_resource_path('example_data.tar.gz')) as tar:
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tar.extractall(cls.tmpdir.path)
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cls.expected_perf = dataframe_cache(
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cls.tmpdir.getpath(
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'example_data/expected_perf/%s' %
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pd.__version__.replace('.', '-'),
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),
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serialization='pickle',
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)
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@parameterized.expand(e for e in dir(examples) if not e.startswith('_'))
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def test_example(self, example):
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mod = getattr(examples, example)
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actual_perf = run_algorithm(
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handle_data=mod.handle_data,
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initialize=mod.initialize,
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before_trading_start=getattr(mod, 'before_trading_start', None),
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analyze=getattr(mod, 'analyze', None),
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bundle='test',
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environ={
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'ZIPLINE_ROOT': self.tmpdir.getpath('example_data/root'),
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},
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capital_base=1e7,
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**mod._test_args()
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)
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assert_equal(
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actual_perf[self.cols_to_check],
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self.expected_perf[example][self.cols_to_check],
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# There is a difference in the datetime columns in pandas
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# 0.16 and 0.17 because in 16 they are object and in 17 they are
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# datetime[ns, UTC]. We will just ignore the dtypes for now.
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check_dtype=False,
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)
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