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https://github.com/wassname/catalyst.git
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Freddie Vargus
a12c34c39c
I'm not sure what the raw csv pulled from the federal reserve looked like before, but when trying to download fresh treasure data (data not stored in `./zipline`), there is an error that says "Time Period not in list". After checking the raw csv now, it looks like there are 5 header rows rather than just 1, so skipping those rows removes that error.
80 lines
2.8 KiB
Python
80 lines
2.8 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
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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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from zipline.utils.paths import ensure_file
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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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@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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market_data = ('SPY_benchmark.csv', 'treasury_curves.csv')
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for data in market_data:
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ensure_file(cls.tmpdir.getpath('example_data/root/data/' + data))
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@parameterized.expand(sorted(examples.EXAMPLE_MODULES))
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def test_example(self, example_name):
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actual_perf = examples.run_example(
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example_name,
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# This should match the invocation in
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# zipline/tests/resources/rebuild_example_data
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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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)
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assert_equal(
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actual_perf[examples._cols_to_check],
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self.expected_perf[example_name][examples._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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