mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 04:05:16 +08:00
Eddie Hebert
115 lines
4.2 KiB
Python
115 lines
4.2 KiB
Python
from collections import deque
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import pytz
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import numpy as np
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import pandas as pd
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from datetime import datetime
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from unittest import TestCase
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from zipline.utils.test_utils import setup_logger
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import zipline.utils.factory as factory
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from zipline.test_algorithms import BatchTransformAlgorithm
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class TestBatchTransform(TestCase):
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def setUp(self):
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self.sim_params = factory.create_simulation_parameters(
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start=datetime(1990, 1, 1, tzinfo=pytz.utc),
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end=datetime(1990, 1, 8, tzinfo=pytz.utc)
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)
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setup_logger(self)
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self.source, self.df = \
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factory.create_test_df_source(self.sim_params)
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def test_event_window(self):
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algo = BatchTransformAlgorithm(sim_params=self.sim_params)
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algo.run(self.source)
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wl = algo.window_length
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# The following assertion depend on window length of 3
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self.assertEqual(wl, 3)
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self.assertEqual(algo.history_return_price_class[:wl],
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[None] * wl,
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"First three iterations should return None." + "\n" +
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"i.e. no returned values until window is full'" +
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"%s" % (algo.history_return_price_class,))
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self.assertEqual(algo.history_return_price_decorator[:wl],
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[None] * wl,
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"First three iterations should return None." + "\n" +
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"i.e. no returned values until window is full'" +
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"%s" % (algo.history_return_price_decorator,))
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# After three Nones, the next value should be a data frame
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self.assertTrue(isinstance(
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algo.history_return_price_class[wl],
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pd.DataFrame)
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)
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# Test whether arbitrary fields can be added to datapanel
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field = algo.history_return_arbitrary_fields[-1]
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self.assertTrue(
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'arbitrary' in field.items,
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'datapanel should contain column arbitrary'
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)
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self.assertTrue(all(
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field['arbitrary'].values.flatten() ==
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[123] * algo.window_length),
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'arbitrary dataframe should contain only "test"'
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)
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for data in algo.history_return_sid_filter[wl:]:
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self.assertIn(0, data.columns)
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self.assertNotIn(1, data.columns)
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for data in algo.history_return_field_filter[wl:]:
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self.assertIn('price', data.items)
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self.assertNotIn('ignore', data.items)
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for data in algo.history_return_field_no_filter[wl:]:
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self.assertIn('price', data.items)
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self.assertIn('ignore', data.items)
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for data in algo.history_return_ticks[wl:]:
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self.assertTrue(isinstance(data, deque))
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for data in algo.history_return_not_full:
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self.assertIsNot(data, None)
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# test overloaded class
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for test_history in [algo.history_return_price_class,
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algo.history_return_price_decorator]:
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# starting at window length, the window should contain
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# consecutive (of window length) numbers up till the end.
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for i in range(algo.window_length, len(test_history)):
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np.testing.assert_array_equal(
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range(i - algo.window_length + 1, i + 1),
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test_history[i].values.flatten()
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)
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def test_passing_of_args(self):
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algo = BatchTransformAlgorithm(1,
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kwarg='str', sim_params=self.sim_params)
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self.assertEqual(algo.args, (1,))
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self.assertEqual(algo.kwargs, {'kwarg': 'str'})
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algo.run(self.source)
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expected_item = ((1, ), {'kwarg': 'str'})
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self.assertEqual(
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algo.history_return_args,
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[
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# 1990-01-01 - market holiday, no event
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# 1990-01-02 - window not full
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None,
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# 1990-01-03 - window not full
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None,
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# 1990-01-04 - window not full, 3rd event
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None,
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# 1990-01-05 - window now full
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expected_item,
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# 1990-01-08 - window now full
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expected_item
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])
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