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MAINT: Move daily aggregator to own module.
Break out the daily history aggregator into its own module, instead of being collocated with DataPortal.
This commit is contained in:
Eddie Hebert
@@ -0,0 +1,279 @@
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#
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# Copyright 2016 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 numbers import Real
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from nose_parameterized import parameterized
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from numpy.testing import assert_almost_equal
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from numpy import nan
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import pandas as pd
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from zipline.data.data_portal import DailyHistoryAggregator
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from zipline.testing.fixtures import (
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WithBcolzEquityMinuteBarReader,
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ZiplineTestCase,
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)
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OHLC = ['open', 'high', 'low', 'close']
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OHLCV = OHLC + ['volume']
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class MinuteToDailyAggregationTestCase(WithBcolzEquityMinuteBarReader,
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ZiplineTestCase):
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# March 2016
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4 5
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# 6 7 8 9 10 11 12
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# 13 14 15 16 17 18 19
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# 20 21 22 23 24 25 26
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# 27 28 29 30 31
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TRADING_ENV_MIN_DATE = START_DATE = pd.Timestamp(
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'2016-03-01', tz='UTC',
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)
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TRADING_ENV_MAX_DATE = END_DATE = pd.Timestamp(
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'2016-03-31', tz='UTC',
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)
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ASSET_FINDER_EQUITY_SIDS = 1, 2
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minutes = pd.date_range('2016-03-15 9:31',
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'2016-03-15 9:36',
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freq='min',
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tz='US/Eastern').tz_convert('UTC')
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@classmethod
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def make_equity_minute_bar_data(cls):
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# sid data is created so that at least one high is lower than a
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# previous high, and the inverse for low
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yield 1, pd.DataFrame(
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{
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'open': [nan, 103.50, 102.50, 104.50, 101.50, nan],
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'high': [nan, 103.90, 102.90, 104.90, 101.90, nan],
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'low': [nan, 103.10, 102.10, 104.10, 101.10, nan],
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'close': [nan, 103.30, 102.30, 104.30, 101.30, nan],
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'volume': [0, 1003, 1002, 1004, 1001, 0]
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},
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index=cls.minutes,
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)
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# sid 2 is included to provide data on different bars than sid 1,
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# as will as illiquidty mid-day
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yield 2, pd.DataFrame(
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{
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'open': [201.50, nan, 204.50, nan, 200.50, 202.50],
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'high': [201.90, nan, 204.90, nan, 200.90, 202.90],
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'low': [201.10, nan, 204.10, nan, 200.10, 202.10],
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'close': [201.30, nan, 203.50, nan, 200.30, 202.30],
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'volume': [2001, 0, 2004, 0, 2000, 2002],
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},
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index=cls.minutes,
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)
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expected_values = {
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1: pd.DataFrame(
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{
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'open': [nan, 103.50, 103.50, 103.50, 103.50, 103.50],
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'high': [nan, 103.90, 103.90, 104.90, 104.90, 104.90],
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'low': [nan, 103.10, 102.10, 102.10, 101.10, 101.10],
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'close': [nan, 103.30, 102.30, 104.30, 101.30, 101.30],
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'volume': [0, 1003, 2005, 3009, 4010, 4010]
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},
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index=minutes,
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),
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2: pd.DataFrame(
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{
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'open': [201.50, 201.50, 201.50, 201.50, 201.50, 201.50],
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'high': [201.90, 201.90, 204.90, 204.90, 204.90, 204.90],
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'low': [201.10, 201.10, 201.10, 201.10, 200.10, 200.10],
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'close': [201.30, 201.30, 203.50, 203.50, 200.30, 202.30],
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'volume': [2001, 2001, 4005, 4005, 6005, 8007],
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},
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index=minutes,
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)
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}
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@classmethod
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def init_class_fixtures(cls):
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super(MinuteToDailyAggregationTestCase, cls).init_class_fixtures()
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cls.EQUITIES = {
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1: cls.env.asset_finder.retrieve_asset(1),
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2: cls.env.asset_finder.retrieve_asset(2)
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}
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def init_instance_fixtures(self):
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super(MinuteToDailyAggregationTestCase, self).init_instance_fixtures()
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# Set up a fresh data portal for each test, since order of calling
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# needs to be tested.
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self.equity_daily_aggregator = DailyHistoryAggregator(
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self.trading_calendar.schedule.market_open,
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self.bcolz_equity_minute_bar_reader,
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)
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@parameterized.expand([
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('open_sid_1', 'open', 1),
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('high_1', 'high', 1),
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('low_1', 'low', 1),
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('close_1', 'close', 1),
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('volume_1', 'volume', 1),
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('open_2', 'open', 2),
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('high_2', 'high', 2),
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('low_2', 'low', 2),
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('close_2', 'close', 2),
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('volume_2', 'volume', 2),
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])
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def test_contiguous_minutes_individual(self, name, field, sid):
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# First test each minute in order.
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method_name = field + 's'
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results = []
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repeat_results = []
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asset = self.EQUITIES[sid]
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for minute in self.minutes:
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value = getattr(self.equity_daily_aggregator, method_name)(
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[asset], minute)[0]
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# Prevent regression on building an array when scalar is intended.
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self.assertIsInstance(value, Real)
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results.append(value)
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# Call a second time with the same dt, to prevent regression
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# against case where crossed start and end dts caused a crash
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# instead of the last value.
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value = getattr(self.equity_daily_aggregator, method_name)(
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[asset], minute)[0]
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# Prevent regression on building an array when scalar is intended.
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self.assertIsInstance(value, Real)
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repeat_results.append(value)
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assert_almost_equal(results, self.expected_values[asset][field],
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err_msg='sid={0} field={1}'.format(asset, field))
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assert_almost_equal(repeat_results, self.expected_values[asset][field],
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err_msg='sid={0} field={1}'.format(asset, field))
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@parameterized.expand([
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('open_sid_1', 'open', 1),
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('high_1', 'high', 1),
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('low_1', 'low', 1),
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('close_1', 'close', 1),
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('volume_1', 'volume', 1),
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('open_2', 'open', 2),
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('high_2', 'high', 2),
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('low_2', 'low', 2),
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('close_2', 'close', 2),
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('volume_2', 'volume', 2),
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])
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def test_skip_minutes_individual(self, name, field, sid):
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# Test skipping minutes, to exercise backfills.
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# Tests initial backfill and mid day backfill.
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method_name = field + 's'
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for i in [1, 5]:
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minute = self.minutes[i]
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asset = self.EQUITIES[sid]
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value = getattr(self.equity_daily_aggregator, method_name)(
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[asset], minute)[0]
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# Prevent regression on building an array when scalar is intended.
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self.assertIsInstance(value, Real)
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assert_almost_equal(value,
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self.expected_values[sid][field][i],
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err_msg='sid={0} field={1} dt={2}'.format(
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sid, field, minute))
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# Call a second time with the same dt, to prevent regression
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# against case where crossed start and end dts caused a crash
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# instead of the last value.
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value = getattr(self.equity_daily_aggregator, method_name)(
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[asset], minute)[0]
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# Prevent regression on building an array when scalar is intended.
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self.assertIsInstance(value, Real)
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assert_almost_equal(value,
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self.expected_values[sid][field][i],
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err_msg='sid={0} field={1} dt={2}'.format(
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sid, field, minute))
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@parameterized.expand(OHLCV)
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def test_contiguous_minutes_multiple(self, field):
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# First test each minute in order.
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method_name = field + 's'
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assets = sorted(self.EQUITIES.values())
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results = {asset: [] for asset in assets}
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repeat_results = {asset: [] for asset in assets}
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for minute in self.minutes:
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values = getattr(self.equity_daily_aggregator, method_name)(
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assets, minute)
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for j, asset in enumerate(assets):
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value = values[j]
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# Prevent regression on building an array when scalar is
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# intended.
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self.assertIsInstance(value, Real)
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results[asset].append(value)
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# Call a second time with the same dt, to prevent regression
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# against case where crossed start and end dts caused a crash
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# instead of the last value.
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values = getattr(self.equity_daily_aggregator, method_name)(
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assets, minute)
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for j, asset in enumerate(assets):
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value = values[j]
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# Prevent regression on building an array when scalar is
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# intended.
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self.assertIsInstance(value, Real)
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repeat_results[asset].append(value)
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for asset in assets:
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assert_almost_equal(results[asset],
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self.expected_values[asset][field],
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err_msg='sid={0} field={1}'.format(
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asset, field))
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assert_almost_equal(repeat_results[asset],
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self.expected_values[asset][field],
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err_msg='sid={0} field={1}'.format(
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asset, field))
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@parameterized.expand(OHLCV)
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def test_skip_minutes_multiple(self, field):
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# Test skipping minutes, to exercise backfills.
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# Tests initial backfill and mid day backfill.
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method_name = field + 's'
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assets = sorted(self.EQUITIES.values())
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for i in [1, 5]:
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minute = self.minutes[i]
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values = getattr(self.equity_daily_aggregator, method_name)(
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assets, minute)
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for j, asset in enumerate(assets):
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value = values[j]
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# Prevent regression on building an array when scalar is
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# intended.
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self.assertIsInstance(value, Real)
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assert_almost_equal(
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value,
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self.expected_values[asset][field][i],
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err_msg='sid={0} field={1} dt={2}'.format(
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asset, field, minute))
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# Call a second time with the same dt, to prevent regression
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# against case where crossed start and end dts caused a crash
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# instead of the last value.
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values = getattr(self.equity_daily_aggregator, method_name)(
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assets, minute)
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for j, asset in enumerate(assets):
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value = values[j]
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# Prevent regression on building an array when scalar is
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# intended.
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self.assertIsInstance(value, Real)
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assert_almost_equal(
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value,
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self.expected_values[asset][field][i],
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err_msg='sid={0} field={1} dt={2}'.format(
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asset, field, minute))
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+14
-255
@@ -1,18 +1,28 @@
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#
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# Copyright 2016 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 textwrap import dedent
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from numbers import Real
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from nose_parameterized import parameterized
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import numpy as np
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from numpy import nan
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from numpy.testing import assert_almost_equal
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import pandas as pd
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from six import iteritems
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from zipline import TradingAlgorithm
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from zipline._protocol import handle_non_market_minutes
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from zipline.assets import Asset
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from zipline.data.data_portal import DailyHistoryAggregator
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from zipline.errors import (
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HistoryInInitialize,
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HistoryWindowStartsBeforeData,
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@@ -25,7 +35,6 @@ from zipline.testing import (
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MockDailyBarReader,
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)
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from zipline.testing.fixtures import (
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WithBcolzEquityMinuteBarReader,
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WithDataPortal,
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ZiplineTestCase,
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alias,
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@@ -33,7 +42,6 @@ from zipline.testing.fixtures import (
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OHLC = ['open', 'high', 'low', 'close']
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OHLCV = OHLC + ['volume']
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OHLCP = OHLC + ['price']
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ALL_FIELDS = OHLCP + ['volume']
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@@ -1712,252 +1720,3 @@ class DailyEquityHistoryTestCase(WithHistory, ZiplineTestCase):
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window_2[self.ASSET1].values)
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np.testing.assert_almost_equal(window_1[self.ASSET2].values,
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window_2[self.ASSET2].values)
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class MinuteToDailyAggregationTestCase(WithBcolzEquityMinuteBarReader,
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ZiplineTestCase):
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# March 2016
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# Su Mo Tu We Th Fr Sa
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# 1 2 3 4 5
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# 6 7 8 9 10 11 12
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# 13 14 15 16 17 18 19
|
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# 20 21 22 23 24 25 26
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# 27 28 29 30 31
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TRADING_ENV_MIN_DATE = START_DATE = pd.Timestamp(
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'2016-03-01', tz='UTC',
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)
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TRADING_ENV_MAX_DATE = END_DATE = pd.Timestamp(
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'2016-03-31', tz='UTC',
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)
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ASSET_FINDER_EQUITY_SIDS = 1, 2
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minutes = pd.date_range('2016-03-15 9:31',
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'2016-03-15 9:36',
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freq='min',
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tz='US/Eastern').tz_convert('UTC')
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@classmethod
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def make_equity_minute_bar_data(cls):
|
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# sid data is created so that at least one high is lower than a
|
||||
# previous high, and the inverse for low
|
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yield 1, pd.DataFrame(
|
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{
|
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'open': [nan, 103.50, 102.50, 104.50, 101.50, nan],
|
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'high': [nan, 103.90, 102.90, 104.90, 101.90, nan],
|
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'low': [nan, 103.10, 102.10, 104.10, 101.10, nan],
|
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'close': [nan, 103.30, 102.30, 104.30, 101.30, nan],
|
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'volume': [0, 1003, 1002, 1004, 1001, 0]
|
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},
|
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index=cls.minutes,
|
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)
|
||||
# sid 2 is included to provide data on different bars than sid 1,
|
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# as will as illiquidty mid-day
|
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yield 2, pd.DataFrame(
|
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{
|
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'open': [201.50, nan, 204.50, nan, 200.50, 202.50],
|
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'high': [201.90, nan, 204.90, nan, 200.90, 202.90],
|
||||
'low': [201.10, nan, 204.10, nan, 200.10, 202.10],
|
||||
'close': [201.30, nan, 203.50, nan, 200.30, 202.30],
|
||||
'volume': [2001, 0, 2004, 0, 2000, 2002],
|
||||
},
|
||||
index=cls.minutes,
|
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)
|
||||
|
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expected_values = {
|
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1: pd.DataFrame(
|
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{
|
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'open': [nan, 103.50, 103.50, 103.50, 103.50, 103.50],
|
||||
'high': [nan, 103.90, 103.90, 104.90, 104.90, 104.90],
|
||||
'low': [nan, 103.10, 102.10, 102.10, 101.10, 101.10],
|
||||
'close': [nan, 103.30, 102.30, 104.30, 101.30, 101.30],
|
||||
'volume': [0, 1003, 2005, 3009, 4010, 4010]
|
||||
},
|
||||
index=minutes,
|
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),
|
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2: pd.DataFrame(
|
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{
|
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'open': [201.50, 201.50, 201.50, 201.50, 201.50, 201.50],
|
||||
'high': [201.90, 201.90, 204.90, 204.90, 204.90, 204.90],
|
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'low': [201.10, 201.10, 201.10, 201.10, 200.10, 200.10],
|
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'close': [201.30, 201.30, 203.50, 203.50, 200.30, 202.30],
|
||||
'volume': [2001, 2001, 4005, 4005, 6005, 8007],
|
||||
},
|
||||
index=minutes,
|
||||
)
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def init_class_fixtures(cls):
|
||||
super(MinuteToDailyAggregationTestCase, cls).init_class_fixtures()
|
||||
|
||||
cls.EQUITIES = {
|
||||
1: cls.env.asset_finder.retrieve_asset(1),
|
||||
2: cls.env.asset_finder.retrieve_asset(2)
|
||||
}
|
||||
|
||||
def init_instance_fixtures(self):
|
||||
super(MinuteToDailyAggregationTestCase, self).init_instance_fixtures()
|
||||
# Set up a fresh data portal for each test, since order of calling
|
||||
# needs to be tested.
|
||||
self.equity_daily_aggregator = DailyHistoryAggregator(
|
||||
self.trading_calendar.schedule.market_open,
|
||||
self.bcolz_equity_minute_bar_reader,
|
||||
)
|
||||
|
||||
@parameterized.expand([
|
||||
('open_sid_1', 'open', 1),
|
||||
('high_1', 'high', 1),
|
||||
('low_1', 'low', 1),
|
||||
('close_1', 'close', 1),
|
||||
('volume_1', 'volume', 1),
|
||||
('open_2', 'open', 2),
|
||||
('high_2', 'high', 2),
|
||||
('low_2', 'low', 2),
|
||||
('close_2', 'close', 2),
|
||||
('volume_2', 'volume', 2),
|
||||
|
||||
])
|
||||
def test_contiguous_minutes_individual(self, name, field, sid):
|
||||
# First test each minute in order.
|
||||
method_name = field + 's'
|
||||
results = []
|
||||
repeat_results = []
|
||||
asset = self.EQUITIES[sid]
|
||||
for minute in self.minutes:
|
||||
value = getattr(self.equity_daily_aggregator, method_name)(
|
||||
[asset], minute)[0]
|
||||
# Prevent regression on building an array when scalar is intended.
|
||||
self.assertIsInstance(value, Real)
|
||||
results.append(value)
|
||||
|
||||
# Call a second time with the same dt, to prevent regression
|
||||
# against case where crossed start and end dts caused a crash
|
||||
# instead of the last value.
|
||||
value = getattr(self.equity_daily_aggregator, method_name)(
|
||||
[asset], minute)[0]
|
||||
# Prevent regression on building an array when scalar is intended.
|
||||
self.assertIsInstance(value, Real)
|
||||
repeat_results.append(value)
|
||||
|
||||
assert_almost_equal(results, self.expected_values[asset][field],
|
||||
err_msg='sid={0} field={1}'.format(asset, field))
|
||||
assert_almost_equal(repeat_results, self.expected_values[asset][field],
|
||||
err_msg='sid={0} field={1}'.format(asset, field))
|
||||
|
||||
@parameterized.expand([
|
||||
('open_sid_1', 'open', 1),
|
||||
('high_1', 'high', 1),
|
||||
('low_1', 'low', 1),
|
||||
('close_1', 'close', 1),
|
||||
('volume_1', 'volume', 1),
|
||||
('open_2', 'open', 2),
|
||||
('high_2', 'high', 2),
|
||||
('low_2', 'low', 2),
|
||||
('close_2', 'close', 2),
|
||||
('volume_2', 'volume', 2),
|
||||
|
||||
])
|
||||
def test_skip_minutes_individual(self, name, field, sid):
|
||||
# Test skipping minutes, to exercise backfills.
|
||||
# Tests initial backfill and mid day backfill.
|
||||
method_name = field + 's'
|
||||
for i in [1, 5]:
|
||||
minute = self.minutes[i]
|
||||
asset = self.EQUITIES[sid]
|
||||
value = getattr(self.equity_daily_aggregator, method_name)(
|
||||
[asset], minute)[0]
|
||||
# Prevent regression on building an array when scalar is intended.
|
||||
self.assertIsInstance(value, Real)
|
||||
assert_almost_equal(value,
|
||||
self.expected_values[sid][field][i],
|
||||
err_msg='sid={0} field={1} dt={2}'.format(
|
||||
sid, field, minute))
|
||||
|
||||
# Call a second time with the same dt, to prevent regression
|
||||
# against case where crossed start and end dts caused a crash
|
||||
# instead of the last value.
|
||||
value = getattr(self.equity_daily_aggregator, method_name)(
|
||||
[asset], minute)[0]
|
||||
# Prevent regression on building an array when scalar is intended.
|
||||
self.assertIsInstance(value, Real)
|
||||
assert_almost_equal(value,
|
||||
self.expected_values[sid][field][i],
|
||||
err_msg='sid={0} field={1} dt={2}'.format(
|
||||
sid, field, minute))
|
||||
|
||||
@parameterized.expand(OHLCV)
|
||||
def test_contiguous_minutes_multiple(self, field):
|
||||
# First test each minute in order.
|
||||
method_name = field + 's'
|
||||
assets = sorted(self.EQUITIES.values())
|
||||
results = {asset: [] for asset in assets}
|
||||
repeat_results = {asset: [] for asset in assets}
|
||||
for minute in self.minutes:
|
||||
values = getattr(self.equity_daily_aggregator, method_name)(
|
||||
assets, minute)
|
||||
for j, asset in enumerate(assets):
|
||||
value = values[j]
|
||||
# Prevent regression on building an array when scalar is
|
||||
# intended.
|
||||
self.assertIsInstance(value, Real)
|
||||
results[asset].append(value)
|
||||
|
||||
# Call a second time with the same dt, to prevent regression
|
||||
# against case where crossed start and end dts caused a crash
|
||||
# instead of the last value.
|
||||
values = getattr(self.equity_daily_aggregator, method_name)(
|
||||
assets, minute)
|
||||
for j, asset in enumerate(assets):
|
||||
value = values[j]
|
||||
# Prevent regression on building an array when scalar is
|
||||
# intended.
|
||||
self.assertIsInstance(value, Real)
|
||||
repeat_results[asset].append(value)
|
||||
for asset in assets:
|
||||
assert_almost_equal(results[asset],
|
||||
self.expected_values[asset][field],
|
||||
err_msg='sid={0} field={1}'.format(
|
||||
asset, field))
|
||||
assert_almost_equal(repeat_results[asset],
|
||||
self.expected_values[asset][field],
|
||||
err_msg='sid={0} field={1}'.format(
|
||||
asset, field))
|
||||
|
||||
@parameterized.expand(OHLCV)
|
||||
def test_skip_minutes_multiple(self, field):
|
||||
# Test skipping minutes, to exercise backfills.
|
||||
# Tests initial backfill and mid day backfill.
|
||||
method_name = field + 's'
|
||||
assets = sorted(self.EQUITIES.values())
|
||||
for i in [1, 5]:
|
||||
minute = self.minutes[i]
|
||||
values = getattr(self.equity_daily_aggregator, method_name)(
|
||||
assets, minute)
|
||||
for j, asset in enumerate(assets):
|
||||
value = values[j]
|
||||
# Prevent regression on building an array when scalar is
|
||||
# intended.
|
||||
self.assertIsInstance(value, Real)
|
||||
assert_almost_equal(
|
||||
value,
|
||||
self.expected_values[asset][field][i],
|
||||
err_msg='sid={0} field={1} dt={2}'.format(
|
||||
asset, field, minute))
|
||||
|
||||
# Call a second time with the same dt, to prevent regression
|
||||
# against case where crossed start and end dts caused a crash
|
||||
# instead of the last value.
|
||||
values = getattr(self.equity_daily_aggregator, method_name)(
|
||||
assets, minute)
|
||||
for j, asset in enumerate(assets):
|
||||
value = values[j]
|
||||
# Prevent regression on building an array when scalar is
|
||||
# intended.
|
||||
self.assertIsInstance(value, Real)
|
||||
assert_almost_equal(
|
||||
value,
|
||||
self.expected_values[asset][field][i],
|
||||
err_msg='sid={0} field={1} dt={2}'.format(
|
||||
asset, field, minute))
|
||||
|
||||
Reference in New Issue
Block a user