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""" |
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Tests for the reference loader for EarningsCalendar. |
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""" |
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from unittest import TestCase |
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import blaze as bz |
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from blaze.compute.core import swap_resources_into_scope |
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from contextlib2 import ExitStack |
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from nose_parameterized import parameterized |
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import pandas as pd |
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import numpy as np |
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from pandas.util.testing import assert_series_equal |
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from six import iteritems |
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from zipline.pipeline import Pipeline |
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from zipline.pipeline.data import EarningsCalendar |
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from zipline.pipeline.engine import SimplePipelineEngine |
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from zipline.pipeline.factors.events import ( |
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BusinessDaysUntilNextEarnings, |
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BusinessDaysSincePreviousEarnings, |
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) |
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from zipline.pipeline.loaders.earnings import EarningsCalendarLoader |
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from zipline.pipeline.loaders.blaze import ( |
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ANNOUNCEMENT_FIELD_NAME, |
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BlazeEarningsCalendarLoader, |
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SID_FIELD_NAME, |
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TS_FIELD_NAME, |
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) |
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from zipline.utils.numpy_utils import make_datetime64D, np_NaT |
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from zipline.utils.tradingcalendar import trading_days |
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from zipline.utils.test_utils import ( |
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make_simple_equity_info, |
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powerset, |
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tmp_asset_finder, |
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) |
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def _to_series(knowledge_dates, earning_dates): |
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""" |
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Helper for converting a dict of strings to a Series of datetimes. |
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This is just for making the test cases more readable. |
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""" |
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return pd.Series( |
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index=pd.to_datetime(knowledge_dates), |
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data=pd.to_datetime(earning_dates), |
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) |
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def num_days_in_range(dates, start, end): |
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""" |
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Return the number of days in `dates` between start and end, inclusive. |
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""" |
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start_idx, stop_idx = dates.slice_locs(start, end) |
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return stop_idx - start_idx |
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def gen_calendars(): |
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""" |
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Generate calendars to use as inputs to test_compute_latest. |
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""" |
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start, stop = '2014-01-01', '2014-01-31' |
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all_dates = pd.date_range(start, stop, tz='utc') |
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# These dates are the points where announcements or knowledge dates happen. |
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# Test every combination of them being absent. |
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critical_dates = pd.to_datetime([ |
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'2014-01-05', |
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'2014-01-10', |
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'2014-01-15', |
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'2014-01-20', |
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]) |
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for to_drop in map(list, powerset(critical_dates)): |
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# Have to yield tuples. |
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yield (all_dates.drop(to_drop),) |
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# Also test with the trading calendar. |
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yield (trading_days[trading_days.slice_indexer(start, stop)],) |
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class EarningsCalendarLoaderTestCase(TestCase): |
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""" |
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Tests for loading the earnings announcement data. |
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""" |
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loader_type = EarningsCalendarLoader |
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@classmethod |
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def setUpClass(cls): |
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cls._cleanup_stack = stack = ExitStack() |
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cls.sids = A, B, C, D, E = range(5) |
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equity_info = make_simple_equity_info( |
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cls.sids, |
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start_date=pd.Timestamp('2013-01-01', tz='UTC'), |
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end_date=pd.Timestamp('2015-01-01', tz='UTC'), |
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) |
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cls.finder = stack.enter_context( |
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tmp_asset_finder(equities=equity_info), |
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) |
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cls.earnings_dates = { |
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# K1--K2--E1--E2. |
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A: _to_series( |
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knowledge_dates=['2014-01-05', '2014-01-10'], |
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earning_dates=['2014-01-15', '2014-01-20'], |
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), |
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# K1--K2--E2--E1. |
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B: _to_series( |
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knowledge_dates=['2014-01-05', '2014-01-10'], |
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earning_dates=['2014-01-20', '2014-01-15'] |
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), |
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# K1--E1--K2--E2. |
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C: _to_series( |
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knowledge_dates=['2014-01-05', '2014-01-15'], |
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earning_dates=['2014-01-10', '2014-01-20'] |
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), |
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# K1 == K2. |
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D: _to_series( |
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knowledge_dates=['2014-01-05'] * 2, |
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earning_dates=['2014-01-10', '2014-01-15'], |
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), |
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E: pd.Series( |
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data=[], |
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index=pd.DatetimeIndex([]), |
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dtype='datetime64[ns]', |
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), |
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} |
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@classmethod |
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def tearDownClass(cls): |
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cls._cleanup_stack.close() |
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def loader_args(self, dates): |
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"""Construct the base earnings announcements object to pass to the |
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loader. |
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Parameters |
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---------- |
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dates : pd.DatetimeIndex |
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The dates we can serve. |
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Returns |
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------- |
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args : tuple[any] |
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The arguments to forward to the loader positionally. |
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""" |
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return dates, self.earnings_dates |
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def setup(self, dates): |
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""" |
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Make a PipelineEngine and expectation functions for the given dates |
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calendar. |
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This exists to make it easy to test our various cases with critical |
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dates missing from the calendar. |
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""" |
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A, B, C, D, E = self.sids |
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def num_days_between(start_date, end_date): |
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return num_days_in_range(dates, start_date, end_date) |
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def zip_with_dates(dts): |
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return pd.Series(pd.to_datetime(dts), index=dates) |
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_expected_next_announce = pd.DataFrame({ |
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A: zip_with_dates( |
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['NaT'] * num_days_between(None, '2014-01-04') + |
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['2014-01-15'] * num_days_between('2014-01-05', '2014-01-15') + |
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['2014-01-20'] * num_days_between('2014-01-16', '2014-01-20') + |
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['NaT'] * num_days_between('2014-01-21', None) |
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), |
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B: zip_with_dates( |
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['NaT'] * num_days_between(None, '2014-01-04') + |
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['2014-01-20'] * num_days_between('2014-01-05', '2014-01-09') + |
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['2014-01-15'] * num_days_between('2014-01-10', '2014-01-15') + |
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['2014-01-20'] * num_days_between('2014-01-16', '2014-01-20') + |
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['NaT'] * num_days_between('2014-01-21', None) |
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), |
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C: zip_with_dates( |
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['NaT'] * num_days_between(None, '2014-01-04') + |
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['2014-01-10'] * num_days_between('2014-01-05', '2014-01-10') + |
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['NaT'] * num_days_between('2014-01-11', '2014-01-14') + |
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['2014-01-20'] * num_days_between('2014-01-15', '2014-01-20') + |
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['NaT'] * num_days_between('2014-01-21', None) |
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), |
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D: zip_with_dates( |
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['NaT'] * num_days_between(None, '2014-01-04') + |
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['2014-01-10'] * num_days_between('2014-01-05', '2014-01-10') + |
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['2014-01-15'] * num_days_between('2014-01-11', '2014-01-15') + |
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['NaT'] * num_days_between('2014-01-16', None) |
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), |
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E: zip_with_dates(['NaT'] * len(dates)), |
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}, index=dates) |
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_expected_previous_announce = pd.DataFrame({ |
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A: zip_with_dates( |
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['NaT'] * num_days_between(None, '2014-01-14') + |
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['2014-01-15'] * num_days_between('2014-01-15', '2014-01-19') + |
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['2014-01-20'] * num_days_between('2014-01-20', None) |
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), |
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B: zip_with_dates( |
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['NaT'] * num_days_between(None, '2014-01-14') + |
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['2014-01-15'] * num_days_between('2014-01-15', '2014-01-19') + |
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['2014-01-20'] * num_days_between('2014-01-20', None) |
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), |
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C: zip_with_dates( |
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['NaT'] * num_days_between(None, '2014-01-09') + |
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['2014-01-10'] * num_days_between('2014-01-10', '2014-01-19') + |
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['2014-01-20'] * num_days_between('2014-01-20', None) |
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), |
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D: zip_with_dates( |
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['NaT'] * num_days_between(None, '2014-01-09') + |
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['2014-01-10'] * num_days_between('2014-01-10', '2014-01-14') + |
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['2014-01-15'] * num_days_between('2014-01-15', None) |
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), |
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E: zip_with_dates(['NaT'] * len(dates)), |
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}, index=dates) |
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_expected_next_busday_offsets = self._compute_busday_offsets( |
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_expected_next_announce |
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) |
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_expected_previous_busday_offsets = self._compute_busday_offsets( |
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_expected_previous_announce |
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) |
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def expected_next_announce(sid): |
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""" |
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Return the expected next announcement dates for ``sid``. |
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""" |
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return _expected_next_announce[sid] |
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def expected_next_busday_offset(sid): |
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""" |
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Return the expected number of days to the next announcement for |
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``sid``. |
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""" |
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return _expected_next_busday_offsets[sid] |
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def expected_previous_announce(sid): |
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""" |
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Return the expected previous announcement dates for ``sid``. |
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""" |
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return _expected_previous_announce[sid] |
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|
244
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def expected_previous_busday_offset(sid): |
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""" |
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Return the expected number of days to the next announcement for |
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``sid``. |
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""" |
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return _expected_previous_busday_offsets[sid] |
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loader = self.loader_type(*self.loader_args(dates)) |
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engine = SimplePipelineEngine(lambda _: loader, dates, self.finder) |
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return ( |
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engine, |
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expected_next_announce, |
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expected_next_busday_offset, |
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expected_previous_announce, |
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expected_previous_busday_offset, |
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) |
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261
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@staticmethod |
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def _compute_busday_offsets(announcement_dates): |
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""" |
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Compute expected business day offsets from a DataFrame of announcement |
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dates. |
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""" |
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# Column-vector of dates on which factor `compute` will be called. |
268
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raw_call_dates = announcement_dates.index.values.astype( |
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'datetime64[D]' |
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)[:, None] |
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|
272
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# 2D array of dates containining expected nexg announcement. |
273
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raw_announce_dates = ( |
274
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announcement_dates.values.astype('datetime64[D]') |
275
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) |
276
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|
277
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# Set NaTs to 0 temporarily because busday_count doesn't support NaT. |
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# We fill these entries with NaNs later. |
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whereNaT = raw_announce_dates == np_NaT |
280
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raw_announce_dates[whereNaT] = make_datetime64D(0) |
281
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|
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|
282
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|
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# The abs call here makes it so that we can use this function to |
283
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# compute offsets for both next and previous earnings (previous |
284
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|
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# earnings offsets come back negative). |
285
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|
|
expected = abs(np.busday_count( |
286
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|
|
raw_call_dates, |
287
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|
|
raw_announce_dates |
288
|
|
|
).astype(float)) |
289
|
|
|
|
290
|
|
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expected[whereNaT] = np.nan |
291
|
|
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return pd.DataFrame( |
292
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|
|
data=expected, |
293
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|
|
columns=announcement_dates.columns, |
294
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|
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index=announcement_dates.index, |
295
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|
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) |
296
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|
|
|
297
|
|
|
@parameterized.expand(gen_calendars()) |
298
|
|
|
def test_compute_earnings(self, dates): |
299
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|
|
|
300
|
|
|
( |
301
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|
|
engine, |
302
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|
|
expected_next, |
303
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|
|
expected_next_busday_offset, |
304
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|
|
expected_previous, |
305
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|
|
expected_previous_busday_offset, |
306
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|
|
) = self.setup(dates) |
307
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|
|
|
308
|
|
|
pipe = Pipeline( |
309
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|
|
columns={ |
310
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|
|
'next': EarningsCalendar.next_announcement.latest, |
311
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|
|
'previous': EarningsCalendar.previous_announcement.latest, |
312
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|
|
'days_to_next': BusinessDaysUntilNextEarnings(), |
313
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|
|
'days_since_prev': BusinessDaysSincePreviousEarnings(), |
314
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|
|
} |
315
|
|
|
) |
316
|
|
|
|
317
|
|
|
result = engine.run_pipeline( |
318
|
|
|
pipe, |
319
|
|
|
start_date=dates[0], |
320
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|
|
end_date=dates[-1], |
321
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|
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) |
322
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|
|
|
323
|
|
|
computed_next = result['next'] |
324
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|
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computed_previous = result['previous'] |
325
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|
|
computed_next_busday_offset = result['days_to_next'] |
326
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|
|
computed_previous_busday_offset = result['days_since_prev'] |
327
|
|
|
|
328
|
|
|
# NaTs in next/prev should correspond to NaNs in offsets. |
329
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|
|
assert_series_equal( |
330
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|
|
computed_next.isnull(), |
331
|
|
|
computed_next_busday_offset.isnull(), |
332
|
|
|
) |
333
|
|
|
assert_series_equal( |
334
|
|
|
computed_previous.isnull(), |
335
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|
|
computed_previous_busday_offset.isnull(), |
336
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|
|
) |
337
|
|
|
|
338
|
|
|
for sid in self.sids: |
339
|
|
|
|
340
|
|
|
assert_series_equal( |
341
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|
|
computed_next.xs(sid, level=1), |
342
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|
|
expected_next(sid), |
343
|
|
|
sid, |
344
|
|
|
) |
345
|
|
|
|
346
|
|
|
assert_series_equal( |
347
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|
|
computed_previous.xs(sid, level=1), |
348
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|
|
expected_previous(sid), |
349
|
|
|
sid, |
350
|
|
|
) |
351
|
|
|
|
352
|
|
|
assert_series_equal( |
353
|
|
|
computed_next_busday_offset.xs(sid, level=1), |
354
|
|
|
expected_next_busday_offset(sid), |
355
|
|
|
sid, |
356
|
|
|
) |
357
|
|
|
|
358
|
|
|
assert_series_equal( |
359
|
|
|
computed_previous_busday_offset.xs(sid, level=1), |
360
|
|
|
expected_previous_busday_offset(sid), |
361
|
|
|
sid, |
362
|
|
|
) |
363
|
|
|
|
364
|
|
|
|
365
|
|
|
class BlazeEarningsCalendarLoaderTestCase(EarningsCalendarLoaderTestCase): |
366
|
|
|
loader_type = BlazeEarningsCalendarLoader |
367
|
|
|
|
368
|
|
|
def loader_args(self, dates): |
369
|
|
|
_, mapping = super( |
370
|
|
|
BlazeEarningsCalendarLoaderTestCase, |
371
|
|
|
self, |
372
|
|
|
).loader_args(dates) |
373
|
|
|
return (bz.Data(pd.concat( |
374
|
|
|
pd.DataFrame({ |
375
|
|
|
ANNOUNCEMENT_FIELD_NAME: earning_dates, |
376
|
|
|
TS_FIELD_NAME: earning_dates.index, |
377
|
|
|
SID_FIELD_NAME: sid, |
378
|
|
|
}) |
379
|
|
|
for sid, earning_dates in iteritems(mapping) |
380
|
|
|
).reset_index(drop=True)),) |
381
|
|
|
|
382
|
|
|
|
383
|
|
|
class BlazeEarningsCalendarLoaderNotInteractiveTestCase( |
384
|
|
|
BlazeEarningsCalendarLoaderTestCase): |
385
|
|
|
"""Test case for passing a non-interactive symbol and a dict of resources. |
386
|
|
|
""" |
387
|
|
|
def loader_args(self, dates): |
388
|
|
|
(bound_expr,) = super( |
389
|
|
|
BlazeEarningsCalendarLoaderNotInteractiveTestCase, |
390
|
|
|
self, |
391
|
|
|
).loader_args(dates) |
392
|
|
|
return swap_resources_into_scope(bound_expr, {}) |
393
|
|
|
|
394
|
|
|
|
395
|
|
|
class EarningsCalendarLoaderInferTimestampTestCase(TestCase): |
396
|
|
|
def test_infer_timestamp(self): |
397
|
|
|
dtx = pd.date_range('2014-01-01', '2014-01-10') |
398
|
|
|
announcement_dates = { |
399
|
|
|
0: dtx, |
400
|
|
|
1: pd.Series(dtx, dtx), |
401
|
|
|
} |
402
|
|
|
loader = EarningsCalendarLoader( |
403
|
|
|
dtx, |
404
|
|
|
announcement_dates, |
405
|
|
|
infer_timestamps=True, |
406
|
|
|
) |
407
|
|
|
self.assertEqual( |
408
|
|
|
loader.announcement_dates.keys(), |
409
|
|
|
announcement_dates.keys(), |
410
|
|
|
) |
411
|
|
|
assert_series_equal( |
412
|
|
|
loader.announcement_dates[0], |
413
|
|
|
pd.Series(index=[dtx[0]] * 10, data=dtx), |
414
|
|
|
) |
415
|
|
|
assert_series_equal( |
416
|
|
|
loader.announcement_dates[1], |
417
|
|
|
announcement_dates[1], |
418
|
|
|
) |
419
|
|
|
|