mirror of
https://github.com/wassname/catalyst.git
synced 2026-08-14 12:10:24 +08:00
Maya Tydykov
11d666daaa
MAINT: add 13d filings to factors init MAINT: rename constant MAINT: add event_date_col field
112 lines
3.5 KiB
Python
112 lines
3.5 KiB
Python
"""
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Tests for the reference loader for 13d filings.
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"""
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import pandas as pd
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from zipline.pipeline.common import(
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DAYS_SINCE_PREV_DISCLOSURE,
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DISCLOSURE_DATE,
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NUM_SHARES,
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PERCENT_SHARES,
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PREVIOUS_NUM_SHARES,
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PREVIOUS_PERCENT_SHARES,
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PREVIOUS_DISCLOSURE_DATE,
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TS_FIELD_NAME,
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)
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from zipline.pipeline.data import _13DFilings
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from zipline.pipeline.factors.events import BusinessDaysSince13DFilingsDate
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from zipline.pipeline.loaders._13d_filings import _13DFilingsLoader
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from zipline.pipeline.loaders.utils import (
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get_values_for_date_ranges,
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zip_with_floats,
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zip_with_dates
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)
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from zipline.testing.fixtures import WithPipelineEventDataLoader
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from zipline.testing.fixtures import ZiplineTestCase
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date_intervals = [[None, '2014-01-04'],
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['2014-01-05', '2014-01-09'],
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['2014-01-10', None]]
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empty_df = pd.DataFrame(
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columns=[NUM_SHARES,
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PERCENT_SHARES,
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DISCLOSURE_DATE,
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TS_FIELD_NAME],
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)
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empty_df[NUM_SHARES] = empty_df[NUM_SHARES].astype('float')
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empty_df[PERCENT_SHARES] = empty_df[PERCENT_SHARES].astype('float')
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empty_df[TS_FIELD_NAME] = empty_df[TS_FIELD_NAME].astype('datetime64[ns]')
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empty_df[DISCLOSURE_DATE] = empty_df[DISCLOSURE_DATE].astype('datetime64[ns]')
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_13d_filngs_cases = [
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pd.DataFrame({
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NUM_SHARES: [1, 15],
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PERCENT_SHARES: [10, 20],
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TS_FIELD_NAME: pd.to_datetime(['2014-01-05', '2014-01-10']),
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DISCLOSURE_DATE: pd.to_datetime(['2014-01-04', '2014-01-09'])
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}),
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empty_df
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]
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def get_expected_previous_values(zip_date_index_with_vals,
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vals,
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date_intervals,
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dates):
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return pd.DataFrame({
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0: get_values_for_date_ranges(zip_date_index_with_vals,
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vals,
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date_intervals,
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dates),
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1: zip_date_index_with_vals(dates, ['NaN'] * len(dates)),
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}, index=dates)
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class _13DFilingsLoaderTestCase(WithPipelineEventDataLoader,
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ZiplineTestCase):
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"""
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Test for _13_filings dataset.
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"""
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pipeline_columns = {
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PREVIOUS_NUM_SHARES:
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_13DFilings.number_shares.latest,
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PREVIOUS_PERCENT_SHARES:
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_13DFilings.percent_shares.latest,
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PREVIOUS_DISCLOSURE_DATE:
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_13DFilings.disclosure_date.latest,
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DAYS_SINCE_PREV_DISCLOSURE:
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BusinessDaysSince13DFilingsDate(),
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}
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@classmethod
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def get_sids(cls):
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return range(2)
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@classmethod
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def get_dataset(cls):
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return {sid: frame
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for sid, frame
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in enumerate(_13d_filngs_cases)}
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loader_type = _13DFilingsLoader
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def setup(self, dates):
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cols = {}
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cols[
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PREVIOUS_DISCLOSURE_DATE
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] = get_expected_previous_values(zip_with_dates,
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['NaT', '2014-01-04', '2014-01-09'],
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date_intervals, dates)
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cols[PREVIOUS_NUM_SHARES] = get_expected_previous_values(
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zip_with_floats, ['NaN', 1, 15], date_intervals, dates
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)
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cols[PREVIOUS_PERCENT_SHARES] = get_expected_previous_values(
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zip_with_floats, ['NaN', 10, 20], date_intervals, dates
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)
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cols[DAYS_SINCE_PREV_DISCLOSURE] = self._compute_busday_offsets(
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cols[PREVIOUS_DISCLOSURE_DATE]
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)
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return cols
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