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import datetime |
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from datashape import istabular |
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import pandas as pd |
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from toolz import valmap |
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from .core import ( |
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TS_FIELD_NAME, |
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SID_FIELD_NAME, |
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bind_expression_to_resources, |
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ffill_query_in_range, |
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) |
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from zipline.pipeline.data import EarningsCalendar |
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from zipline.pipeline.loaders.base import PipelineLoader |
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from zipline.pipeline.loaders.earnings import EarningsCalendarLoader |
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from zipline.pipeline.loaders.utils import ( |
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normalize_data_query_time, |
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normalize_timestamp_to_query_time, |
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) |
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from zipline.utils.input_validation import ensure_timezone |
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from zipline.utils.preprocess import preprocess |
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ANNOUNCEMENT_FIELD_NAME = 'announcement_date' |
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class BlazeEarningsCalendarLoader(PipelineLoader): |
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"""A pipeline loader for the ``EarningsCalendar`` dataset that loads |
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data from a blaze expression. |
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Parameters |
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---------- |
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expr : Expr |
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The expression representing the data to load. |
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resources : dict, optional |
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Mapping from the atomic terms of ``expr`` to actual data resources. |
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odo_kwargs : dict, optional |
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Extra keyword arguments to pass to odo when executing the expression. |
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data_query_time : time, optional |
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The time to use for the data query cutoff. |
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data_query_tz : tzinfo or str |
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The timezeone to use for the data query cutoff. |
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Notes |
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----- |
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The expression should have a tabular dshape of:: |
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Dim * {{ |
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{SID_FIELD_NAME}: int64, |
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{TS_FIELD_NAME}: datetime, |
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{ANNOUNCEMENT_FIELD_NAME}: ?datetime, |
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}} |
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Where each row of the table is a record including the sid to identify the |
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company, the timestamp where we learned about the announcement, and the |
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date when the earnings will be announced. |
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If the '{TS_FIELD_NAME}' field is not included it is assumed that we |
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start the backtest with knowledge of all announcements. |
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""" |
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__doc__ = __doc__.format( |
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TS_FIELD_NAME=TS_FIELD_NAME, |
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SID_FIELD_NAME=SID_FIELD_NAME, |
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ANNOUNCEMENT_FIELD_NAME=ANNOUNCEMENT_FIELD_NAME, |
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) |
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_expected_fields = frozenset({ |
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TS_FIELD_NAME, |
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SID_FIELD_NAME, |
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ANNOUNCEMENT_FIELD_NAME, |
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}) |
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@preprocess(data_query_tz=ensure_timezone) |
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def __init__(self, |
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expr, |
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resources=None, |
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odo_kwargs=None, |
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data_query_time=datetime.time(0), |
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data_query_tz='utc', |
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dataset=EarningsCalendar): |
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dshape = expr.dshape |
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if not istabular(dshape): |
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raise ValueError( |
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'expression dshape must be tabular, got: %s' % dshape, |
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) |
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expected_fields = self._expected_fields |
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self._expr = bind_expression_to_resources( |
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expr[list(expected_fields)], |
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resources, |
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) |
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self._odo_kwargs = odo_kwargs if odo_kwargs is not None else {} |
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self._dataset = dataset |
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self._data_query_time = data_query_time |
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self._data_query_tz = data_query_tz |
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def load_adjusted_array(self, columns, dates, assets, mask): |
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data_query_time = self._data_query_time |
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data_query_tz = self._data_query_tz |
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raw = ffill_query_in_range( |
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self._expr, |
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normalize_data_query_time( |
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dates[0], |
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data_query_time, |
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data_query_tz, |
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), |
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normalize_data_query_time( |
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dates[-1], |
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data_query_time, |
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data_query_tz, |
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), |
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self._odo_kwargs, |
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) |
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sids = raw.loc[:, SID_FIELD_NAME] |
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raw.drop( |
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sids[~sids.isin(assets)].index, |
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inplace=True |
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) |
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normalize_timestamp_to_query_time( |
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raw, |
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data_query_time, |
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data_query_tz, |
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inplace=True, |
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ts_field=TS_FIELD_NAME, |
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) |
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gb = raw.groupby(SID_FIELD_NAME) |
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def mkseries(idx, raw_loc=raw.loc): |
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vs = raw_loc[ |
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idx, [TS_FIELD_NAME, ANNOUNCEMENT_FIELD_NAME] |
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].values |
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return pd.Series( |
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index=pd.DatetimeIndex(vs[:, 0]), |
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data=vs[:, 1], |
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) |
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return EarningsCalendarLoader( |
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dates, |
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valmap(mkseries, gb.groups), |
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dataset=self._dataset, |
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).load_adjusted_array(columns, dates, assets, mask) |
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quantopian /
zipline
