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""" |
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Factors describing information about event data (e.g. earnings |
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announcements, acquisitions, dividends, etc.). |
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""" |
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from numpy import newaxis |
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from zipline.pipeline.data.earnings import EarningsCalendar |
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from zipline.utils.numpy_utils import ( |
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np_NaT, |
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busday_count_mask_NaT, |
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datetime64D_dtype, |
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float64_dtype, |
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) |
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from .factor import Factor |
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class BusinessDaysUntilNextEarnings(Factor): |
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""" |
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Factor returning the number of **business days** (not trading days!) until |
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the next known earnings date for each asset. |
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This doesn't use trading days because the trading calendar includes |
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information that may not have been available to the algorithm at the time |
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when `compute` is called. |
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For example, the NYSE closings September 11th 2001, would not have been |
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known to the algorithm on September 10th. |
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Assets that announced or will announce earnings today will produce a value |
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of 0.0. Assets that will announce earnings on the next upcoming business |
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day will produce a value of 1.0. |
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Assets for which `EarningsCalendar.next_announcement` is `NaT` will produce |
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a value of `NaN`. |
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See Also |
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-------- |
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zipline.pipeline.factors.BusinessDaysSincePreviousEarnings |
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""" |
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inputs = [EarningsCalendar.next_announcement] |
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window_length = 0 |
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dtype = float64_dtype |
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def _compute(self, arrays, dates, assets, mask): |
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# Coerce from [ns] to [D] for numpy busday_count. |
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announce_dates = arrays[0].astype(datetime64D_dtype) |
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# Set masked values to NaT. |
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announce_dates[~mask] = np_NaT |
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# Convert row labels into a column vector for broadcasted comparison. |
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reference_dates = dates.values.astype(datetime64D_dtype)[:, newaxis] |
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return busday_count_mask_NaT(reference_dates, announce_dates) |
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class BusinessDaysSincePreviousEarnings(Factor): |
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""" |
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Factor returning the number of **business days** (not trading days!) since |
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the most recent earnings date for each asset. |
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This doesn't use trading days for symmetry with |
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BusinessDaysUntilNextEarnings. |
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Assets which announced or will announce earnings today will produce a value |
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of 0.0. Assets that announced earnings on the previous business day will |
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produce a value of 1.0. |
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Assets for which `EarningsCalendar.previous_announcement` is `NaT` will |
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produce a value of `NaN`. |
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See Also |
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-------- |
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zipline.pipeline.factors.BusinessDaysUntilNextEarnings |
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""" |
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inputs = [EarningsCalendar.previous_announcement] |
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window_length = 0 |
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dtype = float64_dtype |
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def _compute(self, arrays, dates, assets, mask): |
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# Coerce from [ns] to [D] for numpy busday_count. |
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announce_dates = arrays[0].astype(datetime64D_dtype) |
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# Set masked values to NaT. |
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announce_dates[~mask] = np_NaT |
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# Convert row labels into a column vector for broadcasted comparison. |
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reference_dates = dates.values.astype(datetime64D_dtype)[:, newaxis] |
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return busday_count_mask_NaT(announce_dates, reference_dates) |
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quantopian /
zipline
