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""" |
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Mixins classes for use with Filters and Factors. |
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""" |
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from numpy import full_like |
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from zipline.errors import WindowLengthNotPositive |
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from .term import NotSpecified |
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9
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10
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class PositiveWindowLengthMixin(object): |
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""" |
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Validation mixin enforcing that a Term gets a positive WindowLength |
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""" |
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def _validate(self): |
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if not self.windowed: |
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raise WindowLengthNotPositive(window_length=self.window_length) |
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return super(PositiveWindowLengthMixin, self)._validate() |
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18
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19
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20
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class SingleInputMixin(object): |
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""" |
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Validation mixin enforcing that a Term gets a length-1 inputs list. |
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""" |
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def _validate(self): |
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num_inputs = len(self.inputs) |
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if num_inputs != 1: |
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raise ValueError( |
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"{typename} expects only one input, " |
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"but received {num_inputs} instead.".format( |
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typename=type(self).__name__, |
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num_inputs=num_inputs |
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) |
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) |
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return super(SingleInputMixin, self)._validate() |
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class CustomTermMixin(object): |
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""" |
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Mixin for user-defined rolling-window Terms. |
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Implements `_compute` in terms of a user-defined `compute` function, which |
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is mapped over the input windows. |
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Used by CustomFactor, CustomFilter, CustomClassifier, etc. |
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""" |
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def __new__(cls, |
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inputs=NotSpecified, |
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window_length=NotSpecified, |
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dtype=NotSpecified, |
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**kwargs): |
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unexpected_keys = set(kwargs) - set(cls.params) |
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if unexpected_keys: |
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raise TypeError( |
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"{termname} received unexpected keyword " |
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"arguments {unexpected}".format( |
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termname=cls.__name__, |
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unexpected={k: kwargs[k] for k in unexpected_keys}, |
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) |
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) |
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return super(CustomTermMixin, cls).__new__( |
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cls, |
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inputs=inputs, |
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window_length=window_length, |
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dtype=dtype, |
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**kwargs |
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) |
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70
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def compute(self, today, assets, out, *arrays): |
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""" |
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Override this method with a function that writes a value into `out`. |
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""" |
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raise NotImplementedError() |
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76
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def _compute(self, windows, dates, assets, mask): |
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""" |
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Call the user's `compute` function on each window with a pre-built |
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output array. |
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""" |
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# TODO: Make mask available to user's `compute`. |
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compute = self.compute |
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missing_value = self.missing_value |
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params = self.params |
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out = full_like(mask, missing_value, dtype=self.dtype) |
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with self.ctx: |
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# TODO: Consider pre-filtering columns that are all-nan at each |
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# time-step? |
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for idx, date in enumerate(dates): |
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compute( |
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date, |
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assets, |
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out[idx], |
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*(next(w) for w in windows), |
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**params |
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96
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) |
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out[~mask] = missing_value |
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return out |
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99
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100
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def short_repr(self): |
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return type(self).__name__ + '(%d)' % self.window_length |
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quantopian /
zipline
