zipline.pipeline.factors.ExponentialWeightedStandardDeviation.compute() - Code Metrics - Inspection of "Merge pull request #922 from quantopian/log-noise" - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

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ation.compute() A

↳ Parent: zipline.pipeline.factors.ExponentialWeightedMovingStdDev

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dl	0
loc	11
rs	9.4286

"""
Technical Analysis Factors
--------------------------
"""
from bottleneck import (
    nanargmax,
    nanmax,
    nanmean,
    nansum,
)
from numbers import Number
from numpy import (
    abs,
    arange,
    average,
    clip,
    diff,
    exp,
    fmax,
    full,
    inf,
    isnan,
    log,
    NINF,
    sqrt,
    sum as np_sum,
)
from numexpr import evaluate

from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.mixins import SingleInputMixin
from zipline.utils.control_flow import ignore_nanwarnings
from zipline.utils.input_validation import expect_types
from .factor import CustomFactor


class Returns(CustomFactor):
    """
    Calculates the percent change in close price over the given window_length.

    **Default Inputs**: [USEquityPricing.close]
    """
    inputs = [USEquityPricing.close]

    def compute(self, today, assets, out, close):
        out[:] = (close[-1] - close[0]) / close[0]


class RSI(CustomFactor, SingleInputMixin):
    """
    Relative Strength Index

    **Default Inputs**: [USEquityPricing.close]

    **Default Window Length**: 15
    """
    window_length = 15
    inputs = (USEquityPricing.close,)

    def compute(self, today, assets, out, closes):
        diffs = diff(closes, axis=0)
        ups = nanmean(clip(diffs, 0, inf), axis=0)
        downs = abs(nanmean(clip(diffs, -inf, 0), axis=0))
        return evaluate(
            "100 - (100 / (1 + (ups / downs)))",
            local_dict={'ups': ups, 'downs': downs},
            global_dict={},
            out=out,
        )


class SimpleMovingAverage(CustomFactor, SingleInputMixin):
    """
    Average Value of an arbitrary column

    **Default Inputs**: None

    **Default Window Length**: None
    """
    # numpy's nan functions throw warnings when passed an array containing only
    # nans, but they still returns the desired value (nan), so we ignore the
    # warning.
    ctx = ignore_nanwarnings()

    def compute(self, today, assets, out, data):
        out[:] = nanmean(data, axis=0)


class WeightedAverageValue(CustomFactor):
    """
    Helper for VWAP-like computations.

    **Default Inputs:** None

    **Default Window Length:** None
    """
    def compute(self, today, assets, out, base, weight):
        out[:] = nansum(base * weight, axis=0) / nansum(weight, axis=0)


class VWAP(WeightedAverageValue):
    """
    Volume Weighted Average Price

    **Default Inputs:** [USEquityPricing.close, USEquityPricing.volume]

    **Default Window Length:** None
    """
    inputs = (USEquityPricing.close, USEquityPricing.volume)


class MaxDrawdown(CustomFactor, SingleInputMixin):
    """
    Max Drawdown

    **Default Inputs:** None

    **Default Window Length:** None
    """
    ctx = ignore_nanwarnings()

    def compute(self, today, assets, out, data):
        drawdowns = fmax.accumulate(data, axis=0) - data
        drawdowns[isnan(drawdowns)] = NINF
        drawdown_ends = nanargmax(drawdowns, axis=0)

        # TODO: Accelerate this loop in Cython or Numba.
        for i, end in enumerate(drawdown_ends):
            peak = nanmax(data[:end + 1, i])
            out[i] = (peak - data[end, i]) / data[end, i]


def DollarVolume():
    """
    Returns a Factor computing the product of most recent close price and
    volume.
    """
    return USEquityPricing.close.latest * USEquityPricing.volume.latest


class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
    """
    Base class for factors implementing exponential-weighted operations.

    **Default Inputs:** None

    **Default Window Length:** None

    Parameters
    ----------
    inputs : length-1 list or tuple of BoundColumn
        The expression over which to compute the average.
    window_length : int > 0
        Length of the lookback window over which to compute the average.
    decay_rate : float, 0 < decay_rate <= 1
        Weighting factor by which to discount past observations.

        When calculating historical averages, rows are multiplied by the
        sequence::

            decay_rate, decay_rate ** 2, decay_rate ** 3, ...

    Methods
    -------
    weights
    from_span
    from_halflife
    from_center_of_mass
    """
    params = ('decay_rate',)

    @staticmethod
    def weights(length, decay_rate):
        """
        Return weighting vector for an exponential moving statistic on `length`
        rows with a decay rate of `decay_rate`.
        """
        return full(length, decay_rate) ** arange(length + 1, 1, -1)

    @classmethod
    @expect_types(span=Number)
    def from_span(cls, inputs, window_length, span):
        """
        Convenience constructor for passing `decay_rate` in terms of `span`.

        Forwards `decay_rate` as `1 - (2.0 / (1 + span))`.  This provides the
        behavior equivalent to passing `span` to pandas.ewma.

        Example
        -------
        .. code-block:: python

            # Equivalent to:
            # my_ewma = EWMA(
            #    inputs=[USEquityPricing.close],
            #    window_length=30,
            #    decay_rate=(1 - (2.0 / (1 + 15.0))),
            # )
            my_ewma = EWMA.from_span(
                inputs=[USEquityPricing.close],
                window_length=30,
                span=15,
            )

        Note
        ----
        This classmethod is provided by both
        :class:`ExponentialWeightedMovingAverage` and
        :class:`ExponentialWeightedMovingStdDev`.
        """
        if span <= 1:
            raise ValueError(
                "`span` must be a positive number. %s was passed." % span
            )

        decay_rate = (1.0 - (2.0 / (1.0 + span)))
        assert 0.0 < decay_rate <= 1.0

        return cls(
            inputs=inputs,
            window_length=window_length,
            decay_rate=decay_rate,
        )

    @classmethod
    @expect_types(halflife=Number)
    def from_halflife(cls, inputs, window_length, halflife):
        """
        Convenience constructor for passing ``decay_rate`` in terms of half
        life.

        Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``.  This provides
        the behavior equivalent to passing `halflife` to pandas.ewma.

        Example
        -------
        .. code-block:: python

            # Equivalent to:
            # my_ewma = EWMA(
            #    inputs=[USEquityPricing.close],
            #    window_length=30,
            #    decay_rate=np.exp(np.log(0.5) / 15),
            # )
            my_ewma = EWMA.from_halflife(
                inputs=[USEquityPricing.close],
                window_length=30,
                halflife=15,
            )

        Note
        ----
        This classmethod is provided by both
        :class:`ExponentialWeightedMovingAverage` and
        :class:`ExponentialWeightedMovingStdDev`.
        """
        if halflife <= 0:
            raise ValueError(
                "`span` must be a positive number. %s was passed." % halflife
            )
        decay_rate = exp(log(.5) / halflife)
        assert 0.0 < decay_rate <= 1.0

        return cls(
            inputs=inputs,
            window_length=window_length,
            decay_rate=decay_rate,
        )

    @classmethod
    def from_center_of_mass(cls, inputs, window_length, center_of_mass):
        """
        Convenience constructor for passing `decay_rate` in terms of center of
        mass.

        Forwards `decay_rate` as `1 - (1 / 1 + center_of_mass)`.  This provides
        behavior equivalent to passing `center_of_mass` to pandas.ewma.

        Example
        -------
        .. code-block:: python

            # Equivalent to:
            # my_ewma = EWMA(
            #    inputs=[USEquityPricing.close],
            #    window_length=30,
            #    decay_rate=(1 - (1 / 15.0)),
            # )
            my_ewma = EWMA.from_center_of_mass(
                inputs=[USEquityPricing.close],
                window_length=30,
                center_of_mass=15,
            )

        Note
        ----
        This classmethod is provided by both
        :class:`ExponentialWeightedMovingAverage` and
        :class:`ExponentialWeightedMovingStdDev`.
        """
        return cls(
            inputs=inputs,
            window_length=window_length,
            decay_rate=(1.0 - (1.0 / (1.0 + center_of_mass))),
        )


class ExponentialWeightedMovingAverage(_ExponentialWeightedFactor):
    """
    Exponentially Weighted Moving Average

    **Default Inputs:** None

    **Default Window Length:** None

    Parameters
    ----------
    inputs : length-1 list/tuple of BoundColumn
        The expression over which to compute the average.
    window_length : int > 0
        Length of the lookback window over which to compute the average.
    decay_rate : float, 0 < decay_rate <= 1
        Weighting factor by which to discount past observations.

        When calculating historical averages, rows are multiplied by the
        sequence::

            decay_rate, decay_rate ** 2, decay_rate ** 3, ...

    Notes
    -----
    - This class can also be imported under the name ``EWMA``.

    See Also
    --------
    :func:`pandas.ewma`
    """
    def compute(self, today, assets, out, data, decay_rate):
        out[:] = average(
            data,
            axis=0,
            weights=self.weights(len(data), decay_rate),
        )


class ExponentialWeightedMovingStdDev(_ExponentialWeightedFactor):
    """
    Exponentially Weighted Moving Standard Deviation

    **Default Inputs:** None

    **Default Window Length:** None

    Parameters
    ----------
    inputs : length-1 list/tuple of BoundColumn
        The expression over which to compute the average.
    window_length : int > 0
        Length of the lookback window over which to compute the average.
    decay_rate : float, 0 < decay_rate <= 1
        Weighting factor by which to discount past observations.

        When calculating historical averages, rows are multiplied by the
        sequence::

            decay_rate, decay_rate ** 2, decay_rate ** 3, ...

    Notes
    -----
    - This class can also be imported under the name ``EWMSTD``.

    See Also
    --------
    :func:`pandas.ewmstd`
    """

    def compute(self, today, assets, out, data, decay_rate):
        weights = self.weights(len(data), decay_rate)

        mean = average(data, axis=0, weights=weights)
        variance = average((data - mean) ** 2, axis=0, weights=weights)

        squared_weight_sum = (np_sum(weights) ** 2)
        bias_correction = (
            squared_weight_sum / (squared_weight_sum - np_sum(weights ** 2))
        )
        out[:] = sqrt(variance * bias_correction)


# Convenience aliases.
EWMA = ExponentialWeightedMovingAverage
EWMSTD = ExponentialWeightedMovingStdDev


1			"""
2			Technical Analysis Factors
3			--------------------------
4			"""
5			from bottleneck import (
6			nanargmax,
7			nanmax,
8			nanmean,
9			nansum,
10			)
11			from numbers import Number
12			from numpy import (
13			abs,
14			arange,
15			average,
16			clip,
17			diff,
18			exp,
19			fmax,
20			full,
21			inf,
22			isnan,
23			log,
24			NINF,
25			sqrt,
26			sum as np_sum,
27			)
28			from numexpr import evaluate
29
30			from zipline.pipeline.data import USEquityPricing
31			from zipline.pipeline.mixins import SingleInputMixin
32			from zipline.utils.control_flow import ignore_nanwarnings
33			from zipline.utils.input_validation import expect_types
34			from .factor import CustomFactor
35
36
37			class Returns(CustomFactor):
38			"""
39			Calculates the percent change in close price over the given window_length.
40
41			Default Inputs: [USEquityPricing.close]
42			"""
43			inputs = [USEquityPricing.close]
44
45			def compute(self, today, assets, out, close):
46			out[:] = (close[-1] - close[0]) / close[0]
47
48
49			class RSI(CustomFactor, SingleInputMixin):
50			"""
51			Relative Strength Index
52
53			Default Inputs: [USEquityPricing.close]
54
55			Default Window Length: 15
56			"""
57			window_length = 15
58			inputs = (USEquityPricing.close,)
59
60			def compute(self, today, assets, out, closes):
61			diffs = diff(closes, axis=0)
62			ups = nanmean(clip(diffs, 0, inf), axis=0)
63			downs = abs(nanmean(clip(diffs, -inf, 0), axis=0))
64			return evaluate(
65			"100 - (100 / (1 + (ups / downs)))",
66			local_dict={'ups': ups, 'downs': downs},
67			global_dict={},
68			out=out,
69			)
70
71
72			class SimpleMovingAverage(CustomFactor, SingleInputMixin):
73			"""
74			Average Value of an arbitrary column
75
76			Default Inputs: None
77
78			Default Window Length: None
79			"""
80			# numpy's nan functions throw warnings when passed an array containing only
81			# nans, but they still returns the desired value (nan), so we ignore the
82			# warning.
83			ctx = ignore_nanwarnings()
84
85			def compute(self, today, assets, out, data):
86			out[:] = nanmean(data, axis=0)
87
88
89			class WeightedAverageValue(CustomFactor):
90			"""
91			Helper for VWAP-like computations.
92
93			Default Inputs: None
94
95			Default Window Length: None
96			"""
97			def compute(self, today, assets, out, base, weight):
98			out[:] = nansum(base * weight, axis=0) / nansum(weight, axis=0)
99
100
101			class VWAP(WeightedAverageValue):
102			"""
103			Volume Weighted Average Price
104
105			Default Inputs: [USEquityPricing.close, USEquityPricing.volume]
106
107			Default Window Length: None
108			"""
109			inputs = (USEquityPricing.close, USEquityPricing.volume)
110
111
112			class MaxDrawdown(CustomFactor, SingleInputMixin):
113			"""
114			Max Drawdown
115
116			Default Inputs: None
117
118			Default Window Length: None
119			"""
120			ctx = ignore_nanwarnings()
121
122			def compute(self, today, assets, out, data):
123			drawdowns = fmax.accumulate(data, axis=0) - data
124			drawdowns[isnan(drawdowns)] = NINF
125			drawdown_ends = nanargmax(drawdowns, axis=0)
126
127			# TODO: Accelerate this loop in Cython or Numba.
128			for i, end in enumerate(drawdown_ends):
129			peak = nanmax(data[:end + 1, i])
130			out[i] = (peak - data[end, i]) / data[end, i]
131
132
133			def DollarVolume():
134			"""
135			Returns a Factor computing the product of most recent close price and
136			volume.
137			"""
138			return USEquityPricing.close.latest * USEquityPricing.volume.latest
139
140
141			class _ExponentialWeightedFactor(SingleInputMixin, CustomFactor):
142			"""
143			Base class for factors implementing exponential-weighted operations.
144
145			Default Inputs: None
146
147			Default Window Length: None
148
149			Parameters
150			----------
151			inputs : length-1 list or tuple of BoundColumn
152			The expression over which to compute the average.
153			window_length : int > 0
154			Length of the lookback window over which to compute the average.
155			decay_rate : float, 0 < decay_rate <= 1
156			Weighting factor by which to discount past observations.
157
158			When calculating historical averages, rows are multiplied by the
159			sequence::
160
161			decay_rate, decay_rate 2, decay_rate 3, ...
162
163			Methods
164			-------
165			weights
166			from_span
167			from_halflife
168			from_center_of_mass
169			"""
170			params = ('decay_rate',)
171
172			@staticmethod
173			def weights(length, decay_rate):
174			"""
175			Return weighting vector for an exponential moving statistic on `length`
176			rows with a decay rate of `decay_rate`.
177			"""
178			return full(length, decay_rate) ** arange(length + 1, 1, -1)
179
180			@classmethod
181			@expect_types(span=Number)
182			def from_span(cls, inputs, window_length, span):
183			"""
184			Convenience constructor for passing `decay_rate` in terms of `span`.
185
186			Forwards `decay_rate` as `1 - (2.0 / (1 + span))`. This provides the
187			behavior equivalent to passing `span` to pandas.ewma.
188
189			Example
190			-------
191			.. code-block:: python
192
193			# Equivalent to:
194			# my_ewma = EWMA(
195			# inputs=[USEquityPricing.close],
196			# window_length=30,
197			# decay_rate=(1 - (2.0 / (1 + 15.0))),
198			# )
199			my_ewma = EWMA.from_span(
200			inputs=[USEquityPricing.close],
201			window_length=30,
202			span=15,
203			)
204
205			Note
206			----
207			This classmethod is provided by both
208			:class:`ExponentialWeightedMovingAverage` and
209			:class:`ExponentialWeightedMovingStdDev`.
210			"""
211			if span <= 1:
212			raise ValueError(
213			"`span` must be a positive number. %s was passed." % span
214			)
215
216			decay_rate = (1.0 - (2.0 / (1.0 + span)))
217			assert 0.0 < decay_rate <= 1.0
218
219			return cls(
220			inputs=inputs,
221			window_length=window_length,
222			decay_rate=decay_rate,
223			)
224
225			@classmethod
226			@expect_types(halflife=Number)
227			def from_halflife(cls, inputs, window_length, halflife):
228			"""
229			Convenience constructor for passing ``decay_rate`` in terms of half
230			life.
231
232			Forwards ``decay_rate`` as ``exp(log(.5) / halflife)``. This provides
233			the behavior equivalent to passing `halflife` to pandas.ewma.
234
235			Example
236			-------
237			.. code-block:: python
238
239			# Equivalent to:
240			# my_ewma = EWMA(
241			# inputs=[USEquityPricing.close],
242			# window_length=30,
243			# decay_rate=np.exp(np.log(0.5) / 15),
244			# )
245			my_ewma = EWMA.from_halflife(
246			inputs=[USEquityPricing.close],
247			window_length=30,
248			halflife=15,
249			)
250
251			Note
252			----
253			This classmethod is provided by both
254			:class:`ExponentialWeightedMovingAverage` and
255			:class:`ExponentialWeightedMovingStdDev`.
256			"""
257			if halflife <= 0:
258			raise ValueError(
259			"`span` must be a positive number. %s was passed." % halflife
260			)
261			decay_rate = exp(log(.5) / halflife)
262			assert 0.0 < decay_rate <= 1.0
263
264			return cls(
265			inputs=inputs,
266			window_length=window_length,
267			decay_rate=decay_rate,
268			)
269
270			@classmethod
271			def from_center_of_mass(cls, inputs, window_length, center_of_mass):
272			"""
273			Convenience constructor for passing `decay_rate` in terms of center of
274			mass.
275
276			Forwards `decay_rate` as `1 - (1 / 1 + center_of_mass)`. This provides
277			behavior equivalent to passing `center_of_mass` to pandas.ewma.
278
279			Example
280			-------
281			.. code-block:: python
282
283			# Equivalent to:
284			# my_ewma = EWMA(
285			# inputs=[USEquityPricing.close],
286			# window_length=30,
287			# decay_rate=(1 - (1 / 15.0)),
288			# )
289			my_ewma = EWMA.from_center_of_mass(
290			inputs=[USEquityPricing.close],
291			window_length=30,
292			center_of_mass=15,
293			)
294
295			Note
296			----
297			This classmethod is provided by both
298			:class:`ExponentialWeightedMovingAverage` and
299			:class:`ExponentialWeightedMovingStdDev`.
300			"""
301			return cls(
302			inputs=inputs,
303			window_length=window_length,
304			decay_rate=(1.0 - (1.0 / (1.0 + center_of_mass))),
305			)
306
307
308			class ExponentialWeightedMovingAverage(_ExponentialWeightedFactor):
309			"""
310			Exponentially Weighted Moving Average
311
312			Default Inputs: None
313
314			Default Window Length: None
315
316			Parameters
317			----------
318			inputs : length-1 list/tuple of BoundColumn
319			The expression over which to compute the average.
320			window_length : int > 0
321			Length of the lookback window over which to compute the average.
322			decay_rate : float, 0 < decay_rate <= 1
323			Weighting factor by which to discount past observations.
324
325			When calculating historical averages, rows are multiplied by the
326			sequence::
327
328			decay_rate, decay_rate 2, decay_rate 3, ...
329
330			Notes
331			-----
332			- This class can also be imported under the name ``EWMA``.
333
334			See Also
335			--------
336			:func:`pandas.ewma`
337			"""
338			def compute(self, today, assets, out, data, decay_rate):
339			out[:] = average(
340			data,
341			axis=0,
342			weights=self.weights(len(data), decay_rate),
343			)
344
345
346			class ExponentialWeightedMovingStdDev(_ExponentialWeightedFactor):
347			"""
348			Exponentially Weighted Moving Standard Deviation
349
350			Default Inputs: None
351
352			Default Window Length: None
353
354			Parameters
355			----------
356			inputs : length-1 list/tuple of BoundColumn
357			The expression over which to compute the average.
358			window_length : int > 0
359			Length of the lookback window over which to compute the average.
360			decay_rate : float, 0 < decay_rate <= 1
361			Weighting factor by which to discount past observations.
362
363			When calculating historical averages, rows are multiplied by the
364			sequence::
365
366			decay_rate, decay_rate 2, decay_rate 3, ...
367
368			Notes
369			-----
370			- This class can also be imported under the name ``EWMSTD``.
371
372			See Also
373			--------
374			:func:`pandas.ewmstd`
375			"""
376
377			def compute(self, today, assets, out, data, decay_rate):
378			weights = self.weights(len(data), decay_rate)
379
380			mean = average(data, axis=0, weights=weights)
381			variance = average((data - mean) ** 2, axis=0, weights=weights)
382
383			squared_weight_sum = (np_sum(weights) ** 2)
384			bias_correction = (
385			squared_weight_sum / (squared_weight_sum - np_sum(weights ** 2))
386			)
387			out[:] = sqrt(variance * bias_correction)
388
389
390			# Convenience aliases.
391			EWMA = ExponentialWeightedMovingAverage
392			EWMSTD = ExponentialWeightedMovingStdDev
393

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ation.compute() A

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