zipline.pipeline.factors._ExponentialWeightedFactor.from_span() - Code Metrics - Inspection of "Merge pull request #910 from quantopian/new-factor..." - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

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created 2015-12-13 17:15 UTC

from_span() A

↳ Parent: zipline.pipeline.factors._ExponentialWeightedFactor

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cc	3
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loc	21
rs	9.3143

"""
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.
        """
        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.
        """
        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 / center_of_mass)`.  This provides
        behavior equivalent to passing `center_of_mass` to pandas.ewma.
        """
        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, ...

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


class ExponentialWeightedStandardDeviation(_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, ...

    See Also
    --------
    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 = ExponentialWeightedStandardDeviation


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			if span <= 1:
190			raise ValueError(
191			"`span` must be a positive number. %s was passed." % span
192			)
193
194			decay_rate = (1.0 - (2.0 / (1.0 + span)))
195			assert 0.0 < decay_rate <= 1.0
196
197			return cls(
198			inputs=inputs,
199			window_length=window_length,
200			decay_rate=decay_rate,
201			)
202
203			@classmethod
204			@expect_types(halflife=Number)
205			def from_halflife(cls, inputs, window_length, halflife):
206			"""
207			Convenience constructor for passing `decay_rate` in terms of half life.
208
209			Forwards `decay_rate` as `exp(log(.5) / halflife)`. This provides
210			the behavior equivalent to passing `halflife` to pandas.ewma.
211			"""
212			if halflife <= 0:
213			raise ValueError(
214			"`span` must be a positive number. %s was passed." % halflife
215			)
216			decay_rate = exp(log(.5) / halflife)
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			def from_center_of_mass(cls, inputs, window_length, center_of_mass):
227			"""
228			Convenience constructor for passing `decay_rate` in terms of center of
229			mass.
230
231			Forwards `decay_rate` as `1 - (1 / center_of_mass)`. This provides
232			behavior equivalent to passing `center_of_mass` to pandas.ewma.
233			"""
234			return cls(
235			inputs=inputs,
236			window_length=window_length,
237			decay_rate=(1.0 - (1.0 / (1.0 + center_of_mass))),
238			)
239
240
241			class ExponentialWeightedMovingAverage(_ExponentialWeightedFactor):
242			"""
243			Exponentially Weighted Moving Average
244
245			Default Inputs: None
246
247			Default Window Length: None
248
249			Parameters
250			----------
251			inputs : length-1 list/tuple of BoundColumn
252			The expression over which to compute the average.
253			window_length : int > 0
254			Length of the lookback window over which to compute the average.
255			decay_rate : float, 0 < decay_rate <= 1
256			Weighting factor by which to discount past observations.
257
258			When calculating historical averages, rows are multiplied by the
259			sequence::
260
261			decay_rate, decay_rate 2, decay_rate 3, ...
262
263			See Also
264			--------
265			pandas.ewma
266			"""
267			def compute(self, today, assets, out, data, decay_rate):
268			out[:] = average(
269			data,
270			axis=0,
271			weights=self.weights(len(data), decay_rate),
272			)
273
274
275			class ExponentialWeightedStandardDeviation(_ExponentialWeightedFactor):
276			"""
277			Exponentially Weighted Moving Standard Deviation
278
279			Default Inputs: None
280
281			Default Window Length: None
282
283			Parameters
284			----------
285			inputs : length-1 list/tuple of BoundColumn
286			The expression over which to compute the average.
287			window_length : int > 0
288			Length of the lookback window over which to compute the average.
289			decay_rate : float, 0 < decay_rate <= 1
290			Weighting factor by which to discount past observations.
291
292			When calculating historical averages, rows are multiplied by the
293			sequence::
294
295			decay_rate, decay_rate 2, decay_rate 3, ...
296
297			See Also
298			--------
299			pandas.ewmstd
300			"""
301
302			def compute(self, today, assets, out, data, decay_rate):
303			weights = self.weights(len(data), decay_rate)
304
305			mean = average(data, axis=0, weights=weights)
306			variance = average((data - mean) ** 2, axis=0, weights=weights)
307
308			squared_weight_sum = (np_sum(weights) ** 2)
309			bias_correction = (
310			squared_weight_sum / (squared_weight_sum - np_sum(weights ** 2))
311			)
312			out[:] = sqrt(variance * bias_correction)
313
314
315			# Convenience aliases.
316			EWMA = ExponentialWeightedMovingAverage
317			EWMSTD = ExponentialWeightedStandardDeviation
318

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