zipline.pipeline.factors.binary_operator() - Code Metrics - Inspection of "Merge pull request #940 from quantopian/generalize..." - quantopian/zipline - Measure and Improve Code Quality continuously with Scrutinizer

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by Joe

created 2016-01-08 22:01 UTC

zipline.pipeline.factors.binary_operator() B

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How to fix Long Method

"""
factor.py
"""
from operator import attrgetter
from numbers import Number

from numpy import float64, inf
from toolz import curry

from zipline.errors import (
    UnknownRankMethod,
    UnsupportedDataType,
)
from zipline.lib.rank import masked_rankdata_2d
from zipline.pipeline.mixins import (
    CustomTermMixin,
    PositiveWindowLengthMixin,
    SingleInputMixin,
)
from zipline.pipeline.term import CompositeTerm, NotSpecified
from zipline.pipeline.expression import (
    BadBinaryOperator,
    COMPARISONS,
    is_comparison,
    MATH_BINOPS,
    method_name_for_op,
    NumericalExpression,
    NUMEXPR_MATH_FUNCS,
    UNARY_OPS,
    unary_op_name,
)
from zipline.pipeline.filters import (
    NumExprFilter,
    PercentileFilter,
)
from zipline.utils.control_flow import nullctx
from zipline.utils.numpy_utils import (
    bool_dtype,
    datetime64ns_dtype,
    float64_dtype,
)
from zipline.utils.preprocess import preprocess


_RANK_METHODS = frozenset(['average', 'min', 'max', 'dense', 'ordinal'])


def numbers_to_float64(func, argname, argvalue):
    """
    Preprocessor for converting numerical inputs into floats.

    This is used in the binary operator constructors for Factor so that
    `2 + Factor()` has the same behavior as `2.0 + Factor()`.
    """
    if isinstance(argvalue, Number):
        return float64(argvalue)
    return argvalue


@curry
def set_attribute(name, value):
    """
    Decorator factory for setting attributes on a function.

    Doesn't change the behavior of the wrapped function.

    Usage
    -----
    >>> @set_attribute('__name__', 'foo')
    ... def bar():
    ...     return 3
    ...
    >>> bar()
    3
    >>> bar.__name__
    'foo'
    """
    def decorator(f):
        setattr(f, name, value)
        return f
    return decorator


# Decorators for setting the __name__ and __doc__ properties of a decorated
# function.
# Example:
with_name = set_attribute('__name__')
with_doc = set_attribute('__doc__')


def binop_return_type(op):
    if is_comparison(op):
        return NumExprFilter
    else:
        return NumExprFactor


def binop_return_dtype(op, left, right):
    """
    Compute the expected return dtype for the given binary operator.

    Parameters
    ----------
    op : str
        Operator symbol, (e.g. '+', '-', ...).
    left : numpy.dtype
        Dtype of left hand side.
    right : numpy.dtype
        Dtype of right hand side.

    Returns
    -------
    outdtype : numpy.dtype
        The dtype of the result of `left <op> right`.
    """
    if is_comparison(op):
        if left != right:
            raise TypeError(
                "Don't know how to compute {left} {op} {right}.\n"
                "Comparisons are only supported between Factors of equal "
                "dtypes.".format(left=left, op=op, right=right)
            )
        return bool_dtype

    elif left != float64_dtype or right != float64_dtype:
        raise TypeError(
            "Don't know how to compute {left} {op} {right}.\n"
            "Arithmetic operators are only supported on Factors of "
            "dtype 'float64'.".format(
                left=left.name,
                op=op,
                right=right.name,
            )
        )
    return float64_dtype


def binary_operator(op):
    """
    Factory function for making binary operator methods on a Factor subclass.

    Returns a function, "binary_operator" suitable for implementing functions
    like __add__.
    """
    # When combining a Factor with a NumericalExpression, we use this
    # attrgetter instance to defer to the commuted implementation of the
    # NumericalExpression operator.
    commuted_method_getter = attrgetter(method_name_for_op(op, commute=True))

    @preprocess(other=numbers_to_float64)
    @with_doc("Binary Operator: '%s'" % op)
    @with_name(method_name_for_op(op))
    def binary_operator(self, other):
        # This can't be hoisted up a scope because the types returned by
        # binop_return_type aren't defined when the top-level function is
        # invoked in the class body of Factor.
        return_type = binop_return_type(op)
        if isinstance(self, NumExprFactor):
            self_expr, other_expr, new_inputs = self.build_binary_op(
                op, other,
            )
            return return_type(
                "({left}) {op} ({right})".format(
                    left=self_expr,
                    op=op,
                    right=other_expr,
                ),
                new_inputs,
                dtype=binop_return_dtype(op, self.dtype, other.dtype),
            )
        elif isinstance(other, NumExprFactor):
            # NumericalExpression overrides ops to correctly handle merging of
            # inputs.  Look up and call the appropriate reflected operator with
            # ourself as the input.
            return commuted_method_getter(other)(self)
        elif isinstance(other, Factor):
            if self is other:
                return return_type(
                    "x_0 {op} x_0".format(op=op),
                    (self,),
                    dtype=binop_return_dtype(op, self.dtype, other.dtype),
                )
            return return_type(
                "x_0 {op} x_1".format(op=op),
                (self, other),
                dtype=binop_return_dtype(op, self.dtype, other.dtype),
            )
        elif isinstance(other, Number):
            return return_type(
                "x_0 {op} ({constant})".format(op=op, constant=other),
                binds=(self,),
                # Interpret numeric literals as floats.
                dtype=binop_return_dtype(op, self.dtype, other.dtype)
            )
        raise BadBinaryOperator(op, self, other)

    return binary_operator


def reflected_binary_operator(op):
    """
    Factory function for making binary operator methods on a Factor.

    Returns a function, "reflected_binary_operator" suitable for implementing
    functions like __radd__.
    """
    assert not is_comparison(op)

    @preprocess(other=numbers_to_float64)
    @with_name(method_name_for_op(op, commute=True))
    def reflected_binary_operator(self, other):

        if isinstance(self, NumericalExpression):
            self_expr, other_expr, new_inputs = self.build_binary_op(
                op, other
            )
            return NumExprFactor(
                "({left}) {op} ({right})".format(
                    left=other_expr,
                    right=self_expr,
                    op=op,
                ),
                new_inputs,
                dtype=binop_return_dtype(op, other.dtype, self.dtype)
            )

        # Only have to handle the numeric case because in all other valid cases
        # the corresponding left-binding method will be called.
        elif isinstance(other, Number):
            return NumExprFactor(
                "{constant} {op} x_0".format(op=op, constant=other),
                binds=(self,),
                dtype=binop_return_dtype(op, other.dtype, self.dtype),
            )
        raise BadBinaryOperator(op, other, self)
    return reflected_binary_operator


def unary_operator(op):
    """
    Factory function for making unary operator methods for Factors.
    """
    # Only negate is currently supported.
    valid_ops = {'-'}
    if op not in valid_ops:
        raise ValueError("Invalid unary operator %s." % op)

    @with_doc("Unary Operator: '%s'" % op)
    @with_name(unary_op_name(op))
    def unary_operator(self):
        if self.dtype != float64_dtype:
            raise TypeError(
                "Can't apply unary operator {op!r} to instance of "
                "{typename!r} with dtype {dtypename!r}.\n"
                "{op!r} is only supported for Factors of dtype "
                "'float64'.".format(
                    op=op,
                    typename=type(self).__name__,
                    dtypename=self.dtype.name,
                )
            )

        # This can't be hoisted up a scope because the types returned by
        # unary_op_return_type aren't defined when the top-level function is
        # invoked.
        if isinstance(self, NumericalExpression):
            return NumExprFactor(
                "{op}({expr})".format(op=op, expr=self._expr),
                self.inputs,
                dtype=float64_dtype,
            )
        else:
            return NumExprFactor(
                "{op}x_0".format(op=op),
                (self,),
                dtype=float64_dtype,
            )
    return unary_operator


def function_application(func):
    """
    Factory function for producing function application methods for Factor
    subclasses.
    """
    if func not in NUMEXPR_MATH_FUNCS:
        raise ValueError("Unsupported mathematical function '%s'" % func)

    @with_name(func)
    def mathfunc(self):
        if isinstance(self, NumericalExpression):
            return NumExprFactor(
                "{func}({expr})".format(func=func, expr=self._expr),
                self.inputs,
                dtype=float64_dtype,
            )
        else:
            return NumExprFactor(
                "{func}(x_0)".format(func=func),
                (self,),
                dtype=float64_dtype,
            )
    return mathfunc


FACTOR_DTYPES = frozenset([datetime64ns_dtype, float64_dtype])


class Factor(CompositeTerm):
    """
    Pipeline API expression producing numerically-valued outputs.
    """
    # Dynamically add functions for creating NumExprFactor/NumExprFilter
    # instances.
    clsdict = locals()
    clsdict.update(
        {
            method_name_for_op(op): binary_operator(op)
            # Don't override __eq__ because it breaks comparisons on tuples of
            # Factors.
            for op in MATH_BINOPS.union(COMPARISONS - {'=='})
        }
    )
    clsdict.update(
        {
            method_name_for_op(op, commute=True): reflected_binary_operator(op)
            for op in MATH_BINOPS
        }
    )
    clsdict.update(
        {
            unary_op_name(op): unary_operator(op)
            for op in UNARY_OPS
        }
    )

    clsdict.update(
        {
            funcname: function_application(funcname)
            for funcname in NUMEXPR_MATH_FUNCS
        }
    )

    __truediv__ = clsdict['__div__']
    __rtruediv__ = clsdict['__rdiv__']

    eq = binary_operator('==')

    def _validate(self):
        # Do superclass validation first so that `NotSpecified` dtypes get
        # handled.
        retval = super(Factor, self)._validate()
        if self.dtype not in FACTOR_DTYPES:
            raise UnsupportedDataType(
                typename=type(self).__name__,
                dtype=self.dtype
            )
        return retval

    def rank(self, method='ordinal', ascending=True, mask=NotSpecified):
        """
        Construct a new Factor representing the sorted rank of each column
        within each row.

        Parameters
        ----------
        method : str, {'ordinal', 'min', 'max', 'dense', 'average'}
            The method used to assign ranks to tied elements. See
            `scipy.stats.rankdata` for a full description of the semantics for
            each ranking method. Default is 'ordinal'.
        ascending : bool, optional
            Whether to return sorted rank in ascending or descending order.
            Default is True.
        mask : zipline.pipeline.Filter, optional
            A Filter representing assets to consider when computing ranks.
            If mask is supplied, ranks are computed ignoring any asset/date
            pairs for which `mask` produces a value of False.

        Returns
        -------
        ranks : zipline.pipeline.factors.Rank
            A new factor that will compute the ranking of the data produced by
            `self`.

        Notes
        -----
        The default value for `method` is different from the default for
        `scipy.stats.rankdata`.  See that function's documentation for a full
        description of the valid inputs to `method`.

        Missing or non-existent data on a given day will cause an asset to be
        given a rank of NaN for that day.

        See Also
        --------
        scipy.stats.rankdata
        zipline.lib.rank.masked_rankdata_2d
        zipline.pipeline.factors.factor.Rank
        """
        return Rank(self, method=method, ascending=ascending, mask=mask)

    def top(self, N, mask=NotSpecified):
        """
        Construct a Filter matching the top N asset values of self each day.

        Parameters
        ----------
        N : int
            Number of assets passing the returned filter each day.
        mask : zipline.pipeline.Filter, optional
            A Filter representing assets to consider when computing ranks.
            If mask is supplied, top values are computed ignoring any
            asset/date pairs for which `mask` produces a value of False.

        Returns
        -------
        filter : zipline.pipeline.filters.Filter
        """
        return self.rank(ascending=False, mask=mask) <= N

    def bottom(self, N, mask=NotSpecified):
        """
        Construct a Filter matching the bottom N asset values of self each day.

        Parameters
        ----------
        N : int
            Number of assets passing the returned filter each day.
        mask : zipline.pipeline.Filter, optional
            A Filter representing assets to consider when computing ranks.
            If mask is supplied, bottom values are computed ignoring any
            asset/date pairs for which `mask` produces a value of False.

        Returns
        -------
        filter : zipline.pipeline.Filter
        """
        return self.rank(ascending=True, mask=mask) <= N

    def percentile_between(self,
                           min_percentile,
                           max_percentile,
                           mask=NotSpecified):
        """
        Construct a new Filter representing entries from the output of this
        Factor that fall within the percentile range defined by min_percentile
        and max_percentile.

        Parameters
        ----------
        min_percentile : float [0.0, 100.0]
            Return True for assets falling above this percentile in the data.
        max_percentile : float [0.0, 100.0]
            Return True for assets falling below this percentile in the data.
        mask : zipline.pipeline.Filter, optional
            A Filter representing assets to consider when percentile
            thresholds.  If mask is supplied, percentile cutoffs are computed
            each day using only assets for which `mask` returns True, and
            assets not passing `mask` will produce False in the output of this
            filter as well.

        Returns
        -------
        out : zipline.pipeline.filters.PercentileFilter
            A new filter that will compute the specified percentile-range mask.

        See Also
        --------
        zipline.pipeline.filters.filter.PercentileFilter
        """
        return PercentileFilter(
            self,
            min_percentile=min_percentile,
            max_percentile=max_percentile,
            mask=mask,
        )

    def isnan(self):
        """
        A Filter producing True for all values where this Factor is NaN.

        Returns
        -------
        nanfilter : zipline.pipeline.filters.Filter
        """
        return self != self

    def notnan(self):
        """
        A Filter producing True for values where this Factor is not NaN.

        Returns
        -------
        nanfilter : zipline.pipeline.filters.Filter
        """
        return ~self.isnan()

    def isfinite(self):
        """
        A Filter producing True for values where this Factor is anything but
        NaN, inf, or -inf.
        """
        return (-inf < self) & (self < inf)


class NumExprFactor(NumericalExpression, Factor):
    """
    Factor computed from a numexpr expression.

    Parameters
    ----------
    expr : string
       A string suitable for passing to numexpr.  All variables in 'expr'
       should be of the form "x_i", where i is the index of the corresponding
       factor input in 'binds'.
    binds : tuple
       A tuple of factors to use as inputs.

    Notes
    -----
    NumExprFactors are constructed by numerical operators like `+` and `-`.
    Users should rarely need to construct a NumExprFactor directly.
    """
    pass


class Rank(SingleInputMixin, Factor):
    """
    A Factor representing the row-wise rank data of another Factor.

    Parameters
    ----------
    factor : zipline.pipeline.factors.Factor
        The factor on which to compute ranks.
    method : str, {'average', 'min', 'max', 'dense', 'ordinal'}
        The method used to assign ranks to tied elements.  See
        `scipy.stats.rankdata` for a full description of the semantics for each
        ranking method.

    See Also
    --------
    scipy.stats.rankdata : Underlying ranking algorithm.
    zipline.factors.Factor.rank : Method-style interface to same functionality.

    Notes
    -----
    Most users should call Factor.rank rather than directly construct an
    instance of this class.
    """
    window_length = 0
    dtype = float64_dtype

    def __new__(cls, factor, method, ascending, mask):
        return super(Rank, cls).__new__(
            cls,
            inputs=(factor,),
            method=method,
            ascending=ascending,
            mask=mask,
        )

    def _init(self, method, ascending, *args, **kwargs):
        self._method = method
        self._ascending = ascending
        return super(Rank, self)._init(*args, **kwargs)

    @classmethod
    def static_identity(cls, method, ascending, *args, **kwargs):
        return (
            super(Rank, cls).static_identity(*args, **kwargs),
            method,
            ascending,
        )

    def _validate(self):
        """
        Verify that the stored rank method is valid.
        """
        if self._method not in _RANK_METHODS:
            raise UnknownRankMethod(
                method=self._method,
                choices=set(_RANK_METHODS),
            )
        return super(Rank, self)._validate()

    def _compute(self, arrays, dates, assets, mask):
        """
        For each row in the input, compute a like-shaped array of per-row
        ranks.
        """
        return masked_rankdata_2d(
            arrays[0],
            mask,
            self.inputs[0].missing_value,
            self._method,
            self._ascending,
        )

    def __repr__(self):
        return "{type}({input_}, method='{method}', mask={mask})".format(
            type=type(self).__name__,
            input_=self.inputs[0],
            method=self._method,
            mask=self.mask,
        )


class CustomFactor(PositiveWindowLengthMixin, CustomTermMixin, Factor):
    '''
    Base class for user-defined Factors.

    Parameters
    ----------
    inputs : iterable, optional
        An iterable of `BoundColumn` instances (e.g. USEquityPricing.close),
        describing the data to load and pass to `self.compute`.  If this
        argument is passed to the CustomFactor constructor, we look for a
        class-level attribute named `inputs`.
    window_length : int, optional
        Number of rows to pass for each input.  If this argument is not passed
        to the CustomFactor constructor, we look for a class-level attribute
        named `window_length`.

    Notes
    -----
    Users implementing their own Factors should subclass CustomFactor and
    implement a method named `compute` with the following signature:

    .. code-block:: python

        def compute(self, today, assets, out, *inputs):
           ...

    On each simulation date, ``compute`` will be called with the current date,
    an array of sids, an output array, and an input array for each expression
    passed as inputs to the CustomFactor constructor.

    The specific types of the values passed to `compute` are as follows::

        today : np.datetime64[ns]
            Row label for the last row of all arrays passed as `inputs`.
        assets : np.array[int64, ndim=1]
            Column labels for `out` and`inputs`.
        out : np.array[self.dtype, ndim=1]
            Output array of the same shape as `assets`.  `compute` should write
            its desired return values into `out`.
        *inputs : tuple of np.array
            Raw data arrays corresponding to the values of `self.inputs`.

    ``compute`` functions should expect to be passed NaN values for dates on
    which no data was available for an asset.  This may include dates on which
    an asset did not yet exist.

    For example, if a CustomFactor requires 10 rows of close price data, and
    asset A started trading on Monday June 2nd, 2014, then on Tuesday, June
    3rd, 2014, the column of input data for asset A will have 9 leading NaNs
    for the preceding days on which data was not yet available.

    Examples
    --------

    A CustomFactor with pre-declared defaults:

    .. code-block:: python

        class TenDayRange(CustomFactor):
            """
            Computes the difference between the highest high in the last 10
            days and the lowest low.

            Pre-declares high and low as default inputs and `window_length` as
            10.
            """

            inputs = [USEquityPricing.high, USEquityPricing.low]
            window_length = 10

            def compute(self, today, assets, out, highs, lows):
                from numpy import nanmin, nanmax

                highest_highs = nanmax(highs, axis=0)
                lowest_lows = nanmin(lows, axis=0)
                out[:] = highest_highs - lowest_lows


        # Doesn't require passing inputs or window_length because they're
        # pre-declared as defaults for the TenDayRange class.
        ten_day_range = TenDayRange()

    A CustomFactor without defaults:

    .. code-block:: python

        class MedianValue(CustomFactor):
            """
            Computes the median value of an arbitrary single input over an
            arbitrary window..

            Does not declare any defaults, so values for `window_length` and
            `inputs` must be passed explicitly on every construction.
            """

            def compute(self, today, assets, out, data):
                from numpy import nanmedian
                out[:] = data.nanmedian(data, axis=0)

        # Values for `inputs` and `window_length` must be passed explicitly to
        # MedianValue.
        median_close10 = MedianValue([USEquityPricing.close], window_length=10)
        median_low15 = MedianValue([USEquityPricing.low], window_length=15)
    '''
    dtype = float64_dtype
    ctx = nullctx()


1			"""
2			factor.py
3			"""
4			from operator import attrgetter
5			from numbers import Number
6
7			from numpy import float64, inf
8			from toolz import curry
9
10			from zipline.errors import (
11			UnknownRankMethod,
12			UnsupportedDataType,
13			)
14			from zipline.lib.rank import masked_rankdata_2d
15			from zipline.pipeline.mixins import (
16			CustomTermMixin,
17			PositiveWindowLengthMixin,
18			SingleInputMixin,
19			)
20			from zipline.pipeline.term import CompositeTerm, NotSpecified
21			from zipline.pipeline.expression import (
22			BadBinaryOperator,
23			COMPARISONS,
24			is_comparison,
25			MATH_BINOPS,
26			method_name_for_op,
27			NumericalExpression,
28			NUMEXPR_MATH_FUNCS,
29			UNARY_OPS,
30			unary_op_name,
31			)
32			from zipline.pipeline.filters import (
33			NumExprFilter,
34			PercentileFilter,
35			)
36			from zipline.utils.control_flow import nullctx
37			from zipline.utils.numpy_utils import (
38			bool_dtype,
39			datetime64ns_dtype,
40			float64_dtype,
41			)
42			from zipline.utils.preprocess import preprocess
43
44
45			_RANK_METHODS = frozenset(['average', 'min', 'max', 'dense', 'ordinal'])
46
47
48			def numbers_to_float64(func, argname, argvalue):
49			"""
50			Preprocessor for converting numerical inputs into floats.
51
52			This is used in the binary operator constructors for Factor so that
53			`2 + Factor()` has the same behavior as `2.0 + Factor()`.
54			"""
55			if isinstance(argvalue, Number):
56			return float64(argvalue)
57			return argvalue
58
59
60			@curry
61			def set_attribute(name, value):
62			"""
63			Decorator factory for setting attributes on a function.
64
65			Doesn't change the behavior of the wrapped function.
66
67			Usage
68			-----
69			>>> @set_attribute('__name__', 'foo')
70			... def bar():
71			... return 3
72			...
73			>>> bar()
74			3
75			>>> bar.__name__
76			'foo'
77			"""
78			def decorator(f):
79			setattr(f, name, value)
80			return f
81			return decorator
82
83
84			# Decorators for setting the __name__ and __doc__ properties of a decorated
85			# function.
86			# Example:
87			with_name = set_attribute('__name__')
88			with_doc = set_attribute('__doc__')
89
90
91			def binop_return_type(op):
92			if is_comparison(op):
93			return NumExprFilter
94			else:
95			return NumExprFactor
96
97
98			def binop_return_dtype(op, left, right):
99			"""
100			Compute the expected return dtype for the given binary operator.
101
102			Parameters
103			----------
104			op : str
105			Operator symbol, (e.g. '+', '-', ...).
106			left : numpy.dtype
107			Dtype of left hand side.
108			right : numpy.dtype
109			Dtype of right hand side.
110
111			Returns
112			-------
113			outdtype : numpy.dtype
114			The dtype of the result of `left <op> right`.
115			"""
116			if is_comparison(op):
117			if left != right:
118			raise TypeError(
119			"Don't know how to compute {left} {op} {right}.\n"
120			"Comparisons are only supported between Factors of equal "
121			"dtypes.".format(left=left, op=op, right=right)
122			)
123			return bool_dtype
124
125			elif left != float64_dtype or right != float64_dtype:
126			raise TypeError(
127			"Don't know how to compute {left} {op} {right}.\n"
128			"Arithmetic operators are only supported on Factors of "
129			"dtype 'float64'.".format(
130			left=left.name,
131			op=op,
132			right=right.name,
133			)
134			)
135			return float64_dtype
136
137
138			def binary_operator(op):
139			"""
140			Factory function for making binary operator methods on a Factor subclass.
141
142			Returns a function, "binary_operator" suitable for implementing functions
143			like __add__.
144			"""
145			# When combining a Factor with a NumericalExpression, we use this
146			# attrgetter instance to defer to the commuted implementation of the
147			# NumericalExpression operator.
148			commuted_method_getter = attrgetter(method_name_for_op(op, commute=True))
149
150			@preprocess(other=numbers_to_float64)
151			@with_doc("Binary Operator: '%s'" % op)
152			@with_name(method_name_for_op(op))
153			def binary_operator(self, other):
154			# This can't be hoisted up a scope because the types returned by
155			# binop_return_type aren't defined when the top-level function is
156			# invoked in the class body of Factor.
157			return_type = binop_return_type(op)
158			if isinstance(self, NumExprFactor):
159			self_expr, other_expr, new_inputs = self.build_binary_op(
160			op, other,
161			)
162			return return_type(
163			"({left}) {op} ({right})".format(
164			left=self_expr,
165			op=op,
166			right=other_expr,
167			),
168			new_inputs,
169			dtype=binop_return_dtype(op, self.dtype, other.dtype),
170			)
171			elif isinstance(other, NumExprFactor):
172			# NumericalExpression overrides ops to correctly handle merging of
173			# inputs. Look up and call the appropriate reflected operator with
174			# ourself as the input.
175			return commuted_method_getter(other)(self)
176			elif isinstance(other, Factor):
177			if self is other:
178			return return_type(
179			"x_0 {op} x_0".format(op=op),
180			(self,),
181			dtype=binop_return_dtype(op, self.dtype, other.dtype),
182			)
183			return return_type(
184			"x_0 {op} x_1".format(op=op),
185			(self, other),
186			dtype=binop_return_dtype(op, self.dtype, other.dtype),
187			)
188			elif isinstance(other, Number):
189			return return_type(
190			"x_0 {op} ({constant})".format(op=op, constant=other),
191			binds=(self,),
192			# Interpret numeric literals as floats.
193			dtype=binop_return_dtype(op, self.dtype, other.dtype)
194			)
195			raise BadBinaryOperator(op, self, other)
196
197			return binary_operator
198
199
200			def reflected_binary_operator(op):
201			"""
202			Factory function for making binary operator methods on a Factor.
203
204			Returns a function, "reflected_binary_operator" suitable for implementing
205			functions like __radd__.
206			"""
207			assert not is_comparison(op)
208
209			@preprocess(other=numbers_to_float64)
210			@with_name(method_name_for_op(op, commute=True))
211			def reflected_binary_operator(self, other):
212
213			if isinstance(self, NumericalExpression):
214			self_expr, other_expr, new_inputs = self.build_binary_op(
215			op, other
216			)
217			return NumExprFactor(
218			"({left}) {op} ({right})".format(
219			left=other_expr,
220			right=self_expr,
221			op=op,
222			),
223			new_inputs,
224			dtype=binop_return_dtype(op, other.dtype, self.dtype)
225			)
226
227			# Only have to handle the numeric case because in all other valid cases
228			# the corresponding left-binding method will be called.
229			elif isinstance(other, Number):
230			return NumExprFactor(
231			"{constant} {op} x_0".format(op=op, constant=other),
232			binds=(self,),
233			dtype=binop_return_dtype(op, other.dtype, self.dtype),
234			)
235			raise BadBinaryOperator(op, other, self)
236			return reflected_binary_operator
237
238
239			def unary_operator(op):
240			"""
241			Factory function for making unary operator methods for Factors.
242			"""
243			# Only negate is currently supported.
244			valid_ops = {'-'}
245			if op not in valid_ops:
246			raise ValueError("Invalid unary operator %s." % op)
247
248			@with_doc("Unary Operator: '%s'" % op)
249			@with_name(unary_op_name(op))
250			def unary_operator(self):
251			if self.dtype != float64_dtype:
252			raise TypeError(
253			"Can't apply unary operator {op!r} to instance of "
254			"{typename!r} with dtype {dtypename!r}.\n"
255			"{op!r} is only supported for Factors of dtype "
256			"'float64'.".format(
257			op=op,
258			typename=type(self).__name__,
259			dtypename=self.dtype.name,
260			)
261			)
262
263			# This can't be hoisted up a scope because the types returned by
264			# unary_op_return_type aren't defined when the top-level function is
265			# invoked.
266			if isinstance(self, NumericalExpression):
267			return NumExprFactor(
268			"{op}({expr})".format(op=op, expr=self._expr),
269			self.inputs,
270			dtype=float64_dtype,
271			)
272			else:
273			return NumExprFactor(
274			"{op}x_0".format(op=op),
275			(self,),
276			dtype=float64_dtype,
277			)
278			return unary_operator
279
280
281			def function_application(func):
282			"""
283			Factory function for producing function application methods for Factor
284			subclasses.
285			"""
286			if func not in NUMEXPR_MATH_FUNCS:
287			raise ValueError("Unsupported mathematical function '%s'" % func)
288
289			@with_name(func)
290			def mathfunc(self):
291			if isinstance(self, NumericalExpression):
292			return NumExprFactor(
293			"{func}({expr})".format(func=func, expr=self._expr),
294			self.inputs,
295			dtype=float64_dtype,
296			)
297			else:
298			return NumExprFactor(
299			"{func}(x_0)".format(func=func),
300			(self,),
301			dtype=float64_dtype,
302			)
303			return mathfunc
304
305
306			FACTOR_DTYPES = frozenset([datetime64ns_dtype, float64_dtype])
307
308
309			class Factor(CompositeTerm):
310			"""
311			Pipeline API expression producing numerically-valued outputs.
312			"""
313			# Dynamically add functions for creating NumExprFactor/NumExprFilter
314			# instances.
315			clsdict = locals()
316			clsdict.update(
317			{
318			method_name_for_op(op): binary_operator(op)
319			# Don't override __eq__ because it breaks comparisons on tuples of
320			# Factors.
321			for op in MATH_BINOPS.union(COMPARISONS - {'=='})
322			}
323			)
324			clsdict.update(
325			{
326			method_name_for_op(op, commute=True): reflected_binary_operator(op)
327			for op in MATH_BINOPS
328			}
329			)
330			clsdict.update(
331			{
332			unary_op_name(op): unary_operator(op)
333			for op in UNARY_OPS
334			}
335			)
336
337			clsdict.update(
338			{
339			funcname: function_application(funcname)
340			for funcname in NUMEXPR_MATH_FUNCS
341			}
342			)
343
344			__truediv__ = clsdict['__div__']
345			__rtruediv__ = clsdict['__rdiv__']
346
347			eq = binary_operator('==')
348
349			def _validate(self):
350			# Do superclass validation first so that `NotSpecified` dtypes get
351			# handled.
352			retval = super(Factor, self)._validate()
353			if self.dtype not in FACTOR_DTYPES:
354			raise UnsupportedDataType(
355			typename=type(self).__name__,
356			dtype=self.dtype
357			)
358			return retval
359
360			def rank(self, method='ordinal', ascending=True, mask=NotSpecified):
361			"""
362			Construct a new Factor representing the sorted rank of each column
363			within each row.
364
365			Parameters
366			----------
367			method : str, {'ordinal', 'min', 'max', 'dense', 'average'}
368			The method used to assign ranks to tied elements. See
369			`scipy.stats.rankdata` for a full description of the semantics for
370			each ranking method. Default is 'ordinal'.
371			ascending : bool, optional
372			Whether to return sorted rank in ascending or descending order.
373			Default is True.
374			mask : zipline.pipeline.Filter, optional
375			A Filter representing assets to consider when computing ranks.
376			If mask is supplied, ranks are computed ignoring any asset/date
377			pairs for which `mask` produces a value of False.
378
379			Returns
380			-------
381			ranks : zipline.pipeline.factors.Rank
382			A new factor that will compute the ranking of the data produced by
383			`self`.
384
385			Notes
386			-----
387			The default value for `method` is different from the default for
388			`scipy.stats.rankdata`. See that function's documentation for a full
389			description of the valid inputs to `method`.
390
391			Missing or non-existent data on a given day will cause an asset to be
392			given a rank of NaN for that day.
393
394			See Also
395			--------
396			scipy.stats.rankdata
397			zipline.lib.rank.masked_rankdata_2d
398			zipline.pipeline.factors.factor.Rank
399			"""
400			return Rank(self, method=method, ascending=ascending, mask=mask)
401
402			def top(self, N, mask=NotSpecified):
403			"""
404			Construct a Filter matching the top N asset values of self each day.
405
406			Parameters
407			----------
408			N : int
409			Number of assets passing the returned filter each day.
410			mask : zipline.pipeline.Filter, optional
411			A Filter representing assets to consider when computing ranks.
412			If mask is supplied, top values are computed ignoring any
413			asset/date pairs for which `mask` produces a value of False.
414
415			Returns
416			-------
417			filter : zipline.pipeline.filters.Filter
418			"""
419			return self.rank(ascending=False, mask=mask) <= N
420
421			def bottom(self, N, mask=NotSpecified):
422			"""
423			Construct a Filter matching the bottom N asset values of self each day.
424
425			Parameters
426			----------
427			N : int
428			Number of assets passing the returned filter each day.
429			mask : zipline.pipeline.Filter, optional
430			A Filter representing assets to consider when computing ranks.
431			If mask is supplied, bottom values are computed ignoring any
432			asset/date pairs for which `mask` produces a value of False.
433
434			Returns
435			-------
436			filter : zipline.pipeline.Filter
437			"""
438			return self.rank(ascending=True, mask=mask) <= N
439
440			def percentile_between(self,
441			min_percentile,
442			max_percentile,
443			mask=NotSpecified):
444			"""
445			Construct a new Filter representing entries from the output of this
446			Factor that fall within the percentile range defined by min_percentile
447			and max_percentile.
448
449			Parameters
450			----------
451			min_percentile : float [0.0, 100.0]
452			Return True for assets falling above this percentile in the data.
453			max_percentile : float [0.0, 100.0]
454			Return True for assets falling below this percentile in the data.
455			mask : zipline.pipeline.Filter, optional
456			A Filter representing assets to consider when percentile
457			thresholds. If mask is supplied, percentile cutoffs are computed
458			each day using only assets for which `mask` returns True, and
459			assets not passing `mask` will produce False in the output of this
460			filter as well.
461
462			Returns
463			-------
464			out : zipline.pipeline.filters.PercentileFilter
465			A new filter that will compute the specified percentile-range mask.
466
467			See Also
468			--------
469			zipline.pipeline.filters.filter.PercentileFilter
470			"""
471			return PercentileFilter(
472			self,
473			min_percentile=min_percentile,
474			max_percentile=max_percentile,
475			mask=mask,
476			)
477
478			def isnan(self):
479			"""
480			A Filter producing True for all values where this Factor is NaN.
481
482			Returns
483			-------
484			nanfilter : zipline.pipeline.filters.Filter
485			"""
486			return self != self
487
488			def notnan(self):
489			"""
490			A Filter producing True for values where this Factor is not NaN.
491
492			Returns
493			-------
494			nanfilter : zipline.pipeline.filters.Filter
495			"""
496			return ~self.isnan()
497
498			def isfinite(self):
499			"""
500			A Filter producing True for values where this Factor is anything but
501			NaN, inf, or -inf.
502			"""
503			return (-inf < self) & (self < inf)
504
505
506			class NumExprFactor(NumericalExpression, Factor):
507			"""
508			Factor computed from a numexpr expression.
509
510			Parameters
511			----------
512			expr : string
513			A string suitable for passing to numexpr. All variables in 'expr'
514			should be of the form "x_i", where i is the index of the corresponding
515			factor input in 'binds'.
516			binds : tuple
517			A tuple of factors to use as inputs.
518
519			Notes
520			-----
521			NumExprFactors are constructed by numerical operators like `+` and `-`.
522			Users should rarely need to construct a NumExprFactor directly.
523			"""
524			pass
525
526
527			class Rank(SingleInputMixin, Factor):
528			"""
529			A Factor representing the row-wise rank data of another Factor.
530
531			Parameters
532			----------
533			factor : zipline.pipeline.factors.Factor
534			The factor on which to compute ranks.
535			method : str, {'average', 'min', 'max', 'dense', 'ordinal'}
536			The method used to assign ranks to tied elements. See
537			`scipy.stats.rankdata` for a full description of the semantics for each
538			ranking method.
539
540			See Also
541			--------
542			scipy.stats.rankdata : Underlying ranking algorithm.
543			zipline.factors.Factor.rank : Method-style interface to same functionality.
544
545			Notes
546			-----
547			Most users should call Factor.rank rather than directly construct an
548			instance of this class.
549			"""
550			window_length = 0
551			dtype = float64_dtype
552
553			def __new__(cls, factor, method, ascending, mask):
554			return super(Rank, cls).__new__(
555			cls,
556			inputs=(factor,),
557			method=method,
558			ascending=ascending,
559			mask=mask,
560			)
561
562			def _init(self, method, ascending, args, *kwargs):
563			self._method = method
564			self._ascending = ascending
565			return super(Rank, self)._init(args, *kwargs)
566
567			@classmethod
568			def static_identity(cls, method, ascending, args, *kwargs):
569			return (
570			super(Rank, cls).static_identity(args, *kwargs),
571			method,
572			ascending,
573			)
574
575			def _validate(self):
576			"""
577			Verify that the stored rank method is valid.
578			"""
579			if self._method not in _RANK_METHODS:
580			raise UnknownRankMethod(
581			method=self._method,
582			choices=set(_RANK_METHODS),
583			)
584			return super(Rank, self)._validate()
585
586			def _compute(self, arrays, dates, assets, mask):
587			"""
588			For each row in the input, compute a like-shaped array of per-row
589			ranks.
590			"""
591			return masked_rankdata_2d(
592			arrays[0],
593			mask,
594			self.inputs[0].missing_value,
595			self._method,
596			self._ascending,
597			)
598
599			def __repr__(self):
600			return "{type}({input_}, method='{method}', mask={mask})".format(
601			type=type(self).__name__,
602			input_=self.inputs[0],
603			method=self._method,
604			mask=self.mask,
605			)
606
607
608			class CustomFactor(PositiveWindowLengthMixin, CustomTermMixin, Factor):
609			'''
610			Base class for user-defined Factors.
611
612			Parameters
613			----------
614			inputs : iterable, optional
615			An iterable of `BoundColumn` instances (e.g. USEquityPricing.close),
616			describing the data to load and pass to `self.compute`. If this
617			argument is passed to the CustomFactor constructor, we look for a
618			class-level attribute named `inputs`.
619			window_length : int, optional
620			Number of rows to pass for each input. If this argument is not passed
621			to the CustomFactor constructor, we look for a class-level attribute
622			named `window_length`.
623
624			Notes
625			-----
626			Users implementing their own Factors should subclass CustomFactor and
627			implement a method named `compute` with the following signature:
628
629			.. code-block:: python
630
631			def compute(self, today, assets, out, *inputs):
632			...
633
634			On each simulation date, ``compute`` will be called with the current date,
635			an array of sids, an output array, and an input array for each expression
636			passed as inputs to the CustomFactor constructor.
637
638			The specific types of the values passed to `compute` are as follows::
639
640			today : np.datetime64[ns]
641			Row label for the last row of all arrays passed as `inputs`.
642			assets : np.array[int64, ndim=1]
643			Column labels for `out` and`inputs`.
644			out : np.array[self.dtype, ndim=1]
645			Output array of the same shape as `assets`. `compute` should write
646			its desired return values into `out`.
647			*inputs : tuple of np.array
648			Raw data arrays corresponding to the values of `self.inputs`.
649
650			``compute`` functions should expect to be passed NaN values for dates on
651			which no data was available for an asset. This may include dates on which
652			an asset did not yet exist.
653
654			For example, if a CustomFactor requires 10 rows of close price data, and
655			asset A started trading on Monday June 2nd, 2014, then on Tuesday, June
656			3rd, 2014, the column of input data for asset A will have 9 leading NaNs
657			for the preceding days on which data was not yet available.
658
659			Examples
660			--------
661
662			A CustomFactor with pre-declared defaults:
663
664			.. code-block:: python
665
666			class TenDayRange(CustomFactor):
667			"""
668			Computes the difference between the highest high in the last 10
669			days and the lowest low.
670
671			Pre-declares high and low as default inputs and `window_length` as
672			10.
673			"""
674
675			inputs = [USEquityPricing.high, USEquityPricing.low]
676			window_length = 10
677
678			def compute(self, today, assets, out, highs, lows):
679			from numpy import nanmin, nanmax
680
681			highest_highs = nanmax(highs, axis=0)
682			lowest_lows = nanmin(lows, axis=0)
683			out[:] = highest_highs - lowest_lows
684
685
686			# Doesn't require passing inputs or window_length because they're
687			# pre-declared as defaults for the TenDayRange class.
688			ten_day_range = TenDayRange()
689
690			A CustomFactor without defaults:
691
692			.. code-block:: python
693
694			class MedianValue(CustomFactor):
695			"""
696			Computes the median value of an arbitrary single input over an
697			arbitrary window..
698
699			Does not declare any defaults, so values for `window_length` and
700			`inputs` must be passed explicitly on every construction.
701			"""
702
703			def compute(self, today, assets, out, data):
704			from numpy import nanmedian
705			out[:] = data.nanmedian(data, axis=0)
706
707			# Values for `inputs` and `window_length` must be passed explicitly to
708			# MedianValue.
709			median_close10 = MedianValue([USEquityPricing.close], window_length=10)
710			median_low15 = MedianValue([USEquityPricing.low], window_length=15)
711			'''
712			dtype = float64_dtype
713			ctx = nullctx()
714

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