zipline.pipeline.factors.Returns

Complexity

Total Complexity

1

Size/Duplication

Total Lines	14
Duplicated Lines	0 %

1 Method

Rating	Name	Duplication	Size	Complexity
A	compute()	0	2	1

"""
Technical Analysis Factors
--------------------------
"""
from bottleneck import (
    nanargmax,
    nanmax,
    nanmean,
    nansum,
)
from numpy import (
    abs,
    clip,
    diff,
    fmax,
    inf,
    isnan,
    NINF,
)
from numexpr import evaluate

from zipline.pipeline.data import USEquityPricing
from zipline.pipeline.term import SingleInputMixin
from zipline.utils.control_flow import ignore_nanwarnings
from .factor import CustomFactor


class Returns(CustomFactor):
    """
    Calculates the % change in close price over the given window_length
    (i.e. the returns for a given asset).

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

    **Default Window Length**: 30
    """
    window_length = 30
    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]