Combining Factors¶

Factors can be combined, both with other Factors and with scalar values, via any of the builtin mathematical operators (+, -, *, etc). This makes it easy to write complex expressions that combine multiple Factors. For example, constructing a Factor that computes the average of two other Factors is simply:

>>> f1 = SomeFactor(...)
>>> f2 = SomeOtherFactor(...)
>>> average = (f1 + f2) / 2.0

In this lesson, we will create a pipeline that creates a relative_difference factor by combining a 10-day average factor and a 30-day average factor.

As usual, let's start with our imports:

In [1]:

from zipline.pipeline import Pipeline
from zipline.research import run_pipeline
from zipline.pipeline.data import EquityPricing
from zipline.pipeline.factors import SimpleMovingAverage

For this example, we need two factors: a 10-day mean close price factor, and a 30-day one:

In [2]:

mean_close_10 = SimpleMovingAverage(inputs=[EquityPricing.close], window_length=10)
mean_close_30 = SimpleMovingAverage(inputs=[EquityPricing.close], window_length=30)

Then, let's create a percent difference factor by combining our mean_close_30 factor with our mean_close_10 factor.

In this example, percent_difference is still a Factor even though it's composed as a combination of more primitive factors. We can add percent_difference as a column in our pipeline. Let's define make_pipeline to create a pipeline with percent_difference as a column (and not the mean close factors):

In [4]:

def make_pipeline():

    mean_close_10 = SimpleMovingAverage(inputs=[EquityPricing.close], window_length=10)
    mean_close_30 = SimpleMovingAverage(inputs=[EquityPricing.close], window_length=30)

    percent_difference = (mean_close_10 - mean_close_30) / mean_close_30

    return Pipeline(
        columns={
            'percent_difference': percent_difference
        }
    )

Let's see what the new output looks like:

In [5]:

result = run_pipeline(make_pipeline(), start_date='2015-05-05', end_date='2015-05-05')
result

Out[5]:

		percent_difference
2015-05-05 00:00:00+00:00	Equity(FIBBG000C2V3D6 [A])	-0.000111
	Equity(FIBBG00B3T3HD3 [AA])	NaN
	Equity(QI000000004076 [AABA])	-0.017429
	Equity(FIBBG001B9VR83 [AAC])	NaN
	Equity(FIBBG006T1NZ18 [AAC])	0.048123
	...	...
	Equity(FIBBG0039320N9 [ZTS])	-0.006740
	Equity(FIBBG001Z7M393 [ZU])	-0.043705
	Equity(FIBBG000PYX812 [ZUMZ])	-0.082763
	Equity(FIBBG000C3CQP1 [ZVO])	-0.041755
	Equity(FIBBG001NFC923 [ZX])	0.040279

8282 rows × 1 columns