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:
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:
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.
percent_difference = (mean_close_10 - mean_close_30) / mean_close_30
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):
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:
result = run_pipeline(make_pipeline(), start_date='2015-05-05', end_date='2015-05-05')
result
Next Lesson: Filters