In [1]:
from quantopian.pipeline import Pipeline
from quantopian.research import run_pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import SimpleMovingAverage, AverageDollarVolume
Like factors, filters can be combined. Combining filters is done using the & (and) and | (or) operators. For example, let's say we want to screen for securities that are in the top 10% of average dollar volume and have a latest close price above $20. To start, let's make a high dollar volume filter using an AverageDollarVolume factor and percentile_between:
In [2]:
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(90, 100)
Note: percentile_between is a Factor method returning a Filter.
Next, let's create a latest_close factor and define a filter for securities that closed above $20:
In [3]:
latest_close = USEquityPricing.close.latest
above_20 = latest_close > 20
Now we can combine our high_dollar_volume filter with our above_20 filter using the & operator:
In [4]:
tradeable_filter = high_dollar_volume & above_20
This filter will evaluate to True for securities where both high_dollar_volume and above_20 are True. Otherwise, it will evaluate to False. A similar computation can be made with the | (or) operator.
If we want to use this filter as a screen in our pipeline, we can simply pass tradeable_filter as the screen argument.
In [5]:
def make_pipeline():
mean_close_10 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=10)
mean_close_30 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=30)
percent_difference = (mean_close_10 - mean_close_30) / mean_close_30
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(90, 100)
latest_close = USEquityPricing.close.latest
above_20 = latest_close > 20
tradeable_filter = high_dollar_volume & above_20
return Pipeline(
columns={
'percent_difference': percent_difference
},
screen=tradeable_filter
)
When we run this, our pipeline output now only includes ~700 securities.
In [6]:
result = run_pipeline(make_pipeline(), '2015-05-05', '2015-05-05')
print 'Number of securities that passed the filter: %d' % len(result)
result
Out[6]:
In the next lesson, we'll look at masking factors and filters.