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:
from zipline.pipeline import Pipeline
from zipline.research import run_pipeline
from zipline.pipeline.data import EquityPricing
from zipline.pipeline.factors import SimpleMovingAverage, AverageDollarVolume
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:
latest_close = EquityPricing.close.latest
above_20 = latest_close > 20
Now we can combine our high_dollar_volume filter with our above_20 filter using the & operator:
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.
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
dollar_volume = AverageDollarVolume(window_length=30)
high_dollar_volume = dollar_volume.percentile_between(90, 100)
latest_close = EquityPricing.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.
result = run_pipeline(make_pipeline(), start_date='2015-05-05', end_date='2015-05-05')
print(f'Number of securities that passed the filter: {len(result)}')
result
Number of securities that passed the filter: 753
| percent_difference | ||
|---|---|---|
| 2015-05-05 00:00:00+00:00 | Equity(FIBBG000C2V3D6 [A]) | -0.000111 |
| Equity(QI000000004076 [AABA]) | -0.017429 | |
| Equity(FIBBG005P7Q881 [AAL]) | 0.007729 | |
| Equity(FIBBG000F7RCJ1 [AAP]) | -0.010067 | |
| Equity(FIBBG000B9XRY4 [AAPL]) | 0.016827 | |
| ... | ... | |
| Equity(FIBBG000BH3GZ2 [YUM]) | 0.057707 | |
| Equity(FIBBG003H0XV18 [YY]) | 0.060564 | |
| Equity(FIBBG000BKPL53 [ZBH]) | -0.007930 | |
| Equity(FIBBG000D13GN8 [ZG]) | 0.003626 | |
| Equity(FIBBG0039320N9 [ZTS]) | -0.006740 |
753 rows × 1 columns
Next Lesson: Masking