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What does the '|' symbol mean for a numexpr expression?

I am trying to understand this example. Particualrly this line: securities_to_trade = (low_returns | high_returns) (line number 104 ).

What I could gather though, is that
1. low_returns and high_returns are objects of class zipline.pipeline.filters.filter.PercentileFilter

2.securities_to_trade is "a Filter computed from a numexpr expression" and

3.the '|' symbol is a logical 'or' operator for numexpr.

I tried to replicate the function of the '|' function unsuccessfully. Please find code below:

import numpy as np  
import numexpr as ne  
a = np.array([1,2,3])  
b = np.array([4,5,6])  
ne.evaluate(a | b)  
ne.evaluate('a | b')  

This is the error I got:

NotImplementedError: couldn't find matching opcode for 'or_bll'  

So, my question is:
1. Am I right in assuming that the '|' symbol is a logical or operator?
2. How do I replicate the operation with arrays?

2 responses

Yes, you are right. The | operator is a logical OR of the two filters. So for example,

securities_to_trade = (low_returns | high_returns)

would create a new filter securities_to_trade. If a stock is in either low_returns OR high_returns it will be included in securities_to_trade.

The functionality is identical to using Python sets.

even_numbers = {2, 4, 6, 8, 10}  
odd_numbers = {1, 3, 5, 7, 9}

evens_or_odds = even_numbers | odd_numbers  
display(evens_or_odds)

{1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

There isn't a direct numpy array function. The closest would be using concatenate and unique.

import numpy as np  
even_numbers = np.array([2, 4, 6, 8, 10])  
odd_numbers = np.array([1, 3, 5, 7, 9])

evens_or_odds = np.unique(np.concatenate([even_numbers, odd_numbers]))  
display(evens_or_odds)

array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])


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Thanks again , @Dan. :)