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What is the most efficient way to get log returns in numpy

What is the fastest and most elegant solution to building a sequence of log returns?

The problem is mainly around mapping a function that takes the i'th and (i+1)'th elements as inputs for every element in the array.

for a function and simple array I can define the log returns as follows:

import numpy as np
ar = np.random.rand(10)
f_logR = lambda ri, rf: np.log(rf) - np.log(ri)

logR = np.asarray([f_logR(ar[i], rf) for i,rf in enumerate(ar[1:])])

However, I am building a list from individual numpy elements and then converting it back into a numpy array again.

I am also accessing the elements in a fairly brutish way as I have little experience with generator functions or numpy internals.

like image 345
Alexander McFarlane Avatar asked Dec 15 '22 14:12

Alexander McFarlane


1 Answers

f_logR = lambda ri, rf: np.log(rf) - np.log(ri)
logR = np.asarray([f_logR(ar[i], rf) for i,rf in enumerate(ar[1:])])

is equivalent to

logR = np.diff(np.log(ar))

np.log takes the log of every value in ar, and np.diff takes the difference between every consecutive pair of values.

like image 162
unutbu Avatar answered Dec 17 '22 04:12

unutbu