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.
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.
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