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파이썬을 활용한 금융 분석(2판) : 파이썬의 기초부터 금융공학, 머신러닝, 퀀트 분석, 매매 시스템 구현까지
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파이썬을 활용한 금융 분석(2판) : 파이썬의 기초부터 금융공학, 머신러닝, 퀀트 분석, 매매 시스템 구현까지

by 이브 힐피시, 김도형
March 2022
Beginner to intermediate
796 pages
18h 14m
Korean
Hanbit Media, Inc.
Content preview from 파이썬을 활용한 금융 분석(2판) : 파이썬의 기초부터 금융공학, 머신러닝, 퀀트 분석, 매매 시스템 구현까지
379
10
장
파이썬 성능 개선
이 방식은 코드 실행 속도를
20
배에서
100
배까지 개선한다.
10.5.2
Numba
알고리즘 구조상
Numba
를 적용하면 좀 더 속도 개선을 할 수 있다. 그리고 실제로
ndarray
버
전에 적용한 함수
ewma
_
nb
( )
에서는 속도 개선이 가능했다.
In [159]: ewma_nb = numba.jit(ewma_py)
In [160]: %time data['EWMA_NB'] = ewma_nb(data[sym], alpha)
➊
CPU times: user 269 ms, sys: 11.4 ms, total: 280 ms
Wall time: 294 ms
In [161]: %timeit data['EWMA_NB'] = ewma_nb(data[sym], alpha)
➊
30.9 ms ± 1.21 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [162]: %time data['EWMA_NB'] = ewma_nb(data[sym].values, alpha)
➋
CPU times: user 94.1 ms, sys: 3.78 ms, total: 97.9 ms
Wall time: 97.6 ms
In [163]: %timeit data['EWMA_NB'] = ewma_nb(data[sym].values, ...
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Publisher Resources

ISBN: 9791162245170