November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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Python
和算法交易
｜
13
些任务又可能需要在一定期限内完成数百万次模拟，通常几乎是实时或者至少是近
实时的。而
Python
作为一种解释性的高级编程语言，并没有被设计为能以足够快的
速度处理这样的计算任务的。
1.1.2 NumPy
和向量化
在
2006
年，
Travis Oliphant
发行了
NumPy
Python
软件包的
1.0
版（
http://numpy.
org
）。
NumPy
的意思是数值
Python
，表明它是专门针对有严格数值要求应用场景的。
基本的
Python
解释器试图在许多方面尽可能地通用，这通常会在运行时导致相当多
的开销。
注
3
另一方面，
NumPy
主要使用专门的方式作为其避免开销并尽可能在某些
应用程序场景中让自己又快又好。
NumPy
的主要类是常规数组对象，称为
n
维数组的
ndarray
对象。它是不可变的，
这意味着它的大小无法更改，并且只能容纳称为
dtype
的一个数据类型。这种专门
的优化可以让代码更简洁而快速。在这种情况下，一种主要方法是矢量化。基本上，
这种方法避免了在
Python
级别的循环，而将循环委托给专用的
NumPy
代码，通常以
C
语言实现，因此速度相当快。
考虑使用纯
Python
根据式（
1-1
）模拟
1000000
个周期末值
S
T
。以下代码的主要部
分是具有
1000000
次迭代的
for
循环。
In [1]: %%time
import random
from math import exp, sqrt
S0 = 100
r = 0.05 ...
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