November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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掌握向量化回测
｜
103
In [17]: a.mean(axis=0)
Out[17]: array([4.5, 5.5, 6.5])
In [18]: np.mean(a, axis=1)
Out[18]: array([ 1., 4., 7., 10.])
通过方法调用计算所有元素的平均值。
通过通用函数计算所有元素的平均值。
计算沿第一个轴的平均值。
计算沿第二个轴的平均值。
作为金融方面的示例，请考虑“
Python
脚本”中的函数
generate_sample_data()
，
该函数使用欧拉离散化生成几何布朗运动的样本路线。该实现利用了多个向量化操
作，这些操作被组合到一行代码中。
有关使用
NumPy
进行向量化的更多详细信息，请参见附录。请参阅
Hilpisch
（
2018
），
以了解金融环境中向量化的众多应用。
Python
的标准指令集和数据模型通常不允许进行向量化数值运算。
NumPy
引入了基于常规数组类
ndarray
的强大向量化技术，这些技术带
来了接近数学符号的简洁代码，例如关于向量和矩阵的线性代数。
4.1.2
使用
pandas
进行向量化
pandas
包和
DataFrame
类大量使用了
NumPy
和
ndarray
类。因此，在
NumPy
上下文
中看到的大多数向量化原理都可以延续到
pandas
身上。这个机制可以通过具体的示
例很好的解释。我们首先定义一个二维
ndarray
对象来开始这个示例：
In [19]: a = np.arange(15).reshape(5, 3)
In [20]: ...
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