November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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100
｜
第
4
章
本章的内容如下。“利用向量化”介绍了向量化作为制定和回测交易策略的有用技
术方法。“基于简单移动平均线的策略”是本章的核心，并在一定程度上深入介绍
了基于
SMA
的策略的矢量回测。“基于动量的策略”介绍并回测了基于股票时序
动量（“近期表现”）的交易策略。“基于均值回归的策略”结束了本章，介绍了
均值回归策略。“数据窥探和过度拟合”讨论了在算法交易策略的回测情况下数据
窥探和过度拟合的陷阱。
本章的主要目标是掌握向量化的方法，该方法可以通过
NumPy
和
pandas
有效且快速
的完成。为此，对提出的方法进行了一些简化的假设，以使讨论更好地集中在向量
化的主题上。
在以下情况下，应考虑向量化回测：
简单交易策略
向量化回测方法在算法交易策略建模方面显然存在局限性。但是，许多流行的
简单策略都可以用向量化的方式进行回测。
互动策略探索
向量化回测允许对交易策略及其特征进行灵活，交互式的探索。通常只需几行
代码即可得出第一个结果，并且可以轻松测试不同的参数组合。
可视化为主要目标
该方法非常适合于可视化所用数据，统计信息，交易信号和结果表现。通常，
只需几行
Python
代码就能生成足以吸引人同时又洞察深刻的图。
全面的回测程序
通常，向量化回测速度非常快，允许人们在短时间内测试多种参数组合。当速
度是关键时，应考虑该方法。
4.1
利用向量化
向量化或数组编程是指一种编程样式，它把对标量（即整数或浮点数）的操作转化
为向量，矩阵甚至多维数组。考虑一个整数向量
v
= (1
，
2
，
3
，
4
，
5)
T
，在
Python
中表示为列表对象 ...
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