November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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130
｜
第
4
章
图 4-17： GLD ETF 的总表现和均值回归策略（SMA = 43，阈值 = 7.5，交易成本为 0.1％）
4.5
数据窥探和过度拟合
本章以及本书的其余部分，重点是使用
Python
对重要概念进行技术实现。有时会随
意选择使用的策略，参数，数据集和算法，有时会特意选择以说明某个问题。毫无
疑问，在讨论应用于金融的技术方法时，看到示例显示“结果良好”会更加令人振
奋和鼓舞，即使这些示例可能不会在例如其他金融工具或时间段上一概而论。
展示具有良好结果的示例的能力通常是以数据窥探为代价的。根据
White
（
2000
）
的说法，可以将数据窥探定义如下：
当出于推理或模型选择的目的而多次使用给定的数据集时，就是数据窥探。
换句话说，可以对同一数据集几次或非常多次地应用某种方法，以获得令人满意的
数字和曲线图。当然，这在交易策略研究中在思想上是不真实的，因为它假定交易
策略具有一定的经济潜力，这在真实世界中可能是不现实的。因为本书的重点是使
用
Python
作为算法交易的编程语言，所以数据窥探方法可能是合理的。这类似于一
本数学书，该数学书举例说明了一个方程式，该方程式具有可以轻松识别的独特解
掌握向量化回测
｜
131
决方案。在数学中，这样简单明了的例子不是规则而是例外，但是它们经常用于教
学目的。 ...
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