November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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132
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第
4
章
当然，我们鼓励你使用本书中提供的代码和示例探索你自己的算法交易策略思想，
并根据你自己的回测结果，验证和结论在实践中实施。总之，恰当而勤奋的策略研
究而不是蛮力驱动的数据窥探和过度拟合才会得到金融市场的补偿反馈。
4.6
小结
在算法交易策略的回测中，向量化是科学计算以及金融分析中的强大概念。本章介
绍了
NumPy
和
pandas
的向量化，并将其应用于最新的三种交易策略：基于简单移动
平均线，动量和均值回归的策略。诚然，本章做出了许多简化的假设，并且对交易
策略进行严格的回测需要考虑更多决定实践中交易成功的因素，例如数据问题，选
择问题，避免过度拟合或市场微观结构要素。但是，本章的主要目标是从技术和实
现的角度着重于向量化的概念及其在算法交易中的作用。关于所提出的所有具体示
例和结果，需要考虑数据窥探，过度拟合和统计意义的问题。
4.7
参考资料和延伸资源
有关使用
NumPy
和
pandas
进行向量化的基础知识，请参阅以下书籍：
McKinney, Wes. 2017.
Python for Data Analysis
. 2nd ed. Sebastopol: O
’
Reilly.
VanderPlas, Jake. 2016.
Python Data Science Handbook
. Sebastopol: O
’
Reilly.
有关在金融场景使用
NumPy
和
pandas
的信息，请参阅以下书籍：
Hilpisch, Yves. 2015.
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