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
｜
107
本章其余部分所述。有关
pandas
向量化基础知识和金融示例的更多信息，请参阅
Hilpisch
（
2018
，第
5
章）。
Numpy
为
Python
的数字计算世界带来了通用的向量化方法，而
pandas
允许对时序数据进行向量化。这对于实施金融算法和对算法交易策略进
行回测确实很有帮助。与标准的
Python
代码相比，比如利用
for
循环和
类似的常用方法来完成，通过使用这种方法，你可以期望代码更简洁，
执行速度更快，
4.2
基于简单移动平均线的策略
基于简单移动平均线（
SMA
）进行交易是一项具有数十年历史的策略，它起源于技
术股票分析领域。例如，布罗克（
Brock
）等（
1992
）的文章以系统的方式对这种策
略进行实证研究。他们写道：
“技术分析”一词是无数交易技术的总称……在本文中，我们探讨了两个最简
单且最受欢迎的技术规则：移动平均振荡和交易区间突破（阻力和支撑水平）。
在第一种方法中，买入和卖出信号是由两个移动平均线（一个长周期和一个短
周期）产生的。我们的研究表明，技术分析有助于预测股票变化。
4.2.1
入门基础
本节重点介绍两个基础的利用
SMA
进行交易回测的策略。下面的示例使用
EOD
数
据文件（
https://oreil.ly/AzE-p
）下
csv
文件中提供的欧元
/
美元汇率的日末（
EOD
）
收盘数据。数据集中的数据来自
Refinitiv Eikon Data API
，代表相应金融工具（
RIC
）
的日末值：
In [37]: raw = pd.read_csv('http://hilpisch.com/pyalgo_eikon_eod_data.csv', ...
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