November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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Python
和算法交易
｜
15
可以肯定地说，
NumPy
为
Python
在科学和金融领域的成功做出了巨大贡献。许多来
自所谓的科学
Python
技术栈中的其他流行
Python
软件包都基于
NumPy
构建，用它
来作为一种高效的数据结构来存储和处理数字数据。实际上，
NumPy
是
SciPy
软件
包项目的产物，它提供了科学场景中经常需要的大量功能。
SciPy
项目认识到需要
更强大的数值数据结构，并将该领域中较老的项目如
Numeric
和
NumArray
整合为了
一个新的，以
NumPy
形式统一的项目。
在算法交易中，蒙特卡洛模拟可能不是编程语言最重要的例子。但是，在算法交易
的世界里，对更大甚至巨大的金融时序数据集进行管理就是一个非常重要的例子。
比如（盘中）交易策略的回测或交易时段内的报价数据流的处理。这正是
pandas
数
据分析包（
http://pandas.pydata.org
）发挥作用的地方。
1.1.3 pandas
和
DataFrame
类
pandas
由韦斯
·
麦金尼（
Wes McKinney
）于
2008
年开发，当时他在
AQR
资本管
理公司（
AQR Capital Management
）工作，这是一家位于康涅狄格州格林威治的大
型对冲基金。就像他任职的其他任何一家对冲基金一样，处理时序数据对于
AQR
Capital Management
来说至关重要，但是那个时代的
Python
并没有为此类数据提供
任何吸引人的支持。韦斯的想法是创建一个模仿该领域
R
统计语言（
http://r-project.
org
）功能的软件包。主要类 ...
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