November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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处理金融数据
｜
95
本章涵盖的另一个重要主题是将完整的
DataFrame
对象有效地存储在磁盘上，以
及将此类内存对象中包含的数据存储到数据库中。本章中使用的数据库类型包括
HDF5
数据库标准和轻量级关系数据库
SQLite3
。本章奠定了第
4
章的基础，第
4
章
讨论了向量化回测。第
5
章涵盖了机器学习和深度学习以进行市场预测。第
6
章讨
论了基于事件的交易策略回测。
3.6
参考资料和延伸资源
你可以在以下链接中找到有关
Quandl
的更多信息：
•
http://quandl.org
可在以下位置找到有关用于从
Quandl
获取数据的软件包的信息：
• Quandl
上的
Python
包页面（
https://www.quandl.com/tools/python
）。
• Quandl Python
封装包的
Github
页 面（
https://github.com/quandl/quandl-
python
）。
更多关于本章用到的程序包的信息，你可以参考官方文档页面。
•
pandas
的主页（
http://pandas.pydata.org
）。
•
PyTables
的主页（
http://pytables.org
）。
• GitHub
上
TsTables
的
fork
（
https://github.com/yhilpisch/tstables
）。
•
SQLite
的主页（
http://sqlite.org
）。
本章引用到的书和文章：
Hilpisch, Yves. 2018.
Python for Finance: Mastering ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.