November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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188
｜
第
5
章
5.4
小结
预测市场的未来走势是金融界的目标。它意味着找出真相，同时也意味着要战胜有
效的市场。如果能够以相当大的优势做到这一点，带来的就是出色的投资和交易回
报。本章介绍了传统统计学，机器学习和深度学习领域的统计技术，以根据过去的
收益或相似的金融数量预测未来的市场方向。对于线性回归和逻辑回归，一些最初
的样本内结果是有希望的。但是，在样本外评估此类策略并考虑交易成本时，会获
得更可靠的结果。
本章并不声称找到了目标。相反，它提供了在探寻目标过程中证明有用的一些技术。
scikit-learn
的统一
API
还可以轻松地将一个线性模型替换为另一个线性模型。从
这个意义上讲，
ScikitBacktesterClass
可以用作探索更多机器学习模型并将其应
用于金融时间序列预测的起点。
在本章开始引用了
1991
年的电影《终结者
2
》，它对于计算机在多快、多大程度上
能够学习和获得意识方面比较乐观。无论你是否相信计算机将在生活的大多数领域
中取代人类，或者确实有一天变得有自我意识，它们已被证明可作为生活中任何领
域的支持设备对人类发挥作用。像机器学习，深度学习或人工智能中使用的算法一
样，至少具有使它们在不久的将来成为更好的算法交易者的希望。有关这些主题和
注意事项的详细说明，请参见
Hilpisch
（
2020
）。
5.5
参考资料和延伸资源
Guido
、
Müller
（
2016
）和
VanderPlas
（
2016
）的书为使用
Python
和
scikit-learn
的机器学习提供了实用的介绍。
Hilpisch
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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.