November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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174
｜
第
5
章
图 5-17：GDX ETF 的总体表现和基于逻辑回归的策略（10 个滞后，样本外，含交易成本）
将先进的机器学习技术应用于股票市场预测通常会在早期获得可喜的结
果。在几个示例中，回测的策略在样本中显著胜过基本工具。通常，如
此出色的表现是由于各种简化的假设以及预测模型的过拟合所致。例如，
对样本外而不是样本内数据集，测试完全相同的策略，并增加交易成本
（这是获得更真实结果图的两种方式），通常表明，考虑这些后，策略
的性能“突然地”落后于基本工具的性能，或者变成净亏损。
5.3
使用深度学习进行市场走势预测
从
Google
开源和公开发布开源深度学习库
TensorFlow
（
http://tensorflow.org
）开
始，它就引起了广泛的兴趣和应用。本节使用
TensorFlow
的方式与上一节
scikit-
learn
相同，都将应用于作为分类问题建模来预测股票市场动向。但是，没有直接
使用
TensorFlow
。相反，它是通过同样受欢迎的
Keras
（
http://keras.io
）深度学习
程序包使用的。
Keras
可以被认为是通过更易于理解和使用的
API
为
TensorFlow
软
件包提供了更高层次的抽象。
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