November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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通过机器学习预测市场动向
｜
157
当涉及整体策略性能时，基于预测的策略的命中率只是问题的一方面。
另一方面是该策略如何正确地把握市场时机。即使命中率低于
50%
，正
确预测某个时间段内最佳和最差日子的策略也可能跑赢市场。另一方面，
如果命中率远高于
50%
的策略对于罕见的大幅波动把握出错，那它的表
现可能仍然不如基本工具。
5.1.7
概括方法
“线性回归回测类”展示了一个
Python
模块，其中包含一个类，该类按照第
4
章的
精神对基于回归的交易策略进行向量化回测。
它允许线性回归模型的样本内拟合和
样本外评估。这意味着回归模型是根据数据集的一部分（例如
2010
年至
2015
年）
进行拟合的，而基于数据集的另一部分（例如
2016
年和
2019
年）进行评估的。
由于涉及优化或拟合步骤，因此可以从实践中更真实地了解市场表现，因为它有助
于避免由于数据窥探和模型过度拟合而引起的问题（另请参见“数据窥探和过度拟
合”）。
图
5-7
展示了对于特定的配置并且在样本外和考虑交易成本之前，基于五次滞后的
回归策略的性能确实优于的
EUR/USD
基础工具，：
In [52]: import LRVectorBacktester as LR
In [53]: lrbt = LR.LRVectorBacktester('EUR=', '2010-1-1', '2019-12-31',
10000, 0.0)
In [54]: lrbt.run_strategy('2010-1-1', ...
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