November 2022
Intermediate to advanced
392 pages
10h 8m
Chinese
Content preview from Python算法交易
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316
｜
第
10
章
99.00 244.593
97.50 232.674
95.00 125.498
90.00 61.701
将数据从
10
分钟的条形重新采样到
1
小时的条形。
在给定百分位值的情况下计算
VaR
值。
10.2.4
持久化模型对象
经过回测，分析杠杆和风险，如果这些都没有问题，就可以接受这个算法交易策略了，
可以持久化模型对象和其他相关算法组件，以便在以后部署中使用。现在，它就可
以体现基于机器学习的交易策略或交易算法。
In [107]: import pickle
In [108]: algorithm = {'model': model, 'mu': mu, 'std': std}
In [109]: pickle.dump(algorithm, open('algorithm.pkl', 'wb'))
10.3
实时算法
到目前为止测试的交易算法是一种离线算法。这样的算法使用完整的数据集来解决
眼前的问题。问题的关键在于训练一种基于决策树的
AdaBoost
分类算法，以该决
策树作为基础分类器，再加上许多不同的时间序列特征和方向标签数据。实际上，
在金融市场中部署交易算法时，它必须逐个消费数据，以预测下一个时间间隔（
bar
）
的市场走势。本节使用上一节中的持久化模型对象，并将其嵌入到流数据的上下文
中。
将离线交易算法转换为实时交易算法的代码主要解决以下问题： ...
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