f15_Trading Strategies2_sma_AAPL_Log return_EUR_OLS regress_df highlight_Lagrang_GaussianNB_DNNClass

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已于 2022-04-25 21:08:31 修改

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分类专栏： pythonForFinance2 文章标签：大数据 big data python

于 2022-04-25 21:06:54 首次发布

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本文探讨了基于简单移动平均线(SMA)的算法交易策略，并通过向量化回测进行验证。同时，文章涉及了线性回归、聚类和深度学习在交易策略中的应用，如高斯朴素贝叶斯、支持向量机和深度神经网络(DNN)。通过随机训练测试拆分评估策略，展示了DNN在样本外的性能。

[T]hey were silly[ˈsɪli]傻瓜，笨蛋 enough to think you can look at the past to predict the future. —The Economist

This chapter is about the vectorized backtesting of algorithmic trading strategies. The term algorithmic trading strategy is used to describe any type of financial trading strategy that is based on an algorithm designed to take long, short, or neutral positions in financial instruments on its own without human interference. A simple algorithm, such as “altering every five minutes between a long and a neutral position in the stock of Apple, Inc.,” satisfies this definition. For the purposes of this chapter and a bit more technically, an algorithmic trading strategy is represented by some Python code t

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