Machine Learning for Market Microstructure and High Frequency Trading
Machine Learning for Market Microstructure and High Frequency Trading
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市场微观结构和高频交易的机器学习
DOI：
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发表时间：
2013
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通讯作者：
Yuriy Nevmyvaka
中科院分区：
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作者：
Michael Kearns;Yuriy Nevmyvaka
在本章中，我们将概述机器学习在高频交易和市场微观结构数据和问题中的应用。机器学习是计算机科学的一个充满活力的子领域，它借鉴了统计学，算法，计算复杂性，人工智能，控制理论和各种其他学科的模型和方法。它的主要重点是计算和信息高效的算法，用于从大型数据集推断出良好的预测模型，因此是应用于HFT中出现的问题的自然候选者，无论是交易执行还是alpha的生成。从历史数据中推断预测模型显然在定量金融中并不新鲜;普遍存在的例子包括CAPM、法马和法国因子[5]的系数估计以及相关方法。HFT给机器学习带来的特殊挑战通常来自于数据的非常精细的粒度-通常是在单个订单、（部分）执行、隐藏的流动性和取消的解析时的微观结构数据-以及缺乏对这些低级数据如何与可操作情况（例如有利可图地购买或出售股票，最佳地执行大订单等）相关的理解。在机器学习的语言中，CAPM及其变体等模型已经规定了用于预测或建模的相关变量或“特征”（超额收益，账面市值比等），在许多高频交易问题中，人们可能没有关于订单簿中流动性的分布如何与未来价格变动相关的先验直觉，如果有的话。因此，特征选择或特征工程成为HFT机器学习的重要过程，也是我们的中心主题之一。由于高频交易本身是一个相对较新的现象，很少有关于机器学习应用于高频交易的出版作品。出于这个原因，我们围绕我们自己工作中的一些案例研究来构建本章[6，14]。在每一个案例研究中，我们都关注我们想要解决或优化的特定交易问题;我们希望解决这个问题的（微观结构）数据;从数据中导出的变量或特征作为机器学习过程的输入;以及应用于这些特征的机器学习算法。我们将研究的案例包括：
In this chapter, we overview the uses of machine learning for high frequency trading and market microstructure data and problems. Machine learning is a vibrant subfield of computer science that draws on models and methods from statistics, algorithms, computational complexity, artificial intelligence, control theory, and a variety of other disciplines. Its primary focus is on computationally and informationally efficient algorithms for inferring good predictive models from large data sets, and thus is a natural candidate for application to problems arising in HFT, both for trade execution and the generation of alpha. The inference of predictive models from historical data is obviously not new in quantitative finance; ubiquitous examples include coefficient estimation for the CAPM, Fama and French factors [5], and related approaches. The special challenges for machine learning presented by HFT generally arise from the very fine granularity of the data — often microstructure data at the resolution of individual orders, (partial) executions, hidden liquidity, and cancellations — and a lack of understanding of how such low-level data relates to actionable circumstances (such as profitably buying or selling shares, optimally executing a large order, etc.). In the language of machine learning, whereas models such as CAPM and its variants already prescribe what the relevant variables or “features” are for prediction or modeling (excess returns, book-to-market ratios, etc.), in many HFT problems one may have no prior intuitions about how (say) the distribution of liquidity in the order book relates to future price movements, if at all. Thus feature selection or feature engineering becomes an important process in machine learning for HFT, and is one of our central themes. Since HFT itself is a relatively recent phenomenon, there are few published works on the application of machine learning to HFT. For this reason, we structure the chapter around a few case studies from our own work [6, 14]. In each case study, we focus on a specific trading problem we would like to solve or optimize; the (microstructure) data from which we hope to solve this problem; the variables or features derived from the data as inputs to a machine learning process; and the machine learning algorithm applied to these features. The cases studies we will examine are: