Gaussian process-based algorithmic trading strategy identification
Gaussian process-based algorithmic trading strategy identification
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基于高斯过程的算法交易策略识别
DOI：
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发表时间：
2012
期刊：
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通讯作者：
A. Kirilenko
中科院分区：
文献类型：
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作者：
Steve Y. Yang;Qifeng Qiao;P. Beling;W. Scherer;A. Kirilenko
许多市场参与者现在采用算法交易，通常定义为使用计算机算法，自动做出某些交易决策，提交订单并在提交后管理这些订单。识别和理解算法交易对金融市场的影响已成为市场运营商和监管机构的关键问题。市场运营商提供的高级数据和审计跟踪信息现在可以全面观察市场参与者的行动。一个关键问题是，在多大程度上可以从对交易行为的观察中理解和描述个人参与者的行为。在本文中，我们考虑的基本问题的分类和识别交易者（或，等价地，交易算法）的基础上观察到的限价订单。这些问题是感兴趣的监管机构从事战略识别欺诈检测和政策制定的目的。文献中提出了一些方法，利用在特征空间上定义的分类规则来描述交易者的行为，该特征空间包括交易量和库存的汇总统计数据，沿着反映买卖行为一致性的衍生变量。我们的主要贡献是建议一个完全不同的特征空间，通过推断顺序优化模型的关键参数来构建，我们将其作为交易者决策过程的替代品。特别是，我们模型的马尔可夫决策过程中的交易行为。我们使用称为逆强化学习（IRL）的机器学习过程，根据对交易行为的观察来推断该过程的奖励（或目标）函数。然后，通过IRL学习的奖励函数构成了一个特征空间，可以作为监督学习（用于交易者的分类或识别）或无监督学习（用于交易者的分类）的基础。利用E-Mini期货合约的真实数据集，我们比较了两种主要的IRL变体，线性IRL和高斯过程IRL，对基于汇总交易统计的方法。结果表明，基于IRL的特征空间支持准确的分类和有意义的聚类。此外，我们认为，因为他们试图学习交易者的基本价值主张在不同的市场条件下，IRL方法是更翔实和强大的比摘要基于实证的方法，非常适合发现新的行为模式的市场参与者。
Many market participants now employ algorithmic trading, commonly defined as the use of computer algorithms, to automatically make certain trading decisions, submit orders and manage those orders after submission. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. Advanced data feeds and audit trail information from market operators now allow for the full observation of market participants’ actions. A key question is the extent to which it is possible to understand and characterize the behaviour of individual participants from observations of trading actions. In this paper, we consider the basic problems of categorizing and recognizing traders (or, equivalently, trading algorithms) on the basis of observed limit orders. These problems are of interest to regulators engaged in strategy identification for the purposes of fraud detection and policy development. Methods have been suggested in the literature for describing trader behaviour using classification rules defined over a feature space consisting of summary trading statistics of volume and inventory, along with derived variables that reflect the consistency of buying or selling behaviour. Our principal contribution is to suggest an entirely different feature space that is constructed by inferring key parameters of a sequential optimization model that we take as a surrogate for the decision-making process of the traders. In particular, we model trader behaviour in terms of a Markov decision process. We infer the reward (or objective) function for this process from observations of trading actions using a process from machine learning known as inverse reinforcement learning (IRL). The reward functions learned through IRL then constitute a feature space that can be the basis for supervised learning (for classification or recognition of traders) or unsupervised learning (for categorization of traders). Making use of a real-world data-set from the E-Mini futures contract, we compare two principal IRL variants, linear IRL and Gaussian Process IRL, against a method based on summary trading statistics. Results suggest that IRL-based feature spaces support accurate classification and meaningful clustering. Further, we argue that, because they attempt to learn traders’ underlying value propositions under different market conditions, the IRL methods are more informative and robust than the summary statistic-based approach and are well suited for discovering new behaviour patterns of market participants.