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聚类限价订单事件以构建订单流信号

文章 arXiv papers · 作者: Yichi Zhang et al.
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总结

ClusterLOB 使用六个随时间变化的特征，对逐委托数据中的单个市场事件进行分组，并采用 K-means++ 聚类。研究将所得分组解释为方向性、机会型和做市型参与者，从而提供一种研究限价订单簿内不同交易行为的方法。

研究使用覆盖小、中、大最小变动价位股票的一年NASDAQ数据。研究按30分钟间隔测量各聚类的订单流失衡，并通过交易策略评估信号，包括将聚类信号与训练集和测试集上的非聚类基准进行比较。研究还分析新增订单、撤单和成交的失衡信号。报告称，在测试集上，依据训练数据夏普比率选出的策略表现更好，但本文未提供具体表现数据，也未进一步证明结果能否推广至该数据集之外。

核心观点

  • 逐委托数据中的市场事件在聚类前以六个随时间变化的特征表示。
  • K-means++ 将事件分为三组，分别解释为方向性、机会型和做市型行为。
  • 研究按30分钟间隔计算聚类层面的订单流失衡，作为交易信号。
  • 研究比较了使用这些信号的策略与基准，并区分训练数据和测试数据。
  • 研究分别分析新增订单、撤单和成交的失衡。

标签

股票市场微观结构机器学习高频交易回测
全文
# ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books


# ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books









In the rapidly evolving world of financial markets, understanding the dynamics of limit order book (LOB) is crucial for unraveling market microstructure and participant behavior. We introduce ClusterLOB as a method to cluster individual market events in a stream of market-by-order (MBO) data into different groups. To do so, each market event is augmented with six time-dependent features. By applying the K-means++ clustering algorithm to the resulting order features, we are then able to assign each new order to one of three distinct clusters, which we identify as directional, opportunistic, and market-making participants, each capturing unique trading behaviors. Our experimental results are performed on one year of MBO data containing small-tick, medium-tick, and large-tick stocks from NASDAQ. To validate the usefulness of our clustering, we compute order flow imbalances across each cluster within 30-minute buckets during the trading day. We treat each cluster's imbalance as a signal that provides insights into trading strategies and participants' responses to varying market conditions. To assess the effectiveness of these signals, we identify the trading strategy with the highest Sharpe ratio in the training dataset, and demonstrate that its performance in the test dataset is superior to benchmark trading strategies that do not incorporate clustering. We also evaluate trading strategies based on order flow imbalance decompositions across different market event types, including add, cancel, and trade events, to assess their robustness in various market conditions. This work establishes a robust framework for clustering market participant behavior, which helps us to better understand market microstructure, and inform the development of more effective predictive trading signals with practical applications in algorithmic trading and quantitative finance.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。