We introduce new machine learning methods for clustering traders who are actively trading in a modern electronic exchange which uses a matching engine to track aggregate and individual-level limit order books. Each trader's individual limit order book is centered (with the current best bid and ask prices acting as a central reference), and the patterns in the individual limit order books are identified using a Wasserstein distance. The method is illustrated using simulated limit order book data and limit order book data from a stock exchange in Canada.

中文翻译：

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一种用于金融市场交易者分类的机器学习方法

我们为在现代电子交易所中积极交易的交易者引入了新的机器学习方法，该交易使用匹配引擎来跟踪聚合和个人级别的限价订单簿。每个交易者的个人限价订单簿都是居中的（当前的最佳买入价和卖出价作为中心参考），并且个人限价订单簿中的模式使用 Wasserstein 距离来识别。该方法使用来自加拿大证券交易所的模拟限价订单簿数据和限价订单簿数据进行说明。