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Negative selection—a new performance measure for automated order execution
Journal of Mathematics in Industry Pub Date : 2021-03-23 , DOI: 10.1186/s13362-021-00102-x
Miles Kumaresan , Nataša Krejić , Sanja Lončar

Automated Order Execution is the dominant way of trading at stock markets. Performance of numerous execution algorithms is measured through slippage from some benchmark. But measuring true slippage in algorithmic execution is a difficult task since the execution as well as benchmarks are function of market activity. In this paper, we propose a new performance measure for execution algorithms. The measure, named Negative Selection, takes a posterior look at the trading window and allows us to determine what would have been the optimal order placement if we knew in advance, before the actual trading, the complete market information during the trading window. We define the performance measure as the difference between the hypothetical optimal trading position and the actual execution. This difference is calculated taking into account all prices and traded quantities within the considered time window. Thus, we are capturing the impact caused by our own trading as a cost that affects all trades. Properties of Negative Selection, which make it well defined and objective are discussed. Some empirical results on real trade data are presented.

中文翻译:

否定选择-一种用于自动执行订单的新绩效衡量指标

自动化订单执行是在股票市场进行交易的主要方式。许多执行算法的性能是通过某些基准测试的延误来衡量的。但是,在算法执行中测量真实滑点是一项艰巨的任务,因为执行以及基准是市场活动的函数。在本文中,我们为执行算法提出了一种新的性能指标。名为负选择(Negative Selection)的衡量标准,是对交易窗口的事后观察,如果我们能够在实际交易之前提前知道交易窗口中的完整市场信息,就可以确定最佳定单的位置。我们将绩效指标定义为假设的最佳交易头寸与实际执行价之间的差额。计算该差异时要考虑到所考虑的时间范围内的所有价格和交易量。因此,我们将自己的交易所产生的影响捕捉为影响所有交易的成本。讨论了否定选择的属性,使之定义明确且客观。提出了一些关于实际贸易数据的实证结果。
更新日期:2021-03-24
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