跳至正文
返回文库全部文档

用于诊断订单表现的在线交易成本分析

文章 arXiv papers · 作者: Robert Azencott et al.
阅读原文

总结

本文提出一个实时框架，用于监控算法订单表现，并识别与执行不佳相关的市场状况。无论执行算法是通过随机控制还是统计学习方法构建，该框架都可以独立运行。实践者选择市场环境特征，再为每笔订单推导称为异常检测器的其他解释变量。

在线分析会估计扩展后的因素集合对订单表现不佳的预测能力，并衡量各因素的预测力。作者建议利用这些结果指导干预，例如调整算法参数、暂停订单或采取更直接的控制。他们还描述了将该方法用于交易后分析，以调整未来的交易操作。所提供的文本概述了方法及预期用途，但没有报告实证结果、具体的检测器定义，也没有证据表明干预能改善执行结果。

核心观点

  • 该框架实时分析订单表现，且不依赖执行算法的设计方式。
  • 实践者选择市场环境特征，再据此推导订单级异常检测器。
  • 在线影响分析会估计哪些因素能够预测订单表现不佳及其预测能力。
  • 分析结果可用于指导参数调整、暂停订单或直接干预执行。
  • 所提供的描述没有实证结果或具体检测器规格。

标签

交易执行市场微观结构统计学
全文
# Realtime market microstructure analysis: online Transaction Cost Analysis


# Realtime market microstructure analysis: online Transaction Cost Analysis









Motivated by the practical challenge in monitoring the performance of a large number of algorithmic trading orders, this paper provides a methodology that leads to automatic discovery of the causes that lie behind a poor trading performance. It also gives theoretical foundations to a generic framework for real-time trading analysis. Academic literature provides different ways to formalize these algorithms and show how optimal they can be from a mean-variance, a stochastic control, an impulse control or a statistical learning viewpoint. This paper is agnostic about the way the algorithm has been built and provides a theoretical formalism to identify in real-time the market conditions that influenced its efficiency or inefficiency. For a given set of characteristics describing the market context, selected by a practitioner, we first show how a set of additional derived explanatory factors, called anomaly detectors, can be created for each market order. We then will present an online methodology to quantify how this extended set of factors, at any given time, predicts which of the orders are underperforming while calculating the predictive power of this explanatory factor set. Armed with this information, which we call influence analysis, we intend to empower the order monitoring user to take appropriate action on any affected orders by re-calibrating the trading algorithms working the order through new parameters, pausing their execution or taking over more direct trading control. Also we intend that use of this method in the post trade analysis of algorithms can be taken advantage of to automatically adjust their trading action.

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。