Behavior based learning in identifying High Frequency Trading strategies

Steve Yang, Mark Paddrik, Roy Hayes, Andrew Todd, Andrei Kirilenko, Peter Beling, William Scherer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

24 Scopus citations

Abstract

Electronic markets have emerged as popular venues for the trading of a wide variety of financial assets, and computer based algorithmic trading has also asserted itself as a dominant force in financial markets across the world. Identifying and understanding the impact of algorithmic trading on financial markets has become a critical issue for market operators and regulators. We propose to characterize traders' behavior in terms of the reward functions most likely to have given rise to the observed trading actions. Our approach is to model trading decisions as a Markov Decision Process (MDP), and use observations of an optimal decision policy to find the reward function. This is known as Inverse Reinforcement Learning (IRL), and a variety of approaches for this problem are known. Our IRL-based approach to characterizing trader behavior strikes a balance between two desirable features in that it captures key empirical properties of order book dynamics and yet remains computationally tractable. Using an IRL algorithm based on linear programming, we are able to achieve more than 90% classification accuracy in distinguishing High Frequency Trading from other trading strategies in experiments on a simulated E-Mini S&P 500 futures market. The results of these empirical tests suggest that High Frequency Trading strategies can be accurately identified and profiled based on observations of individual trading actions.

Original languageEnglish
Title of host publication2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012 - Proceedings
Pages133-140
Number of pages8
DOIs
StatePublished - 2012
Event2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012 - New York City, NY, United States
Duration: 29 Mar 201230 Mar 2012

Publication series

Name2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012 - Proceedings

Conference

Conference2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012
Country/TerritoryUnited States
CityNew York City, NY
Period29/03/1230/03/12

Keywords

  • Algorithmic trading
  • High Frequency Trading
  • Inverse Reinforcement Learning
  • Limit order book
  • Markov Decision Process
  • Price impact
  • Spoofing

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