A reinforcement learning extension to the Almgren-Chriss framework for optimal trade execution

  title={A reinforcement learning extension to the Almgren-Chriss framework for optimal trade execution},
  author={Dieter Hendricks and Diane Wilcox},
  journal={2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr)},
Reinforcement learning is explored as a candidate machine learning technique to enhance existing analytical solutions for optimal trade execution with elements from the market microstructure. Given a volume-to-trade, fixed time horizon and discrete trading periods, the aim is to adapt a given volume trajectory such that it is dynamic with respect to favourable/unfavourable conditions during realtime execution, thereby improving overall cost of trading. We consider the standard Almgren-Chriss… CONTINUE READING

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Y. Nevmyvaka
Feng., M. Kearns. Reinforcement learning for optimal trade execution , Proceedings of the 23rd international conference on machine learning, pp. 673-680 • 2006
View 4 Excerpts
Highly Influenced


R. Almgren
Chriss.Optimal execution of portfolio transactions , Journal of Risk, 3, pp. 5-40 • 2000
View 3 Excerpts
Highly Influenced

Preez.JSE Market Microstructure , MSc Dissertation, University of the Witwatersrand

B. Du
School of Computational and Applied M athematics, • 2013
View 1 Excerpt


H. Degryse
deJong, M. Ravenswaaij, G. Wuyts, Aggressive orders and the resiliency of a limit order market , Review of Finance, 9(2), pp. 201-242 • 2003

Reinforcement learning

P. Dayan, C. Watkins
Encyclopedia of Cognitive Science • 2001
View 2 Excerpts

trading in a dynamic noisy market

D. Vayanos.Strategic
Journal of Finance, • 2001

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