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

@article{Hendricks2014ARL,
  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)},
  year={2014},
  pages={457-464}
}
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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