Market Making via Reinforcement Learning

Thomas Spooner, John Fearnley, Rahul Savani, Andreas Koukorinis · 2018 · AAMAS 2018

IntermediateHFTEmpiricalFree to read

An RL agent with a carefully shaped reward (asymmetrically dampened PnL) learns a competitive quoting policy on LOB data.

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At a glance

Level: Intermediate: Comfortable with statistics, regressions and basic probability models
Horizon: HFT
Data needed: L2 depth (every update) Set what data you have
Themes: Market making Machine learning
Assets: Equities
Market: Global

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@inproceedings{spooner2018market,
  author = {Spooner, Thomas and Fearnley, John and Savani, Rahul and Koukorinis, Andreas},
  title = {{Market Making via Reinforcement Learning}},
  booktitle = {AAMAS 2018},
  year = {2018},
  doi = {10.65109/yyqw8667}
}

Spooner, T., Fearnley, J., Savani, R., & Koukorinis, A. (2018). Market Making via Reinforcement Learning. AAMAS 2018. https://doi.org/10.65109/yyqw8667