ML Anthology

Reinforcement Learning for Optimized Trade Execution

Yuriy Nevmyvaka, Yi Feng, Michael J. Kearns
ICML 2006 pp. 673-680
doi:10.1145/1143844.1143929 /icml/2006/nevmyvaka2006icml-reinforcement/

Abstract

We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets. Our experiments are based on 1.5 years of millisecond time-scale limit order data from NASDAQ, and demonstrate the promise of reinforcement learning methods to market microstructure problems. Our learning algorithm introduces and exploits a natural low-impact factorization of the state space.

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Cite

Text

Nevmyvaka et al. "Reinforcement Learning for Optimized Trade Execution." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143929

Markdown

[Nevmyvaka et al. "Reinforcement Learning for Optimized Trade Execution." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/nevmyvaka2006icml-reinforcement/) doi:10.1145/1143844.1143929

BibTeX

@inproceedings{nevmyvaka2006icml-reinforcement,
  title     = {{Reinforcement Learning for Optimized Trade Execution}},
  author    = {Nevmyvaka, Yuriy and Feng, Yi and Kearns, Michael J.},
  booktitle = {International Conference on Machine Learning},
  year      = {2006},
  pages     = {673-680},
  doi       = {10.1145/1143844.1143929},
  url       = {https://mlanthology.org/icml/2006/nevmyvaka2006icml-reinforcement/}
}