Reinforcement learning for optimized trade execution

Yuriy Nevmyvaka, Yi Feng, Michael Kearns · 2006 · ICML 2006

IntermediateIntradayEmpirical

Early large-scale RL on NASDAQ order book data. Learned policies cut execution costs compared with simple submit-and-leave strategies.

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At a glance

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

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@inproceedings{nevmyvaka2006reinforcement, author = {Nevmyvaka, Yuriy and Feng, Yi and Kearns, Michael}, title = {{Reinforcement learning for optimized trade execution}}, booktitle = {ICML 2006}, year = {2006}, doi = {10.1145/1143844.1143929} }