Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Prediction, Learning, and Games
Prediction, Learning, and Games
Censored exploration and the Dark Pool Problem
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
An assessment of strategies for choosing between competitive marketplaces
Electronic Commerce Research and Applications
Optimal Split of Orders Across Liquidity Pools: A Stochastic Algorithm Approach
SIAM Journal on Financial Mathematics
| Hi-index | 48.22 |
Dark pools are a recent type of stock exchange in which information about outstanding orders is deliberately hidden in order to minimize the market impact of large-volume trades. The success and proliferation of dark pools have created challenging and interesting problems in algorithmic trading---in particular, the problem of optimizing the allocation of a large trade over multiple competing dark pools. In this work, we formalize this optimization as a problem of multi-venue exploration from censored data, and provide a provably efficient and near-optimal algorithm for its solution. Our algorithm and its analysis have much in common with well-studied algorithms for managing the exploration--exploitation trade-off in reinforcement learning. We also provide an extensive experimental evaluation of our algorithm using dark pool execution data from a large brokerage.