Work2006

Reinforcement learning for optimized trade execution

Yuriy Nevmyvaka · Yi Feng · Michael J. Kearns

267 citations0/0 files storedInternational Conference on Machine Learning

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Financial Markets and Investment Strategies Stochastic processes and financial applications Stock Market Forecasting Methods

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. 1.

Citations in this library

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References

2001Optimal execution of portfolio transactions 1998Optimal control of execution costs

Cited by

2019DeepLOB: Deep Convolutional Neural Networks for Limit Order Books 2023Recent advances in reinforcement learning in finance 2018Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning 2021Universal features of price formation in financial markets: perspectives from deep learning 2020Continuous‐time mean–variance portfolio selection: A reinforcement learning framework 2022A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets 2018Trading financial indices with reinforcement learning agents 2017Transaction cost optimization for online portfolio selection

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