Reinforcement-Learning-for-Optimized-trade-execution / Reinforcement Learning for Optimized trade execution.pdf Find file Copy path Fetching contributors… The use of these techniques has reduced 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.



context, an area of machine learning called reinforcement learning (RL) can be applied to solve the problem of optimized trade execution. We present the first large-scale empirical application of reinforcement learning to the important problem of optimized trade execution in modern financial markets.
Reinforcement Learning for Optimized Trade Execution Yuriy Nevmyvaka yuriy.nevmyvaka@lehman.com Lehman Brothers, 745 Seventh Av., New York, NY 10019, USA Yi Feng fengyi@cis.upenn.edu Michael Kearns1 mkearns@cis.upenn.edu University of Pennsylvania, Philadelphia, PA 19104, USA ————— Research which have used historical data has so far explored various RL algorithms [8, 9, 10].

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It has been shown in many hedge fund and research labs that this has indeed succeeded in producing consistent profit (for a certain period of time) .

Reinforcement Learning for Optimized trade execution Many research has been done regarding the use of reinforcement learning in optimizing trade execution.