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- W2160519132 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." @default.
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- W2160519132 date "2006-01-01" @default.
- W2160519132 modified "2023-10-16" @default.
- W2160519132 title "Reinforcement learning for optimized trade execution" @default.
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- W2160519132 doi "https://doi.org/10.1145/1143844.1143929" @default.
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