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Optimal order execution is widely studied by industry practitioners and academic researchers because it determines the profitability of investment decisions and high-level trading strategies, particularly those involving large volumes of orders.
Reinforcement learning for optimized trade execution. In Proceedings of the 23rd international conference on Machine learning . 673–680
Yuriy Nevmyvaka, Yi Feng, and Michael Kearns. 2006 · 2006
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LOBSTER: Limit order book reconstruction system
Ruihong Huang and Tomas Polak. 2011 · 2011
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tijmen Tieleman and Geoffrey Hinton. 2012 · 2012
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013 · 2013
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Limit order books
Frédéric Abergel, Marouane Anane, Anirban Chakraborti, Aymen Jedidi, and Ioane Muni Toke. 2016 · 2016
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Deep reinforcement learning with double q-learning. In Thirtieth AAAI conference on artificial intelligence
Hado Van Hasselt, Arthur Guez, and David Silver. 2016 · 2016
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Trades, quotes and prices: financial markets under the microscope
Jean-Philippe Bouchaud, Julius Bonart, Jonathan Donier, and Martin Gould. 2018 · 2018
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Generating Realistic Stock Market Order Streams
Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, and Michael P Wellman. 2018 · 2018
Cited alongside, same era.
Optimizing Market Making using Multi-Agent Reinforcement Learning
Yagna Patel. 2018 · 2018
Cited alongside, same era.
Market making via reinforcement learning. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems . International Foundation for Autonomous Agents and Multiagent Systems, 434–442
Thomas Spooner, John Fearnley, Rahul Savani, and Andreas Koukorinis. 2018 · 2018
Cited alongside, same era.
Mean field multi-agent reinforcement learning
Yaodong Yang, Rui Luo, Minne Li, Ming Zhou, Weinan Zhang, and Jun Wang. 2018 · 2018
Cited alongside, same era.
Abides: Towards high-fidelity market simulation for ai research
David Byrd, Maria Hybinette, and Tucker Hybinette Balch. 2019 · 2019
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Deep Execution-Value and Policy Based Reinforcement Learning for Trading and Beating Market Benchmarks
Kevin Dabérius, Elvin Granat, and Patrik Karlsson. 2019 · 2019
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Learning Fairness in Multi-Agent Systems. In Advances in Neural Information Processing Systems . 13854–13865
Jiechuan Jiang and Zongqing Lu. 2019 · 2019
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Applications of Reinforcement Learning in Automated Market-Making
Mohammad Mani, Steve Phelps, and Simon Parsons. 2019 · 2019
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Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Svitlana Vyetrenko, David Byrd, Nick Petosa, Mahmoud Mahfouz, Danial Dervovic, Manuela Veloso, and Tucker Hybinette Balch. 2019 · 2019
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Tucker Hybinette Balch, Mahmoud Mahfouz, Joshua Lockhart, Maria Hybinette, and David Byrd. 2019 · 2019
Cited alongside, same era.
Fairness in Multi-agent Reinforcement Learning for Stock Trading
Wenhang Bao. 2019 · 2019
Cited alongside, same era.
Multi-agent deep reinforcement learning for liquidation strategy analysis
Wenhang Bao and Xiao-yang Liu. 2019 · 2019
Cited alongside, same era.
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Svitlana Vyetrenko and Shaojie Xu. 2019 · 2019
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