- Adam Grimes. The art and science of technical analysis: market structure, price action, and trading strategies, volume 544. John Wiley & Sons, 2012.
Paper not yet in RePEc: Add citation now
Angelo Ranaldo. Order aggressiveness in limit order book markets. Journal of Financial Markets, 7(1):53â74, 2004.
Anna A Obizhaeva and Jiang Wang. Optimal trading strategy and supply/demand dynamics. Journal of Financial Markets, 16(1):1â32, 2013.
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Åukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
Paper not yet in RePEc: Add citation now
- Åukasz KidzinÌski, Sharada Prasanna Mohanty, Carmichael F Ong, Zhewei Huang, Shuchang Zhou, Anton Pechenko, Adam Stelmaszczyk, Piotr Jarosik, Mikhail Pavlov, Sergey Kolesnikov, et al. Learning to run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments. In The NIPSâ17 Competition: Building Intelligent Systems, pages 121â153. Springer, 2018.
Paper not yet in RePEc: Add citation now
- Brian Ning, Franco Ho Ting Lin, and Sebastian Jaimungal. Double deep q-learning for optimal execution. arXiv preprint arXiv:1812.06600, 2018.
Paper not yet in RePEc: Add citation now
Charles Cao, Oliver Hansch, and Xiaoxin Wang. The information content of an open limitorder book. Journal of Futures Markets: Futures, Options, and Other Derivative Products, 29(1):16â41, 2009.
Charles MC Lee and Mark J Ready. Inferring trade direction from intraday data. The Journal of Finance, 46(2):733â746, 1991.
- Colin Lea, Michael D Flynn, Rene Vidal, Austin Reiter, and Gregory D Hager. Temporal convolutional networks for action segmentation and detection. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 156â165, 2017.
Paper not yet in RePEc: Add citation now
- David Byrd, Maria Hybinette, and Tucker Hybinette Balch. Abides: Towards high-fidelity multiagent market simulation. In Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, pages 11â22, 2020.
Paper not yet in RePEc: Add citation now
Dimitris Bertsimas and Andrew W Lo. Optimal control of execution costs. Journal of Financial Markets, 1(1):1â50, 1998.
- Hado Van Hasselt, Arthur Guez, and David Silver. Deep reinforcement learning with double q-learning. In Proceedings of the AAAI conference on artificial intelligence, volume 30, 2016.
Paper not yet in RePEc: Add citation now
Hendrik Bessembinder. The degree of price resolution and equity trading costs. Journal of Financial Economics, 45(1):9â34, 1997.
- Henrik Hult and Jonas Kiessling. Algorithmic trading with markov chains. 2010.
Paper not yet in RePEc: Add citation now
Hua He and Harry Mamaysky. Dynamic trading policies with price impact. Journal of Economic Dynamics and Control, 29(5):891â930, 2005.
James P Weston. Competition on the nasdaq and the impact of recent market reforms. The Journal of Finance, 55(6):2565â2598, 2000.
Jin Fang, Michaël Karpe, Zhongyao Ma, and Chen Wang. Multi-agent reinforcement learning in a realistic limit order book market simulation. In Proceedings of the First ACM International Conference on AI in Finance, ICAIF â20, New York, NY, USA, 2020. Association for Computing Machinery.
Jinliang Li and Chunchi Wu. Daily return volatility, bid-ask spreads, and information flow: Analyzing the information content of volume. The Journal of Business, 79(5):2697â2739, 2006.
- Joel Hasbrouck. Securities trading: Principles and procedures. Manuscript, version, 12, 2017.
Paper not yet in RePEc: Add citation now
- John J Murphy. Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. Penguin, 1999.
Paper not yet in RePEc: Add citation now
- John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
Paper not yet in RePEc: Add citation now
- John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust region policy optimization. In International conference on machine learning, pages 1889â1897. PMLR, 2015.
Paper not yet in RePEc: Add citation now
- Jon Danielsson and Richard Payne. Measuring and explaining liquidity on an electronic limit order book: evidence from reuters d2000-2. Available at SSRN 276541, 2001.
Paper not yet in RePEc: Add citation now
- Joonho Lee, Jemin Hwangbo, Lorenz Wellhausen, Vladlen Koltun, and Marco Hutter. Learning quadrupedal locomotion over challenging terrain. Science robotics, 5(47):eabc5986, 2020.
Paper not yet in RePEc: Add citation now
Kalman J Cohen, Steven F Maier, Robert A Schwartz, and David K Whitcomb. Transaction costs, order placement strategy, and existence of the bid-ask spread. Journal of political economy, 89(2):287â305, 1981.
- Kevin Dabérius, Elvin Granat, and Patrik Karlsson. Deep execution-value and policy based reinforcement learning for trading and beating market benchmarks. Available at SSRN 3374766, 2019.
Paper not yet in RePEc: Add citation now
Marco Avellaneda, Josh Reed, and Sasha Stoikov. Forecasting prices from level-i quotes in the presence of hidden liquidity. Algorithmic Finance, 1(1):35â43, 2011.
- Martin D Gould and Julius Bonart. Queue imbalance as a one-tick-ahead price predictor in a limit order book. Market Microstructure and Liquidity, 2(02):1650006, 2016.
Paper not yet in RePEc: Add citation now
Matthias Schnaubelt. Deep reinforcement learning for the optimal placement of cryptocurrency limit orders. European Journal of Operational Research, 296(3):993â1006, 2022.
- Meng Li, William Hsu, Xiaodong Xie, Jason Cong, and Wen Gao. Sacnn: Self-attention convolutional neural network for low-dose ct denoising with self-supervised perceptual loss network. IEEE transactions on medical imaging, 39(7):2289â2301, 2020.
Paper not yet in RePEc: Add citation now
- Michael S Piwowar and Li Wei. The sensitivity of effective spread estimates to tradeâquote matching algorithms. Electronic Markets, 16(2):112â129, 2006.
Paper not yet in RePEc: Add citation now
- Robert Almgren and Neil Chriss. Optimal execution of portfolio transactions. Journal of Risk, 3:5â40, 2001.
Paper not yet in RePEc: Add citation now
Roger D Huang and Hans R Stoll. Dealer versus auction markets: A paired comparison of execution costs on nasdaq and the nyse. Journal of Financial economics, 41(3):313â357, 1996.
- Roy Fox, Ari Pakman, and Naftali Tishby. Taming the noise in reinforcement learning via soft updates. arXiv preprint arXiv:1512.08562, 2015.
Paper not yet in RePEc: Add citation now
- Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.
Paper not yet in RePEc: Add citation now
- Xin Guo, Adrien De Larrard, and Zhao Ruan. Optimal placement in a limit order book: an analytical approach. Mathematics and Financial Economics, 11(2):189â213, 2017.
Paper not yet in RePEc: Add citation now
- Xin Guo, Tze Leung Lai, Howard Shek, and Samuel Po-Shing Wong. Quantitative trading: algorithms, analytics, data, models, optimization. Chapman and Hall/CRC, 2017.
Paper not yet in RePEc: Add citation now
- Yuriy Nevmyvaka, Yi Feng, and Michael Kearns. Reinforcement learning for optimized trade execution. In Proceedings of the 23rd international conference on Machine learning, pages 673â680, 2006.
Paper not yet in RePEc: Add citation now