Learning to simulate realistic limit order book markets from data as a World Agent-Reference-Cited by-同舟云学术

Learning to simulate realistic limit order book markets from data as a World Agent

Published:2022-10-26 Issue: Volume: Page:
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Container-title:3rd ACM International Conference on AI in Finance
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Author:

Coletta Andrea¹,Moulin Aymeric²,Vyetrenko Svitlana¹,Balch Tucker¹

Affiliation:

1. J. P. Morgan Chase, USA

2. Balyasny Asset Management, L.P., USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3533271.3561753

Reference26 articles.

1. Tucker Hybinette Balch Mahmoud Mahfouz Joshua Lockhart Maria Hybinette and David Byrd. 2019. How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?arXiv preprint arXiv:1906.12010(2019). Tucker Hybinette Balch Mahmoud Mahfouz Joshua Lockhart Maria Hybinette and David Byrd. 2019. How to Evaluate Trading Strategies: Single Agent Market Replay or Multiple Agent Interactive Simulation?arXiv preprint arXiv:1906.12010(2019).

2. Jean-Philippe Bouchaud , Julius Bonart , Jonathan Donier , and Martin Gould . 2018. Trades , quotes and prices: financial markets under the microscope . Cambridge University Press . Jean-Philippe Bouchaud, Julius Bonart, Jonathan Donier, and Martin Gould. 2018. Trades, quotes and prices: financial markets under the microscope. Cambridge University Press.

3. ABIDES

4. Carl Chiarella and Giulia Iori . 2002. A simulation analysis of the microstructure of double auction markets. Quantitative finance 2, 5 ( 2002 ), 346. Carl Chiarella and Giulia Iori. 2002. A simulation analysis of the microstructure of double auction markets. Quantitative finance 2, 5 (2002), 346.

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