Abstract
We propose an architecture for algorithmic trading agents for liquidity provisions on centralized exchanges. These implement what we call an adaptive market making multi-strategy, which is based on a limit order grid with continuous experiential learning. The concept exploits definitions of artificial general intelligence (AGI) as an ability to “reach complex goals in complex environments given limited resources”, and is treated as a universal multi-parameter optimization. We present basic reference on implementation of the architecture being back-tested on historical crypto-finance market data and capable of providing almost 1000% excess return (“alpha”) under evaluated market conditions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ganesh S., et. al.: Reinforcement Learning for Market Making in a Multi-agent Dealer Market (2019). arXiv:1911.05892v1, https://arxiv.org/pdf/1911.05892.pdf, Accessed 14 Nov 2019
Sadighian J.: Deep Reinforcement Learning in Cryptocurrency Market Making (2019). arXiv:1911.08647v1, https://arxiv.org/pdf/1911.08647.pdf, Accessed 20 Nov 2019
Sadighian J.: Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making (2020). arXiv:2004.06985v1, https://arxiv.org/pdf/2004.06985.pdf, Accessed 15 Apr 2020
Guéant O., et al.: Dealing with the Inventory Risk. A solution to the market making problem (2020). arXiv:1105.3115, https://arxiv.org/pdf/1105.3115.pdf, Accessed 3 Aug 2012
Tsantekidis A.: Using Deep Learning for price prediction by exploiting stationary limit order book features (2018). arXiv:1810.09965, https://arxiv.org/abs/1810.09965, Accessed 23 Oct 2018
Yanjun C., et al.: Financial Trading Strategy System Based on Machine Learning. Hindawi Math. Prob. Eng. 2020, 13 (2020). Article ID 3589198. https://doi.org/10.1155/2020/3589198
Raheman A., et al.: Architecture of Automated Crypto-Finance Agent (2021). arXiv:2107.07769, https://arxiv.org/abs/2107.07769, Accessed 16 Jul 2021
Goertzel B.: Artificial general intelligence: concept, state of the art, and future prospects. J. Artif. Gener. Intell. 5(1), 1–46 (2014). https://doi.org/10.2478/jagi-2014-0001
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Raheman, A., Kolonin, A., Ansari, I. (2022). Adaptive Multi-strategy Market Making Agent. In: Goertzel, B., Iklé, M., Potapov, A. (eds) Artificial General Intelligence. AGI 2021. Lecture Notes in Computer Science(), vol 13154. Springer, Cham. https://doi.org/10.1007/978-3-030-93758-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-93758-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93757-7
Online ISBN: 978-3-030-93758-4
eBook Packages: Computer ScienceComputer Science (R0)