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.
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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
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DOI: https://doi.org/10.1007/978-3-030-93758-4_21
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