airl-market-making
Unverified ML strategy on Multi by JurajZelman. BotFinder score 18 out of 100.
The official repository for the paper Adversarial Inverse Reinforcement Learning for Market Making (2024) published and presented at the ICAIF'24 conference.
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airl-market-making
This repository contains the code for the paper Adversarial Inverse Reinforcement Learning for Market Making (2024) by Juraj Zelman (Richfox Capital & ETH Zürich), Martin Stefanik (Richfox Capital & ETH Zürich), Moritz Weiß (ETH Zürich) and Prof. Dr. Josef Teichmann (ETH Zürich). The paper was published and presented at the 5th ACM International Conference on AI in Finance (ICAIF ’24) in New York, USA. The links of both affiliations: Richfox Capital and ETH Zürich (Dept. of Mathematics). The full training pipeline can be found in main.ipynb. Beforehand, see the Installation section below. We hope you will learn something new and that this project sparks your curiosity to explore reinforcement learning methods, as well as other related fields! Abstract In this paper, we propose a novel application of the Adversarial Inverse Reinforcement Learning (AIRL) algorithm combining the framework of generative adversarial networks and inverse reinforcement learning for automated reward acquisition in the market making problem. We demonstrate that this algorithm can be used to learn a pure marke
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