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Market Making via Reinforcement Learning in China Commodity Market

Author
Junshu Jiang, Thomas Dierckx, Duxiang Xiao, Wim schoutens
Date Updated
2022/05/19
Category
q-fin.TR
Date Published
2022/05/18
Date Retrieved
2022/05/19
Description
Market maker is an important role in financial market. A successful market maker should control inventory risk, adverse selection risk, and provides liquidity to the market. Reinforcement Learning, as an important methodology in control problems, enjoys the advantage of data-driven and less rigid assumption, receive great attentions in market making field since 2018. However, although China Commodity market, which has biggest trading volume on agricultural products, nonferrous metals and some other sectors, the study of applies RL on Market Making in China market is still rare. In this thesis, we try to fill the gap. We develop the Automatic Trading System and verify the feasibility of applying Reinforcement Learning in China Commodity market. Also, we probe the agent behavior by analyzing how it reacts to different environment conditions.
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URL
https://arxiv.org/abs/2205.08936
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