Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market. (arXiv:2112.04494v1 [cs.LG])

Market makers play a key role in financial markets by providing liquidity.
They usually fill order books with buy and sell limit orders in order to
provide traders alternative price levels to operate. This paper focuses
precisely on the study of these markets makers strategies from an agent-based
perspective. In particular, we propose the application of Reinforcement
Learning (RL) for the creation of intelligent market markers in simulated stock
markets. This research analyzes how RL market maker agents behaves in
non-competitive (only one RL market maker learning at the same time) and
competitive scenarios (multiple RL market markers learning at the same time),
and how they adapt their strategies in a Sim2Real scope with interesting
results. Furthermore, it covers the application of policy transfer between
different experiments, describing the impact of competing environments on RL
agents performance. RL and deep RL techniques are proven as profitable market
maker approaches, leading to a better understanding of their behavior in stock



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