Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market
This addresses the problem of liquidity provision in financial markets for traders and market operators, representing an incremental application of existing RL methods to a new domain.
This paper tackles the problem of creating intelligent market makers in simulated stock markets using Reinforcement Learning (RL), showing that RL and deep RL techniques are profitable approaches that lead to better understanding of market maker behavior.
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 markets.