Reinforcement Learning for Market Making in a Multi-agent Dealer Market
This is an incremental improvement for financial markets, focusing on automated liquidity provision using RL in simulated multi-agent environments.
The paper tackled the problem of market making in a dealer market by building a multi-agent simulation to train a reinforcement learning agent, showing it learns competitor pricing policies and manages inventory effectively with asymmetric pricing and drift adaptation.
Market makers play an important role in providing liquidity to markets by continuously quoting prices at which they are willing to buy and sell, and managing inventory risk. In this paper, we build a multi-agent simulation of a dealer market and demonstrate that it can be used to understand the behavior of a reinforcement learning (RL) based market maker agent. We use the simulator to train an RL-based market maker agent with different competitive scenarios, reward formulations and market price trends (drifts). We show that the reinforcement learning agent is able to learn about its competitor's pricing policy; it also learns to manage inventory by smartly selecting asymmetric prices on the buy and sell sides (skewing), and maintaining a positive (or negative) inventory depending on whether the market price drift is positive (or negative). Finally, we propose and test reward formulations for creating risk averse RL-based market maker agents.