Simulated RL market makers with dynamic inventory penalties and Pareto-front multi-objective training outperform baseline market makers, and a discounted Thompson sampling policy switcher handles non-stationary markets.
A simple approach to arbitrage pricing theory,
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Market Making Strategies with Reinforcement Learning
Simulated RL market makers with dynamic inventory penalties and Pareto-front multi-objective training outperform baseline market makers, and a discounted Thompson sampling policy switcher handles non-stationary markets.