Trading in the Bond Market Using Reinforcement Learning

We develop a Reinforcement Learning framework for trading in the Canadian bond market using a Deep Q-Network (DQN). Unlike traditional forecasting models, our agent learns dynamic trading strategies by interacting with the market environment and optimizing long-term returns. Evaluated across multiple regimes and benchmarked against standard methods, the RL agent demonstrates adaptive behavior and outperforms static approaches in capturing profitable bond trading opportunities.