Research on dynamic multi-strategy integration of quantitative trading based on deep reinforcement learning

In this article, we introduce a framework called Financial Strategy Reinforcement Learning (FSRL) to address the challenges posed by dynamic and complex financial markets, which relies on modeling multi-strategy choices as Markov decision processes (MDP) to dynamically adapt to a wide variety of market conditions and optimize risk and return. This study combines a finite state machine (FSM) with a standardized backtest environment, which has the benefit of ensuring that the robustness and repeatability of the strategy can be evaluated. We conducted validation experiments using major market indexes in China and the United States, and the results of the experiments show that the FSRL framework has advantages over traditional methods. It achieves a higher Sharpe ratio, a higher return, and a better risk control effect. This research provides a scalable and innovative solution for promoting quantitative trading and dynamic strategy integration.