Constructing A Stock Trading Agent Based on Deep Reinforcement Learning
Constructing stable and profitable trading strategies in the complex and ever-changing financial market environ-ment poses a significant challenge.Traditional portfolio methods fail to adapt promptly to rapid market changes due to their reli-ance on assumptions about market efficiency and normally distributed returns,which do not accurately reflect the dynamic market environment.Addressing these challenges,this paper investigated the effective integration of DRL algorithms with financial trading scenarios,exploring the potential of DRL algorithms for optimizing stock trading strategies to achieve consistent profitabili-ty.By selecting constituents of the SSE 50 Index as the trading instruments and incorporating technical indicators such as stock prices,trading volumes,and moving averages to construct the trading market environment,the study meticulously designed state spaces,action spaces,and objective functions.It employed DRL algorithms,including DDPG,SAC,and TD3,to train a highly adaptive trading agent.The results showed that this stock trading agent exhibits significant advantages in profitability and risk control compared to traditional portfolio strategies.
stock trading strategydeep reinforcement learningtrading agentinvestment portfoliorisk control
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维普
