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With the application of artificial intelligence in the financial field,quantitative trading is considered to be profitable. Based on this, this paperproposes an improved deep recurrent DRQN-ARBR model because the existingquantitative trading model ignores the impact of irrational investor behavioron the market, making the application effect poor in an environment where thestock market in China is non-efficiency. By changing the fully connected layerin the original model to the LSTM layer and using the emotion indicator ARBR toconstruct a trading strategy, this model solves the problems of the traditionalDQN model with limited memory for empirical data storage and the impact ofobservable Markov properties on performance. At the same time, this paper alsoimproved the shortcomings of the original model with fewer stock states andchose more technical indicators as the input values of the model. Theexperimental results show that the DRQN-ARBR algorithm proposed in this papercan significantly improve the performance of reinforcement learning in stocktrading.

Improved Method of Stock Trading under Reinforcement Learning Based on DRQN and Sentiment Indicators ARBR