  With the application of artificial intelligence in the financial field,
quantitative trading is considered to be profitable. Based on this, this paper
proposes an improved deep recurrent DRQN-ARBR model because the existing
quantitative trading model ignores the impact of irrational investor behavior
on the market, making the application effect poor in an environment where the
stock market in China is non-efficiency. By changing the fully connected layer
in the original model to the LSTM layer and using the emotion indicator ARBR to
construct a trading strategy, this model solves the problems of the traditional
DQN model with limited memory for empirical data storage and the impact of
observable Markov properties on performance. At the same time, this paper also
improved the shortcomings of the original model with fewer stock states and
chose more technical indicators as the input values of the model. The
experimental results show that the DRQN-ARBR algorithm proposed in this paper
can significantly improve the performance of reinforcement learning in stock
trading.
