  With the development of artificial intelligence technology, quantitative
trading systems represented by reinforcement learning have emerged in the stock
trading market. The authors combined the deep Q network in reinforcement
learning with the sentiment quantitative indicator ARBR to build a
high-frequency stock trading model for the share market. To improve the
performance of the model, the PCA algorithm is used to reduce the
dimensionality feature vector while incorporating the influence of market
sentiment on the long-short power into the spatial state of the trading model
and uses the LSTM layer to replace the fully connected layer to solve the
traditional DQN model due to limited empirical data storage. Through the use of
cumulative income, Sharpe ratio to evaluate the performance of the model and
the use of double moving averages and other strategies for comparison. The
results show that the improved model proposed by authors is far superior to the
comparison model in terms of income, achieving a maximum annualized rate of
return of 54.5%, which is proven to be able to increase reinforcement learning
performance significantly in stock trading.
