  Portfolio management issues have been extensively studied in the field of
artificial intelligence in recent years, but existing deep learning-based
quantitative trading methods have some areas where they could be improved.
First of all, the prediction mode of stocks is singular; often, only one
trading expert is trained by a model, and the trading decision is solely based
on the prediction results of the model. Secondly, the data source used by the
model is relatively simple, and only considers the data of the stock itself,
ignoring the impact of the whole market risk on the stock. In this paper, the
DQN algorithm is introduced into asset management portfolios in a novel and
straightforward way, and the performance greatly exceeds the benchmark, which
fully proves the effectiveness of the DRL algorithm in portfolio management.
This also inspires us to consider the complexity of financial problems, and the
use of algorithms should be fully combined with the problems to adapt. Finally,
in this paper, the strategy is implemented by selecting the assets and actions
with the largest Q value. Since different assets are trained separately as
environments, there may be a phenomenon of Q value drift among different assets
(different assets have different Q value distribution areas), which may easily
lead to incorrect asset selection. Consider adding constraints so that the Q
values of different assets share a Q value distribution to improve results.
