Portfolio Management with Reinforcement Learning
Portfolio management is a crucial trading task for investment companies in the market. In this work, reinforcement learning (RL) incorporating the transformer structure is combined with deep learning (DL) to build an automated portfolio management model. The proposed method uses the Sharpe ratio along with transaction cost as the reward and build an environment that contains the whole A-share market to train the RL agent. The result demonstrates that the trained strategy outperforms The Shanghai Composite Index.
PDF Abstract
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
