基于LSTM和DDPG的股票交易决策算法OA北大核心CSTPCD

With the development of Artificial Intelligence applications,the optimal automatic stock trading strategy to help investors achieve considerable returns in the volatile financial market has become a research hotspot at present.This paper proposes a stock trading decision-making algorithm LSTM-DDPG(Long Short-Term Memory Network-Deep Deterministic Policy Gradient).This algorithm combines the LSTM network that is better at capturing time series characteristics with the DDPG algorithm that is good at processing high-dimensional spatial data,and adds Dropout operation to reduce overfitting.In order to better grasp the dynamic changes of the market,six classic technical indicators in the stock market are introduced to expand the state space dimension of LSTM-DDPG.At the same time,two reward functions,cumulative return and Sharpe ratio,are used on LSTM-DDPG to provide investors with a variety of investment options.To verify its effectiveness,the proposed algorithm is applied to two kinds of trading tasks:single stock and stock portfolio.The datasets for the investment tasks include the data from both the US market and the Chinese market.The experimental results on multiple evaluation metrics such as cumulative return,Sharpe ratio,and Calmar ratio show that the proposed algorithm performs well in both domestic and foreign markets for the two kinds of investment tasks.