基于LSTM和DDPG的股票交易决策算法
Stock trading decision-making algorithm based on LSTM and DDPG
何杉杉 1周雅兰 1郭宇阳1
作者信息
- 1. 广东财经大学信息学院,广州,510320
- 折叠
摘要
随着人工智能应用的发展,在动荡多变的金融市场中帮助投资者获得可观收益的最优自动股票交易策略成为目前的研究热点.因此,提出了一种股票交易决策算法LSTM-DDPG(Long Short-Term Memory Network-Deep Deterministic Policy Gradient),将擅于捕捉时间序列特征的LSTM网络融入擅于处理高维空间数据的DDPG算法,并加入 Dropout操作来减少过拟合.为了更好地把握市场的动态变化,引入了股票市场中六种经典技术指标来拓展LSTM-DDPG 的状态空间维度.同时,在LSTM-DDPG上使用累计收益和夏普比率两种奖励函数,为投资者提供多种投资方案.为了验证提出的算法的有效性,将该算法应用在单只股票和股票投资组合两种交易任务中,两种投资任务的数据集均包含了美国市场和中国市场的数据.实验结果表明,在两种投资任务的国内外市场中,所提出的算法在累计回报、夏普比率、卡玛比率等多个评价指标上均有良好表现.
Abstract
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.
关键词
深度强化学习/交易决策/DDPG/LSTM/夏普比率/单只股票交易/股票投资组合Key words
deep reinforcement learning/trading decision/DDPG/LSTM/Sharpe ratio/single stock trading/stock portfolio引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据