基于深度强化学习DDPG算法的投资组合管理

将深度强化学习技术应用于投资组合管理,采用深度强化学习中的深度确定性策略梯度DDPG（Deep Deterministic Policy Gradient）算法,通过限制单只股票的投资权重,分散风险,并采用丢弃算法（Dropout）,即在训练模型时随机丢弃节点,解决过拟合问题。以中国股市为例,选取16只中证100指数成分股作为风险资产进行实验。结果表明,本文基于深度强化学习方法构建的投资组合,在实验期间的价值增幅显著高于对照组（等权重组合）,2年达到65%,约为对照组的2.5倍,表明了本文方法的有效性。而且通过进一步实验,表明了当用于训练的数据离测试数据时间越近,则本文构建的投资组合表现越好。

Portfolio Management Based on DDPG Algorithm of Deep Reinforcement Learning

This paper applies DRL(Deep Reinforcement Learning) technology to portfolio management and adopts DDPG (Deep Deterministic Policy Gradient) algorithm. By limiting the weight of individual stock, risk diversification is achieved and by using Dropout, that is, randomly dropping some nodes when training models, over-fitting problems are solved. Taking Chinese stock market as an example, this paper selects 16 of China securities 100 index constituent stocks as risky assets. The experimental results show that the 2-year accumulative return rate of the portfolio constructed in this paper reaches 65%, which is about 2.5 times of that of the control group（a portfolio with weights evenly distributed among the same 16 stocks). This strongly indicates the effectiveness of the method. Moreover, through further experiments, this paper indicates that the closer the data used for training is from the test data, the better the performance of the portfolio constructed in this paper.