论文标题
一位新颖的专家建议使用深入的强化学习用于投资组合管理的汇总框架
A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management
论文作者
论文摘要
几年来，使用深入的加强学习解决投资组合管理问题一直在财务上引起了很多关注。我们已经提出了一种使用专家信号和历史价格数据的新方法，以进食我们的强化学习框架。尽管据我们所知，但专家信号已在以前的金融领域中使用，但这是该方法首次与Deep RL同时解决金融投资组合管理问题。我们提出的框架由一个用于汇总信号的卷积网络，另一个用于历史价格数据的卷积网络以及一个香草网络。我们使用近端政策优化算法作为处理奖励并在环境中采取行动的代理。结果表明，平均而言，我们的框架可以获得最佳专家所赚取的利润的90％。
Solving portfolio management problems using deep reinforcement learning has been getting much attention in finance for a few years. We have proposed a new method using experts signals and historical price data to feed into our reinforcement learning framework. Although experts signals have been used in previous works in the field of finance, as far as we know, it is the first time this method, in tandem with deep RL, is used to solve the financial portfolio management problem. Our proposed framework consists of a convolutional network for aggregating signals, another convolutional network for historical price data, and a vanilla network. We used the Proximal Policy Optimization algorithm as the agent to process the reward and take action in the environment. The results suggested that, on average, our framework could gain 90 percent of the profit earned by the best expert.
