论文标题
股票投资组合优化的深度加固学习
Deep Reinforcement Learning for Stock Portfolio Optimization
论文作者
论文摘要
股票投资组合优化是将资金不断重新分配给各种股票的过程。在本文中，我们将提出问题，以便我们可以正确地将强化学习应用于任务。为了保持对市场的现实假设，我们也将将交易成本和风险因素纳入州。最重要的是，我们将应用各种最先进的深度强化学习算法进行比较。由于动作空间是连续的，因此在最先进的连续政策梯度算法的家族中测试了现实的配方：深层确定性策略梯度（DDPG），广义确定性策略梯度（GDPG）和近端政策优化（PPO），前两者的表现要比最后两个更好。接下来，我们将使用用于库存子集选择的最小方差投资组合理论的端到端解决方案，以及用于提取多频数据模式的小波变换。讨论了有关结果以及未来的研究方向的观察和假设。1
Stock portfolio optimization is the process of constant re-distribution of money to a pool of various stocks. In this paper, we will formulate the problem such that we can apply Reinforcement Learning for the task properly. To maintain a realistic assumption about the market, we will incorporate transaction cost and risk factor into the state as well. On top of that, we will apply various state-of-the-art Deep Reinforcement Learning algorithms for comparison. Since the action space is continuous, the realistic formulation were tested under a family of state-of-the-art continuous policy gradients algorithms: Deep Deterministic Policy Gradient (DDPG), Generalized Deterministic Policy Gradient (GDPG) and Proximal Policy Optimization (PPO), where the former two perform much better than the last one. Next, we will present the end-to-end solution for the task with Minimum Variance Portfolio Theory for stock subset selection, and Wavelet Transform for extracting multi-frequency data pattern. Observations and hypothesis were discussed about the results, as well as possible future research directions.1
