论文标题
基于风险返回增强算法的智能算法交易
An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm
论文作者
论文摘要
这篇科学论文提出了一种新型的投资组合优化模型，使用改进的深钢筋学习算法。优化模型的目标函数是投资组合累积回报的期望和价值的加权总和。所提出的算法基于参与者 - 批判性架构，其中关键网络的主要任务是使用分位数回归来了解投资组合累积返回的分布，而Actor网络通过最大化上述目标函数来输出最佳投资组合权重。同时，我们利用线性转换功能来实现资产短销售。最后，使用一种称为APE-X的多进程方法来加速深度强化学习训练的速度。为了验证我们提出的方法，我们对两个代表性的投资组合进行了进行回测，并观察到这项工作中提出的模型优于基准策略。
This scientific paper propose a novel portfolio optimization model using an improved deep reinforcement learning algorithm. The objective function of the optimization model is the weighted sum of the expectation and value at risk(VaR) of portfolio cumulative return. The proposed algorithm is based on actor-critic architecture, in which the main task of critical network is to learn the distribution of portfolio cumulative return using quantile regression, and actor network outputs the optimal portfolio weight by maximizing the objective function mentioned above. Meanwhile, we exploit a linear transformation function to realize asset short selling. Finally, A multi-process method is used, called Ape-x, to accelerate the speed of deep reinforcement learning training. To validate our proposed approach, we conduct backtesting for two representative portfolios and observe that the proposed model in this work is superior to the benchmark strategies.
