标题： 高维股票投资组合交易的深度强化学习 显示英文标题

标题： High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning

摘要： 本文提出了一种基于深度Q学习的深度强化学习算法，用于金融投资组合交易。 该算法能够从任何大小的横截面数据集中交易高维投资组合，这些数据集可能包含数据缺口和资产历史长度不唯一的情况。 我们通过为每个环境采样一个资产来依次建立环境，对投资给予相应资产的收益奖励，并通过一组资产的平均收益奖励现金保留。 这迫使代理战略性地将资本分配给其预测表现优于平均水平的资产。 我们在样本外分析中应用了我们的方法论，针对48个美国股票投资组合设置，这些设置在股票数量（从10只到500只股票）、选择标准以及交易成本水平方面各不相同。 该算法在使用所有投资组合的单一超参数设置的情况下，平均显著优于所有考虑的被动和主动基准投资策略。 显示英文摘要

摘要： This paper proposes a Deep Reinforcement Learning algorithm for financial portfolio trading based on Deep Q-learning. The algorithm is capable of trading high-dimensional portfolios from cross-sectional datasets of any size which may include data gaps and non-unique history lengths in the assets. We sequentially set up environments by sampling one asset for each environment while rewarding investments with the resulting asset's return and cash reservation with the average return of the set of assets. This enforces the agent to strategically assign capital to assets that it predicts to perform above-average. We apply our methodology in an out-of-sample analysis to 48 US stock portfolio setups, varying in the number of stocks from ten up to 500 stocks, in the selection criteria and in the level of transaction costs. The algorithm on average outperforms all considered passive and active benchmark investment strategies by a large margin using only one hyperparameter setup for all portfolios.