基于深度强化学习的高维股票组合交易

Uta Pigorsch, S. Schäfer
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引用次数: 5

摘要

本文提出了一种基于Deep Q-learning的金融投资组合交易深度强化学习算法[1]。该算法能够交易来自任何规模的横断面数据集的高维投资组合,这些数据集可能包括资产中的数据缺口和非唯一历史长度。我们通过为每个环境采样一种资产来顺序地设置环境,同时用所得资产的回报奖励投资,并用资产集的平均回报奖励现金储备。这迫使代理战略性地将资金分配给它预测表现高于平均水平的资产。我们将我们的方法应用于对48个美国股票投资组合的样本外分析,这些股票的数量从10只到500只不等,选择标准和交易成本水平也有所不同。平均而言,该算法仅使用一个超参数设置对所有投资组合优于所有考虑的被动和主动基准投资策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning
This paper proposes a Deep Reinforcement Learning algorithm for financial portfolio trading based on Deep Q-learning [1]. 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.
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