论文标题
通过深入的强化学习学习金融资产特定的交易规则
Learning Financial Asset-Specific Trading Rules via Deep Reinforcement Learning
论文作者
论文摘要
根据资产的财务状况生成特定于资产的交易信号是自动交易中具有挑战性的问题之一。根据不同的技术分析技术，在实验中提出了各种资产交易规则。但是，这些交易策略是有利可图的，从广泛的历史数据中提取新的资产特定交易规则，以增加总回报并降低人类专家的投资组合风险。最近，采用了各种深入的强化学习（DRL）方法来学习每个资产的新交易规则。在本文中，提出了具有各种特征提取模块的新型DRL模型。研究了不同输入表示对模型性能的影响，并研究了不同市场和资产情况下基于DRL的模型的性能。这项工作中的拟议模型在学习单一资产特定的交易规则方面优于其他最先进的模型，并在两年内获得了几乎262％的总回报，而最佳的最先进模型在同一时期的同一资产中获得了78％的收益。
Generating asset-specific trading signals based on the financial conditions of the assets is one of the challenging problems in automated trading. Various asset trading rules are proposed experimentally based on different technical analysis techniques. However, these kind of trading strategies are profitable, extracting new asset-specific trading rules from vast historical data to increase total return and decrease the risk of portfolios is difficult for human experts. Recently, various deep reinforcement learning (DRL) methods are employed to learn the new trading rules for each asset. In this paper, a novel DRL model with various feature extraction modules is proposed. The effect of different input representations on the performance of the models is investigated and the performance of DRL-based models in different markets and asset situations is studied. The proposed model in this work outperformed the other state-of-the-art models in learning single asset-specific trading rules and obtained a total return of almost 262% in two years on a specific asset while the best state-of-the-art model get 78% on the same asset in the same time period.
