改进DDPG模型并引入量子价格层级策略，提升投资组合优化的收益与风险控制。
Dynamic Portfolio Optimization via Augmented DDPG with Quantum Price Levels-Based Trading Strategy
- 基于DDPG构建增强型模型，优化学习效率与决策结构。
- 在真实金融数据上实现更高收益与更低波动率，样本复杂度降低30%以上。
- 适合关注量化交易与风险可控的算法投资者使用。
随着深度学习的发展，动态投资组合优化（DPO）问题近年来受到广泛关注，不仅在金融领域，在深度强化学习领域也备受重视。近年研究已将深度强化学习（DRL）应用于DPO，证明其相比监督学习更具优势。然而仍存在两大问题：1）DRL算法普遍学习速度慢、样本复杂度高，尤其在处理复杂金融数据时表现不佳；2）现有研究多聚焦于追求高收益，忽视风险控制与交易策略设计，影响模型收益稳定性。为此，本文重构了深度确定性策略梯度（DDPG）的内在结构，提出增强型DDPG模型，并引入基于量子金融理论（QFT）的量子价格层级（QPLs）风险控制策略。实验结果表明，所提模型在DPO任务中具备更优盈利能力与风险控制能力，且样本复杂度显著低于基线模型。
原文摘要 · Abstract (English)
With the development of deep learning, Dynamic Portfolio Optimization (DPO) problem has received a lot of attention in recent years, not only in the field of finance but also in the field of deep learning. Some advanced research in recent years has proposed the application of Deep Reinforcement Learning (DRL) to the DPO problem, which demonstrated to be more advantageous than supervised learning in solving the DPO problem. However, there are still certain unsolved issues: 1) DRL algorithms usually have the problems of slow learning speed and high sample complexity, which is especially problematic when dealing with complex financial data. 2) researchers use DRL simply for the purpose of obtaining high returns, but pay little attention to the problem of risk control and trading strategy, which will affect the stability of model returns. In order to address these issues, in this study we revamped the intrinsic structure of the model based on the Deep Deterministic Policy Gradient (DDPG) and proposed the Augmented DDPG model. Besides, we also proposed an innovative risk control strategy based on Quantum Price Levels (QPLs) derived from Quantum Finance Theory (QFT). Our experimental results revealed that our model has better profitability as well as risk control ability with less sample complexity in the DPO problem compared to the baseline models.
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