用双智能体强化学习，同时优化现有投资和发现新机会。
FinXplore: An Adaptive Deep Reinforcement Learning Framework for Balancing and Discovering Investment Opportunities
- 双DRL智能体分工：一个管现有资产，一个探索新机会。
- 在两个真实市场数据集上表现优于主流策略。
- 适合关注动态资产配置与创新机会的量化投资者。
投资组合优化对平衡金融决策中的风险与收益至关重要。深度强化学习（DRL）作为前沿工具，通过试错交互学习动态资产配置。然而，多数DRL方法仅限于预设投资范围内的资产分配，忽视了新机会的探索。本文提出一种融合利用现有资产与探索扩展投资范围中潜在机会的投资框架。该方法采用两个DRL智能体，动态平衡二者目标，以适应市场变化并提升投资绩效。一个智能体负责现有投资范围内的资产分配，另一个则辅助在扩展范围内探索新机会。通过两个真实市场数据集验证了该方法的有效性。实验表明，所提方法在性能上显著优于当前最先进策略及基线方法。
原文摘要 · Abstract (English)
Portfolio optimization is essential for balancing risk and return in financial decision-making. Deep Reinforcement Learning (DRL) has stood out as a cutting-edge tool for portfolio optimization that learns dynamic asset allocation using trial-and-error interactions. However, most DRL-based methods are restricted to allocating assets within a pre-defined investment universe and overlook exploring new opportunities. This study introduces an investment landscape that integrates exploiting existing assets with exploring new investment opportunities in an extended universe. The proposed approach leverages two DRL agents and dynamically balances these objectives to adapt to evolving markets while enhancing portfolio performance. One agent allocates assets within the existing universe, while another assists in exploring new opportunities in the extended universe. The effciency of the proposed methodology is determined using two real-world market data sets. The experiments demonstrate the superiority of the suggested approach against the state-of-the-art portfolio strategies and baseline methods.
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