In recent years, deep or reinforcement learning approaches have been applied to optimise investment portfolios through learning the spatial and temporal information under the dynamic financial market. Yet in most cases, the existing approaches may produce biased trading signals based on the conventional price data due to a lot of market noises, which possibly fails to balance the investment returns and risks. Accordingly, a multi-agent and self-adaptive portfolio optimisation framework integrated with attention mechanisms and time series, namely the MASAAT, is proposed in this work in which multiple trading agents are created to observe and analyse the price series and directional change data that recognises the significant changes of asset prices at different levels of granularity for enhancing the signal-to-noise ratio of price series. Afterwards, by reconstructing the tokens of financial data in a sequence, the attention-based cross-sectional analysis module and temporal analysis module of each agent can effectively capture the correlations between assets and the dependencies between time points. Besides, a portfolio generator is integrated into the proposed framework to fuse the spatial-temporal information and then summarise the portfolios suggested by all trading agents to produce a newly ensemble portfolio for reducing biased trading actions and balancing the overall returns and risks. The experimental results clearly demonstrate that the MASAAT framework achieves impressive enhancement when compared with many well-known portfolio optimsation approaches on three challenging data sets of DJIA, S&P 500 and CSI 300. More importantly, our proposal has potential strengths in many possible applications for future study.

翻译：近年来，深度或强化学习方法已应用于通过学习动态金融市场下的时空信息来优化投资组合。然而在多数情况下，现有方法可能基于传统价格数据因大量市场噪声而产生偏差交易信号，这可能导致无法平衡投资回报与风险。为此，本文提出了一种集成注意力机制与时间序列的多智能体自适应投资组合优化框架MASAAT，其中创建了多个交易智能体来观察和分析价格序列以及方向变化数据，这些数据能识别不同粒度下资产价格的显著变化，从而提高价格序列的信噪比。随后，通过重构序列中金融数据的令牌，每个智能体的基于注意力的横截面分析模块和时间分析模块能够有效捕捉资产间的相关性及时序点间的依赖关系。此外，该框架集成了一个投资组合生成器，用于融合时空信息并汇总所有交易智能体建议的投资组合，从而生成新的集成投资组合，以减少偏差交易行为并平衡整体收益与风险。实验结果表明，在DJIA、S&P 500和CSI 300三个具有挑战性的数据集上，与众多知名投资组合优化方法相比，MASAAT框架实现了显著性能提升。更重要的是，本方案在未来研究的诸多潜在应用中具有优势。