标题： DeepAries：自适应再平衡区间选择以增强投资组合选择 显示英文标题

标题： DeepAries: Adaptive Rebalancing Interval Selection for Enhanced Portfolio Selection

摘要： 我们提出 DeepAries，一种用于动态投资组合管理的新颖深度强化学习框架，该框架联合优化再平衡决策的时间和分配。 与之前不考虑市场状况的固定再平衡间隔的强化学习方法不同，DeepAries 适应性地选择最优再平衡间隔以及投资组合权重，以减少不必要的交易成本并最大化风险调整后的收益。 我们的框架结合基于 Transformer 的状态编码器，有效捕捉复杂的长期市场依赖关系，并与近端策略优化（PPO）相结合，生成同时的离散（再平衡间隔）和连续（资产分配）动作。 在多个现实金融市场上的大量实验表明，DeepAries 在风险调整收益、交易成本和回撤方面显著优于传统固定频率和完全再平衡策略。 此外，我们在 https://deep-aries.github.io/ 提供 DeepAries 的实时演示，同时在 https://github.com/dmis-lab/DeepAries 上提供源代码和数据集，展示了 DeepAries 产生与市场制度变化一致的可解释再平衡和分配决策的能力。 总体而言，DeepAries 通过将时间安排和分配整合到统一的决策过程中，为自适应且实用的投资组合管理引入了一种创新范式。 显示英文摘要

摘要： We propose DeepAries , a novel deep reinforcement learning framework for dynamic portfolio management that jointly optimizes the timing and allocation of rebalancing decisions. Unlike prior reinforcement learning methods that employ fixed rebalancing intervals regardless of market conditions, DeepAries adaptively selects optimal rebalancing intervals along with portfolio weights to reduce unnecessary transaction costs and maximize risk-adjusted returns. Our framework integrates a Transformer-based state encoder, which effectively captures complex long-term market dependencies, with Proximal Policy Optimization (PPO) to generate simultaneous discrete (rebalancing intervals) and continuous (asset allocations) actions. Extensive experiments on multiple real-world financial markets demonstrate that DeepAries significantly outperforms traditional fixed-frequency and full-rebalancing strategies in terms of risk-adjusted returns, transaction costs, and drawdowns. Additionally, we provide a live demo of DeepAries at https://deep-aries.github.io/, along with the source code and dataset at https://github.com/dmis-lab/DeepAries, illustrating DeepAries' capability to produce interpretable rebalancing and allocation decisions aligned with shifting market regimes. Overall, DeepAries introduces an innovative paradigm for adaptive and practical portfolio management by integrating both timing and allocation into a unified decision-making process.