We propose DeePM (Deep Portfolio Manager), a structured deep-learning macro portfolio manager trained end-to-end to maximize a robust, risk-adjusted utility. DeePM addresses three fundamental challenges in financial learning: (1) it resolves the asynchronous "ragged filtration" problem via a Directed Delay (Causal Sieve) mechanism that prioritizes causal impulse-response learning over information freshness; (2) it combats low signal-to-noise ratios via a Macroeconomic Graph Prior, regularizing cross-asset dependence according to economic first principles; and (3) it optimizes a distributionally robust objective where a smooth worst-window penalty serves as a differentiable proxy for Entropic Value-at-Risk (EVaR) - a window-robust utility encouraging strong performance in the most adverse historical subperiods. In large-scale backtests from 2010-2025 on 50 diversified futures with highly realistic transaction costs, DeePM attains net risk-adjusted returns that are roughly twice those of classical trend-following strategies and passive benchmarks, solely using daily closing prices. Furthermore, DeePM improves upon the state-of-the-art Momentum Transformer architecture by roughly fifty percent. The model demonstrates structural resilience across the 2010s "CTA (Commodity Trading Advisor) Winter" and the post-2020 volatility regime shift, maintaining consistent performance through the pandemic, inflation shocks, and the subsequent higher-for-longer environment. Ablation studies confirm that strictly lagged cross-sectional attention, graph prior, principled treatment of transaction costs, and robust minimax optimization are the primary drivers of this generalization capability.

翻译：本文提出DeePM（深度投资组合管理器），这是一种端到端训练的结构化深度学习宏观投资组合管理器，旨在最大化稳健的风险调整后效用。DeePM解决了金融学习中的三个根本性挑战：（1）通过定向延迟（因果筛）机制解决异步“参差滤波”问题，该机制优先考虑因果脉冲响应学习而非信息新鲜度；（2）通过宏观经济图先验应对低信噪比问题，依据经济学第一原理规范跨资产依赖性；（3）优化分布稳健目标，其中平滑的最差窗口惩罚函数作为熵风险价值（EVaR）的可微分代理——这是一种窗口稳健的效用函数，旨在激励在最不利历史子区间内的强劲表现。在2010-2025年间对50种多元化期货进行的大规模回溯测试中（采用高度真实的交易成本），DeePM仅使用每日收盘价即实现了净风险调整后收益，其表现约为经典趋势跟踪策略和被动基准的两倍。此外，DeePM较当前最先进的Momentum Transformer架构提升了约百分之五十的性能。该模型在2010年代“CTA（商品交易顾问）寒冬”及2020年后波动体制转换期间展现出结构性韧性，在疫情、通胀冲击及随后的长期高利率环境中均保持稳定表现。消融研究证实，严格滞后的横截面注意力机制、图先验、交易成本的原理性处理以及稳健的极小极大优化是这一泛化能力的主要驱动因素。