Trading with the Momentum Transformer [!IMPORTANT] Latest Work DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management extends the Momentum Transformer to end-to-end portfolio construction, re-implemented in PyTorch. Key contributions: 1. Graph neural networks encoding macroeconomic priors across assets 2. Multi-asset cross-sectional attention with a causal lag (Directed Delay) mechanism 3. Portfolio-level loss — optimises on a pooled portfolio Sharpe ratio rather than univariate per-asset objectives 4. Regime-robust minimax optimisation — a SoftMin proxy for Entropic Value-at-Risk (EVaR) that penalises the worst historical subperiods 5. Realistic transaction costs in the loss — asset-specific costs baked directly into the training objective 6. Two-pass exact gradient accumulation — correct gradients for the coupled Sharpe-ratio objective at scale In backtests from 2010--2025, DeePM roughly doubles the net risk-adjusted returns of classical trend-following and improves upon the Momentum Transformer by approximately fifty percent. See the paper and GitHub for full

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