{"repo":"kieranjwood/slow-momentum-fast-reversion","free":true,"listed":false,"github":"https://github.com/kieranjwood/slow-momentum-fast-reversion","clone":"git clone https://github.com/kieranjwood/slow-momentum-fast-reversion.git","description":"This code accompanies the the paper Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection (https://arxiv.org/pdf/2105.13727.pdf).","language":"Python","stars":274,"topics":["deep-learning","machine-learning","trading","change-point-detection","trading-strategies","quantum-mechanics","momentum-trading-strategy"],"license":"MIT","category":"machine-learning","readme_excerpt":"Slow Momentum with Fast Reversion [!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 details. About This code accompanies the the paper Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection and preprint. :warning: This work has now been improved upon with the paper Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture. Please refer to the this repo for the implementation of both the Slow Momentum with Fas","default_branch":null,"files":null,"tree":[],"storefront":"/r/kieranjwood","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/kieranjwood/slow-momentum-fast-reversion/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}