{"repo":"kieranjwood/deepm","free":true,"listed":false,"github":"https://github.com/kieranjwood/deepm","clone":"git clone https://github.com/kieranjwood/deepm.git","description":"This code accompanies the paper DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management (https://arxiv.org/abs/2601.05975)","language":"Python","stars":20,"topics":["attention-mechanism","deep-learning","graph-neural-networks","portfolio-management","portfolio-optimization","quantitative-finance","risk-measures","robust-optimization","systematic-trading-strategies","algorithmic-trading"],"license":"MIT","category":"trading","readme_excerpt":"DeePM: Regime-Robust Deep Learning for Systematic Macro Portfolio Management Paper PDF Kieran Wood, Stephen J. Roberts, Stefan Zohren All of our works are covered on my website. This repository contains the code to reproduce the experiments in the paper. 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 ","default_branch":null,"files":null,"tree":[],"storefront":"/r/kieranjwood","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/kieranjwood/deepm/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."}