{"repo":"YuriyKolesnikov/diffquant","free":true,"listed":false,"github":"https://github.com/YuriyKolesnikov/diffquant","clone":"git clone https://github.com/YuriyKolesnikov/diffquant.git","description":"End-to-End Differentiable Trading Pipeline","language":"Python","stars":14,"topics":["algorithmic-trading","backpropagation-through-time","cryptocurrency","deep-learning","differentiable-programming","itransformer","pytorch","quantitative-finance","reinforcement-learning","sharpe-ratio"],"license":null,"category":"machine-learning","readme_excerpt":"DiffQuant End-to-End Differentiable Trading Pipeline --- Contents - How it works - Validation protocol - Quick start - Structure - Experiments - Configuration - Dataset - Experimental status - Results - Limitations - Roadmap - Related work - Citation --- Most ML trading systems face the same structural gap: the model optimizes a proxy — MSE, cross-entropy, TD-error — while performance is measured in realized PnL. A better-fitting proxy does not guarantee better actual returns. DiffQuant closes this gap by design. The pipeline from raw market features through a differentiable mark-to-market simulator to the Sharpe ratio is a single computation graph. loss.backward() optimizes what the strategy actually earns, not a surrogate for it. Research article (English · Medium): DiffQuant: End-to-End Sharpe Optimization Through a Differentiable Trading Simulator Статья (Русский · Habr): DiffQuant: прямая оптимизация коэффициента Шарпа через дифференцируемый торговый симулятор --- How it works The full pipeline is a single differentiable computation graph: The simulator implements exact mark-to-market accounting as tensor operations — no surrogate losses, no reward shaping. The entire chain is differentiable: smooth abs(x) = √(x² + ε) replaces x to preserve C∞ differentiability through transaction cost computation — critical when the model operates near flat. Policy head: direction × gate direction encodes the alpha signal; gate encodes whether to trade at all. When confidence is low, ga","default_branch":null,"files":null,"tree":[],"storefront":"/r/YuriyKolesnikov","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/YuriyKolesnikov/diffquant/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."}