25 citations · 34 across the 13 of their papers we have counts for
7 papers · 1 filter
LEMs: A Primer On Large Execution Models
Remi Genet, Hugo Inzirillo
This paper introduces Large Execution Models (LEMs), a novel deep learning framework that extends transformer-based architectures to address complex execution problems with flexibl…
Deep Learning for VWAP Execution in Crypto Markets: Beyond the Volume Curve
Remi Genet
Volume-Weighted Average Price (VWAP) is arguably the most prevalent benchmark for trade execution as it provides an unbiased standard for comparing performance across market partic…
VWAP Execution with Signature-Enhanced Transformers: A Multi-Asset Learning Approach
Remi Genet
In this paper I propose a novel approach to Volume Weighted Average Price (VWAP) execution that addresses two key practical challenges: the need for asset-specific model training a…
Recurrent Neural Networks for Dynamic VWAP Execution: Adaptive Trading Strategies with Temporal Kolmogorov-Arnold Networks
Remi Genet
The execution of Volume Weighted Average Price (VWAP) orders remains a critical challenge in modern financial markets, particularly as trading volumes and market complexity continu…
SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms
Rémi Genet, Hugo Inzirillo
In this paper, we propose a novel approach that enhances recurrent neural networks (RNNs) by incorporating path signatures into their gating mechanisms. Our method modifies both Lo…
STAN: Smooth Transition Autoregressive Networks
Hugo Inzirillo, Remi Genet
Traditional Smooth Transition Autoregressive (STAR) models offer an effective way to model these dynamics through smooth regime changes based on specific transition variables. In t…
Keras Sig: Efficient Path Signature Computation on GPU in Keras 3
Rémi Genet, Hugo Inzirillo
In this paper we introduce Keras Sig a high-performance pythonic library designed to compute path signature for deep learning applications. Entirely built in Keras 3, \textit{Keras…