12 papers
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…
TKAN: Temporal Kolmogorov-Arnold Networks
Remi Genet, Hugo Inzirillo
Recurrent Neural Networks (RNNs) have revolutionized many areas of machine learning, particularly in natural language and data sequence processing. Long Short-Term Memory (LSTM) ha…
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…