Efficient Triangular Arbitrage Detection via Graph Neural Networks

TL;DR

This paper introduces a Graph Neural Network approach for efficiently detecting triangular arbitrage opportunities in currency exchange markets, outperforming traditional methods in speed and yield.

Contribution

It presents a novel GNN-based framework for arbitrage detection, integrating a relaxed loss function and Deep Q-Learning to improve efficiency and effectiveness.

Findings

Higher average yield compared to traditional methods

Significantly reduced computational time

Effective in synthetic market simulations

Abstract

Triangular arbitrage is a profitable trading strategy in financial markets that exploits discrepancies in currency exchange rates. Traditional methods for detecting triangular arbitrage opportunities, such as exhaustive search algorithms and linear programming solvers, often suffer from high computational complexity and may miss potential opportunities in dynamic markets. In this paper, we propose a novel approach to triangular arbitrage detection using Graph Neural Networks (GNNs). By representing the currency exchange network as a graph, we leverage the powerful representation and learning capabilities of GNNs to identify profitable arbitrage opportunities more efficiently. Specifically, we formulate the triangular arbitrage problem as a graph-based optimization task and design a GNN architecture that captures the complex relationships between currencies and exchange rates. We introduce a relaxed loss function to enable more flexible learning and integrate Deep Q-Learning principles to optimize the expected returns. Our experiments on a synthetic dataset demonstrate that the proposed GNN-based method achieves a higher average yield with significantly reduced computational time compared to traditional methods. This work highlights the potential of using GNNs for solving optimization problems in finance and provides a promising approach for real-time arbitrage detection in dynamic financial markets.