Efficient Triangular Arbitrage Detection via Graph Neural Networks

ArXiv ID: 2502.03194 “View on arXiv”

Authors: Unknown

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.

Keywords: Triangular Arbitrage, Graph Neural Networks, Foreign Exchange, Deep Q-Learning, Algorithmic Trading

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats
  • Why: The paper employs advanced mathematical concepts including graph neural networks, optimization formulations, and Deep Q-Learning, indicating high math complexity. However, the evidence is based on a synthetic dataset without backtests, real data, or statistical metrics, resulting in low empirical rigor.
  flowchart TD
    A["Research Goal<br>Efficient Triangular Arbitrage Detection"] --> B["Data Input<br>Synthetic Forex Market Dataset"]
    B --> C["Methodology<br>Graph Neural Network with DQN"]
    C --> D["Computational Process<br>Relaxed Loss Function &amp; Optimized Path Search"]
    D --> E["Key Finding 1<br>Higher Average Yield vs Traditional Methods"]
    D --> F["Key Finding 2<br>Significantly Reduced Computational Time"]
    E --> G["Outcome<br>Real-time Arbitrage Detection Framework"]
    F --> G