An innovative approach to identifying triangular arbitrage opportunities in financial markets using the Bellman-Ford algorithm

Abstract

Existing arbitrage detection techniques rely on exhaustive search or linear programming, which are computationally expensive and often miss profitable cycles in dynamic markets. Triangular arbitrage is a profitable trading strategy that exploits discrepancies in currency exchange rates, but common algorithms detect only a limited number of loops and cannot find non-loop opportunities. To address these gaps, this study presents a realtime, graph-based framework for identifying triangular arbitrage opportunities in cryptocurrency markets using an optimized implementation of the Bellman–Ford algorithm. By modeling currency exchange rates as a directed graph and detecting negative-weight cycles, the framework efficiently identifies profitable arbitrage opportunities under realistic trading conditions. The proposed framework achieves an average detection latency of 0.002 milliseconds, providing empirical performance benchmarks for single-exchange cryptocurrency trading systems. Experiments on a six-month historical dataset yielded a detection accuracy of 92%, while additional validation on live cryptocurrency market data streams confirmed the framework’s real-time performance and low latency. This high-speed detection is crucial in high-frequency trading (HFT), where brief pricing inefficiencies can yield significant profits before being corrected. The experimental pipeline is designed to support reproducibility and comparative evaluation in applied FinTech research.