Talk · International Conference on Intelligent Sustainable Systems · March 12, 2025
CryptoArbML: Enhancing Cryptocurrency Arbitrage with Machine Learning-Driven Predictive Analytics
Nishanth Reddy Kuraku, Hardik Gupta, Ragini Tripathi, C. Jyotsna, Aiswariya Milan K
Abstract
Cryptocurrency arbitrage using machine learning (ML) enhances the ArbitrageExplorer platform through CryptoArbML, enabling efficient recognition and execution of arbitrage opportunities. In real-world scenarios, traders identify profitable exchanges for arbitrage, but market volatility and time-sensitive decisions often lead to missed opportunities or marginal profits. This work leverages advanced time series forecasting using LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) models. LSTM tracks long-term dependencies for price prediction, while GRU efficiently processes sequential data to discover arbitrage spots. These models conduct real-time market analysis, dynamically adapt strategies, and perform well under high volatility. LSTM achieved 98.52% accuracy, slightly outperforming GRU (98.45%), though GRU identified additional arbitrage opportunities. Hyperparameter tuning further improved precision (96.25% for LSTM, 95.15% for GRU), demonstrating their effectiveness in real-world arbitrage execution.
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