Using Machine Learning for Predicting Arbitrage Occurrences in Cryptocurrency Exchanges

Abstract

Cryptocurrency arbitrage, a riskless trading strategy, can yield profits but requires swift execution due to volatile opportunities that vanish rapidly. Utilizing arbitrage bots for algorithmic trading is essential for immediate trade execution across exchanges like Binance and Bybit. This paper implements such a system focusing on BTCUSDT and ETHUSDT pairs. Integrating Machine Learning (ML) aims to predict arbitrage occurrences in advance for faster trade execution, a tactic many traders overlook. Logistic Regression, Random Forest, Support Vector Machine, and Multilayer Perceptron models are implemented. Adding ML principles required the collection of a dataset with historical prices of the observed cryptocurrency pairs for various time intervals, on which we trained the model. Afterward, the model was evaluated in a live-trading environment. Results show Random Forest predicting exploitable arbitrage intervals ahead for Ether, with ML models more effective during less volatile periods. However, careful consideration is needed as predictions may not always align with market realities, leading to mixed trading outcomes. Furthermore, the training led to a model that can predict the occurrence of arbitrage; however, classifying the calculations even more carefully than in reality, resulting in a partially profitable or partially lossy trading strategy depending on the time of day and the current market stage.