Predicting Arbitrage Occurrences With Machine Learning and Improved Decision Threshold Level in Live‐Trading Crypto Environments

ABSTRACT Cryptocurrencies represent a significantly utilized class of digital assets, encompassing a diverse array of tokens and coins available for trading purposes. In this study, the integration of machine learning algorithms with an arbitrage trading strategy across cryptocurrency exchanges is explored. The objective is to scrutinize prominent cryptocurrency pairs characterized by high volatility, vulnerability to speculation, regulatory gaps, liquidity constraints, and heavy‐tail distribution, with the intention of training the model to predict the potential for arbitrage. To differentiate from competitors who await rare arbitrage opportunities, a novel approach is introduced to enhance arbitrage profitability. The primary innovation of this study lies in demonstrating the capability to predict profitable arbitrage opportunities in discrete intervals in advance, by incorporating sophisticated confidence level metrics to initiate arbitrage trades only when the model's predictions demonstrate substantial certainty. The findings indicate that the profitability of the entire strategy exceeds 100% within a 1‐week timeframe.