2025/11/01 by José Ángel Islas Anguiano, Anguiano, José Ángel Islas, Andrés García-Medina +1 · 2 voices
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Blockchain Technology Applications and Security #Computational Finance (q-fin.CP) #FOS: Economics and business #Financial Markets and Investment Strategies #Stock Market Forecasting Methods #q-fin.CP
paper · pdf · doi:10.48550/arxiv.2511.00665
openalex publication_date 2025/11/01 · arxiv published 2025/11/01 · arxiv updated 2025/11/01 · openalex created_date 2025/11/05 · openalex updated_date 2026/07/28
In this note, we compare Bitcoin trading performance using two machine learning models-Light Gradient Boosting Machine (LightGBM) and Long Short-Term Memory (LSTM)-and two technical analysis-based strategies: Exponential Moving Average (EMA) crossover and a combination of Moving Average Convergence/Divergence with the Average Directional Index (MACD+ADX). The objective is to evaluate how trading signals can be used to maximize profits in the Bitcoin market. This comparison was motivated by the U.S. Securities and Exchange Commission's (SEC) approval of the first spot Bitcoin exchange-traded funds (ETFs) on 2024-01-10. Our results show that the LSTM model achieved a cumulative return of approximately 65.23% in under a year, significantly outperforming LightGBM, the EMA and MACD+ADX strategies, as well as the baseline buy-and-hold. This study highlights the potential for deeper integration of machine learning and technical analysis in the rapidly evolving cryptocurrency landscape.