Technical Analysis Meets Machine Learning: Bitcoin Evidence

ArXiv ID: 2511.00665 “View on arXiv”

Authors: José Ángel Islas Anguiano, Andrés García-Medina

Abstract

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.

Keywords: Bitcoin, LSTM, LightGBM, technical analysis, cryptocurrency, trading strategies

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders
  • Why: The paper employs advanced machine learning models (LSTM, LightGBM) requiring solid technical understanding, but lacks detailed derivations or heavy formulaic proofs, placing it in the mid-range for math complexity. Empirical rigor is relatively high due to the use of real Bitcoin data, explicit backtest periods, and specific performance metrics, though the absence of full reproducibility code or extensive statistical robustness checks prevents a top score.
  flowchart TD
    A["Research Goal: Compare ML vs TA for Bitcoin Trading"] --> B["Key Methodology<br>LightGBM vs LSTM vs EMA vs MACD+ADX"]
    B --> C["Data Source<br>Bitcoin Price Data (Pre/Post ETF)"]
    C --> D["Computational Process<br>Model Training & Backtesting"]
    D --> E["Key Finding: LSTM Achieved 65.23% Return<br>Outperformed all others & Buy-and-Hold"]