Technical Analysis Meets Machine Learning: Bitcoin Evidence

TL;DR

This paper compares machine learning models and technical analysis strategies for Bitcoin trading, finding that LSTM models significantly outperform traditional methods and baseline strategies in terms of returns.

Contribution

It provides a comparative analysis of LSTM and LightGBM models against technical strategies for Bitcoin trading, highlighting the superior performance of LSTM.

Findings

LSTM achieved approximately 65.23% return in under a year.

LSTM outperformed LightGBM, EMA, MACD+ADX, and buy-and-hold strategies.

Results suggest potential for integrating machine learning with technical analysis in crypto trading.

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.