标题： 技术分析与机器学习的结合：比特币证据 显示英文标题

标题： Technical Analysis Meets Machine Learning: Bitcoin Evidence

摘要： 在本篇笔记中，我们使用两种机器学习模型——Light Gradient Boosting Machine（LightGBM）和Long Short-Term Memory（LSTM）——以及两种基于技术分析的策略：指数移动平均线（EMA）交叉策略和移动平均收敛/发散与平均方向指数（MACD+ADX）的组合策略，比较比特币交易表现。目标是评估如何利用交易信号在比特币市场中最大化利润。这种比较是由美国证券交易委员会（SEC）于2024-01-10批准首批现货比特币交易所交易基金（ETF）所激发的。我们的结果表明，LSTM模型在不到一年的时间内实现了约65.23%的累计回报，显著优于LightGBM、EMA和MACD+ADX策略以及基准的买入并持有策略。这项研究突显了机器学习和技术分析在快速发展的加密货币领域更深层次整合的潜力。 显示英文摘要

摘要： 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.