With the increasing maturity and expansion of the cryptocurrency market, understanding and predicting its price fluctuations has become an important issue in the field of financial engineering. This article introduces an innovative Genetic Algorithm-generated Alpha Sentiment (GAS) blending ensemble model specifically designed to predict Bitcoin market trends. The model integrates advanced ensemble learning methods, feature selection algorithms, and in-depth sentiment analysis to effectively capture the complexity and variability of daily Bitcoin trading data. The GAS framework combines 34 Alpha factors with 8 news economic sentiment factors to provide deep insights into Bitcoin price fluctuations by accurately analyzing market sentiment and technical indicators. The core of this study is using a stacked model (including LightGBM, XGBoost, and Random Forest Classifier) for trend prediction which demonstrates excellent performance in traditional buy-and-hold strategies. In addition, this article also explores the effectiveness of using genetic algorithms to automate alpha factor construction as well as enhancing predictive models through sentiment analysis. Experimental results show that the GAS model performs competitively in daily Bitcoin trend prediction especially when analyzing highly volatile financial assets with rich data.

翻译：随着加密货币市场的日益成熟与扩张，理解并预测其价格波动已成为金融工程领域的重要课题。本文提出一种创新的遗传算法生成Alpha情感因子（GAS）融合集成模型，专门用于预测比特币市场趋势。该模型整合了先进的集成学习方法、特征选择算法以及深入的情感分析，以有效捕捉每日比特币交易数据的复杂性与多变性。GAS框架结合了34个Alpha因子与8个新闻经济情感因子，通过精准分析市场情绪与技术指标，为比特币价格波动提供深度洞察。本研究的核心在于使用堆叠模型（包括LightGBM、XGBoost和随机森林分类器）进行趋势预测，该模型在传统的买入持有策略中展现出卓越性能。此外，本文还探讨了利用遗传算法自动化构建Alpha因子的有效性，以及通过情感分析增强预测模型的能力。实验结果表明，GAS模型在每日比特币趋势预测中具有竞争优势，尤其是在分析数据丰富且波动剧烈的金融资产时表现突出。