标题： 基于遗传算法生成的Alpha因子和情感的分类混合集成（GAS） 显示英文标题

标题： Blending Ensemble for Classification with Genetic-algorithm generated Alpha factors and Sentiments (GAS)

摘要： 随着加密货币市场的日益成熟和扩展，理解和预测其价格波动已成为金融工程领域的重要问题。 本文介绍了一种创新的遗传算法生成的阿尔法情绪（GAS）混合集成模型，专门设计用于预测比特币市场趋势。 该模型结合了先进的集成学习方法、特征选择算法和深入的情绪分析，以有效捕捉每日比特币交易数据的复杂性和变化性。 GAS框架结合了34个阿尔法因子和8个新闻经济情绪因子，通过准确分析市场情绪和技术指标，为比特币价格波动提供深入了解。 本研究的核心是使用堆叠模型（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并持有策略中表现出色。 此外，本文还探讨了使用遗传算法自动构建阿尔法因子以及通过情绪分析增强预测模型的有效性。 实验结果表明，GAS模型在每日比特币趋势预测中表现具有竞争力，尤其是在分析数据丰富的高波动性金融资产时。 显示英文摘要

摘要： 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.