用遗传算法生成因子，融合情绪与技术指标预测比特币走势。
Blending Ensemble for Classification with Genetic-algorithm generated Alpha factors and Sentiments (GAS)
- 用遗传算法自动构建34个因子和8个情绪因子，形成混合模型。
- 在日度比特币趋势预测中表现优异，优于传统买入持有策略。
- 适合量化交易研究者，尤其关注加密货币波动性分析的人群。
随着加密货币市场日益成熟和扩展，理解并预测其价格波动已成为金融工程领域的重要课题。本文提出一种专为预测比特币市场趋势设计的创新性遗传算法生成阿尔法情绪（GAS）融合集成模型。该模型结合先进的集成学习方法、特征选择算法及深度情绪分析，有效捕捉每日比特币交易数据的复杂性和变异性。GAS框架整合了34个阿尔法因子与8个新闻经济情绪因子，通过精准分析市场情绪和技术指标，深入揭示比特币价格波动机制。研究核心采用堆叠模型（包含LightGBM、XGBoost和随机森林分类器）进行趋势预测，在传统买入持有策略下表现出色。此外，文章还探讨了使用遗传算法自动化构建阿尔法因子的有效性，以及通过情绪分析提升预测模型性能的可能性。实验结果表明，GAS模型在日度比特币趋势预测中具有竞争力，尤其适用于分析数据丰富且波动剧烈的金融资产。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。