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

TL;DR

This paper presents a novel ensemble model using genetic algorithms to generate alpha factors and sentiment analysis for predicting Bitcoin market trends, demonstrating strong performance in volatile conditions.

Contribution

It introduces a genetic algorithm-based framework for automated alpha factor construction combined with sentiment analysis in an ensemble learning model for cryptocurrency prediction.

Findings

GAS model outperforms traditional methods in Bitcoin trend prediction.

Genetic algorithms effectively automate alpha factor creation.

Sentiment analysis enhances predictive accuracy in volatile markets.

Abstract

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.