基于多模态扰动集合分类器的智能股票交易决策系统

Xiaoyu Hou, Chao Luo, Baozhong Gao
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引用次数: 0

摘要

蜡烛图在金融市场中被广泛用作有效的技术分析工具。传统上,蜡烛图的不同组合形成了特定的看涨/看跌形态,为投资者提供了更多盈利交易的机会。然而,大多数形态都来自于主观的专业知识,而没有进行定量分析。在本文中,我们将看涨/看跌形态与集合学习相结合,提出了一种用于做出股票交易决策的智能系统。多模态扰动集合分类器(ECMP)旨在生成一组多样化的精确基础分类器,以进一步确定蜡烛图形态。它通过以下方法实现这一目标:首先,通过引导采样对样本空间进行扰动;其次,采用基于邻域粗糙集理论的属性缩减算法来选择相关特征;第三,通过随机子空间选择对特征空间进行扰动。最终,交易决策将以这一过程的分类结果为指导。为了评估所提出的模型,我们将其应用于中国股市的实证调查。实验结果清楚地证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An intelligent stock trading decision system based on ensemble classifier through multimodal perturbation
Candlesticks are widely used as an effective technical analysis tool in financial markets. Traditionally, different combinations of candlesticks have formed specific bullish/bearish patterns providing investors with increased opportunities for profitable trades. However, most patterns derived from subjective expertise without quantitative analysis. In this article, combining bullish/bearish patterns with ensemble learning, we present an intelligent system for making stock trading decisions. The Ensemble Classifier through Multimodal Perturbation (ECMP) is designed to generate a diverse set of precise base classifiers to further determine the candlestick patterns. It achieves this by: first, introducing perturbations to the sample space through bootstrap sampling; second, employing an attribute reduction algorithm based on neighborhood rough set theory to select relevant features; third, perturbing the feature space through random subspace selection. Ultimately, the trading decisions are guided by the classification outcomes of this procedure. To evaluate the proposed model, we apply it to empirical investigations within the context of the Chinese stock market. The results obtained from our experiments clearly demonstrate the effectiveness of the approach.
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