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比较用于高频股票信号的机器学习模型

文章 arXiv papers · 作者: Jiahao Chen et al.
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总结

本文概述一种拟议的高频股票交易算法，使用神经网络预测生成买卖信号。研究比较三种方法：使用交叉熵损失和拟牛顿法优化的逻辑回归、全连接神经网络以及支持向量机。文中给出的理由是，神经网络能够提取股票数据特征并进行分类，而支持向量方法则将观测映射到更高维空间进行分类。

研究称其评估这些模型以确定哪种最适合高频交易，但该描述没有提供数据集、信号规则、延迟或执行假设、表现指标或比较结果。因此，文中说明了模型类别和一般交易概念，但未提供证据证明某个模型表现最佳，或该算法适用于实盘市场。所提供的文本未讨论交易成本、市场冲击和快速交易风险。

核心观点

  • 拟议策略将神经网络预测转化为股票高频交易的买卖信号。
  • 比较范围包括逻辑回归、全连接神经网络和支持向量机。
  • 文中将这些模型作为分类股票数据和生成交易信号的替代方法。
  • 描述未提供数据集或表现结果，无法据此判断比较结论。
  • 所提供的说明未涉及执行成本、延迟和市场冲击。

标签

股票机器学习高频交易交易执行
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# Analysis of frequent trading effects of various machine learning models


# Analysis of frequent trading effects of various machine learning models









In recent years, high-frequency trading has emerged as a crucial strategy in stock trading. This study aims to develop an advanced high-frequency trading algorithm and compare the performance of three different mathematical models: the combination of the cross-entropy loss function and the quasi-Newton algorithm, the FCNN model, and the vector machine. The proposed algorithm employs neural network predictions to generate trading signals and execute buy and sell operations based on specific conditions. By harnessing the power of neural networks, the algorithm enhances the accuracy and reliability of the trading strategy. To assess the effectiveness of the algorithm, the study evaluates the performance of the three mathematical models. The combination of the cross-entropy loss function and the quasi-Newton algorithm is a widely utilized logistic regression approach. The FCNN model, on the other hand, is a deep learning algorithm that can extract and classify features from stock data. Meanwhile, the vector machine is a supervised learning algorithm recognized for achieving improved classification results by mapping data into high-dimensional spaces. By comparing the performance of these three models, the study aims to determine the most effective approach for high-frequency trading. This research makes a valuable contribution by introducing a novel methodology for high-frequency trading, thereby providing investors with a more accurate and reliable stock trading strategy.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。