Analysis of frequent trading effects of various machine learning models

TL;DR

This paper compares three machine learning models—logistic regression, FCNN, and SVM—for high-frequency stock trading, introducing a novel algorithm that leverages neural networks to improve trading accuracy and reliability.

Contribution

It presents a new high-frequency trading algorithm that integrates neural network predictions and compares the effectiveness of three different mathematical models.

Findings

The neural network-based algorithm improves trading accuracy.

FCNN outperforms traditional models in classification tasks.

The study identifies the most effective model for high-frequency trading.

Abstract

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.