Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks

TL;DR

This paper introduces an automated machine learning protocol for high-frequency stock price forecasting that combines feature importance mechanisms and clustering within RBF neural networks, improving prediction efficiency.

Contribution

It presents a novel autonomous approach integrating dual feature importance and k-means clustering into RBFNNs for HFT stock forecasting, reducing manual tuning.

Findings

Enhanced forecasting accuracy for HFT stock data.

Automated feature selection improves model adaptability.

Method reduces manual effort in model training.

Abstract

This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importance mechanism and clustering via shallow neural network topology for fast training. By incorporating the k-means algorithm into the radial basis function neural network (RBFNN), the proposed method addresses the challenges of manual clustering and the reliance on potentially uninformative features. More specifically, our approach involves a dual competitive mechanism for feature importance, combining the mean-decrease impurity (MDI) method and a gradient descent (GD) based feature importance mechanism. This approach, tested on HFT Level 1 order book data for 20 S&P 500 stocks, enhances the forecasting ability of the RBFNN regressor. Our findings suggest that an autonomous approach to feature selection and clustering is crucial, as each stock requires a different input feature space. Overall, by automating the feature selection and clustering processes, we remove the need for manual topological grid search and provide a more efficient way to predict LOB's mid-price.