用自动聚类与神经网络预测高频股票价格，提升预测效率。
Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks
- 通过双机制自动评估特征重要性，避免人工选择。
- 在20只标普500股票的订单簿数据上提升预测准确率。
- 适合需要快速建模的量化交易开发者参考。
本研究提出一种用于高频交易（HFT）股票价格预测的自主机器学习框架，结合浅层神经网络拓扑与k-means算法，实现特征自动聚类与选择。该方法引入双重特征重要性评估机制：均方减少不纯度（MDI）与基于梯度下降（GD）的方法。在20只标普500成分股的高频订单簿（Level 1 LOB）数据上测试，显著提升了径向基函数神经网络（RBFNN）的回归预测能力。结果表明，每只股票需独立优化输入特征空间，自动化处理可替代传统手动调参与网格搜索，更高效地预测订单簿中间价。
原文摘要 · Abstract (English)
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.
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