Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading

2024/12/26 by Adamantios Ntakaris, Ntakaris, Adamantios, Gbenga Ibikunle +1
Computer Science · Engineering · #Advanced Algorithms and Applications #FOS: Computer and information sciences #FOS: Economics and business #Iterative Learning Control Systems #Machine Learning (cs.LG) #Neural Networks and Applications #Statistical Finance (q-fin.ST)

paper · pdf · doi:10.48550/arxiv.2412.19372

openalex publication_date 2024/12/26 · openalex created_date 2024/12/31 · openalex updated_date 2026/07/28

Abstract

High-frequency trading (HFT) has transformed modern financial markets, making reliable short-term price forecasting models essential. In this study, we present a novel approach to mid-price forecasting using Level 1 limit order book (LOB) data from NASDAQ, focusing on 100 U.S. stocks from the S&P 500 index during the period from September to November 2022. Expanding on our previous work with Radial Basis Function Neural Networks (RBFNN), which leveraged automated feature importance techniques based on mean decrease impurity (MDI) and gradient descent (GD), we introduce the Adaptive Learning Policy Engine (ALPE) - a reinforcement learning (RL)-based agent designed for batch-free, immediate mid-price forecasting. ALPE incorporates adaptive epsilon decay to dynamically balance exploration and exploitation, outperforming a diverse range of highly effective machine learning (ML) and deep learning (DL) models in forecasting performance.

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