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

TL;DR

This paper introduces ALPE, a reinforcement learning-based adaptive engine for real-time mid-price forecasting in high-frequency trading, outperforming traditional ML and DL models using order book data.

Contribution

We develop ALPE, a novel RL-based policy engine that provides batch-free, immediate price forecasts, advancing HFT prediction methods beyond previous neural network approaches.

Findings

ALPE outperforms existing ML and DL models in forecasting accuracy.

Adaptive epsilon decay improves exploration-exploitation balance.

Real-time, batch-free predictions enhance HFT decision-making.

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.