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.

翻译：高频交易（HFT）已经改变了现代金融市场，使得可靠的短期价格预测模型变得至关重要。在本研究中，我们提出了一种利用纳斯达克一级限价订单簿（LOB）数据进行中间价预测的新方法，重点关注2022年9月至11月期间标准普尔500指数中的100只美国股票。在我们先前利用基于平均不纯度减少（MDI）和梯度下降（GD）的自动特征重要性技术、采用径向基函数神经网络（RBFNN）的研究基础上，我们引入了自适应学习策略引擎（ALPE）——一种基于强化学习（RL）的智能体，专为无需批处理、即时进行中间价预测而设计。ALPE融入了自适应ε衰减机制，以动态平衡探索与利用，在预测性能上超越了一系列高效能的机器学习（ML）和深度学习（DL）模型。