用强化学习实时预测股票中间价，比多种模型更准。
Minimal Batch Adaptive Learning Policy Engine for Real-Time Mid-Price Forecasting in High-Frequency Trading
- 设计自适应学习策略引擎，无需分批处理，即时预测价格。
- 在100只标普500股票上，性能超越多种主流机器学习与深度学习模型。
- 通过动态调整探索与利用平衡，提升高频交易中的预测稳定性。
高频交易已重塑现代金融市场，可靠的短期价格预测模型至关重要。本研究基于2022年9月至11月期间纳斯达克的100只标普500成分股的Level 1限价订单簿（LOB）数据，提出一种新型中间价预测方法。在先前基于径向基函数神经网络（RBFNN）并结合均方误差下降（MDI）与梯度下降（GD）自动特征重要性分析的基础上，本文引入自适应学习策略引擎（ALPE）——一种基于强化学习（RL）的代理，实现无批次、即时的中间价预测。ALPE采用自适应ε衰减机制，动态平衡探索与利用，在预测性能上显著优于多种高效机器学习（ML）与深度学习（DL）模型。
原文摘要 · Abstract (English)
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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