This study aims to address the challenges of futures price prediction in high-frequency trading (HFT) by proposing a continuous learning factor predictor based on graph neural networks. The model integrates multi-factor pricing theories with real-time market dynamics, effectively bypassing the limitations of existing methods that lack financial theory guidance and ignore various trend signals and their interactions. We propose three heterogeneous tasks, including price moving average regression, price gap regression and change-point detection to trace the short-, intermediate-, and long-term trend factors present in the data. In addition, this study also considers the cross-sectional correlation characteristics of future contracts, where prices of different futures often show strong dynamic correlations. Each variable (future contract) depends not only on its historical values (temporal) but also on the observation of other variables (cross-sectional). To capture these dynamic relationships more accurately, we resort to the spatio-temporal graph neural network (STGNN) to enhance the predictive power of the model. The model employs a continuous learning strategy to simultaneously consider these tasks (factors). Additionally, due to the heterogeneity of the tasks, we propose to calculate parameter importance with mutual information between original observations and the extracted features to mitigate the catastrophic forgetting (CF) problem. Empirical tests on 49 commodity futures in China's futures market demonstrate that the proposed model outperforms other state-of-the-art models in terms of prediction accuracy. Not only does this research promote the integration of financial theory and deep learning, but it also provides a scientific basis for actual trading decisions.

翻译：本研究旨在解决高频交易中期货价格预测面临的挑战，提出一种基于图神经网络的持续学习因子预测模型。该模型将多因子定价理论与实时市场动态相结合，有效突破了现有方法缺乏金融理论指导且忽视多种趋势信号及其交互作用的局限。我们提出了三项异构任务，包括价格移动平均回归、价格缺口回归和变化点检测，以追踪数据中存在的短期、中期和长期趋势因子。此外，本研究还考虑了期货合约的截面相关特征——不同期货价格常呈现强动态相关性，每个变量（期货合约）不仅依赖于自身历史值（时间维度），还依赖于其他变量的观测值（截面维度）。为更精准地捕捉这些动态关系，我们采用时空图神经网络（STGNN）增强模型预测能力。模型采用持续学习策略以同时兼顾上述任务（因子），同时针对任务异质性，提出通过原始观测值与提取特征之间的互信息计算参数重要性，从而缓解灾难性遗忘问题。基于中国期货市场49种商品期货的实证测试表明，本模型在预测精度上优于其他现有最优模型。该研究不仅促进了金融理论与深度学习的融合，也为实际交易决策提供了科学依据。