基于 VCformer + TPA 多任务深度学习的 A 股横截面股票排序与量化回测系统。 项目将选股问题转化为排序学习问题——不预测具体收益率，而是预测同一交易日股票间的相对强弱次序，规避金融时序高噪声场景下直接回归预测的固有困难。模型融合 VCformer（变量维度注意力）与 TPA（多尺度时序卷积注意力），采用 Pairwise Hinge Loss 作为排序损失，构建多任务学习框架同步优化排序能力与波动率预测精度。 适用场景：量化研究、因子挖掘、策略回测、金融时序深度学习、排序学习选股。
☆25Jul 21, 2026Updated 3 weeks ago
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