Modern deep learning for asset allocation typically separates forecasting from optimization. We argue this creates a fundamental mismatch where minimizing prediction errors fails to yield robust portfolios. We propose the Signature Informed Transformer to address this by unifying feature extraction and decision making into a single policy. Our model employs path signatures to encode complex path dependencies and introduces a specialized attention mechanism that targets geometric asset relationships. By directly minimizing the Conditional Value at Risk we ensure the training objective aligns with financial goals. We prove that our attention module rigorously amplifies signature derived signals. Experiments across diverse equity universes show our approach significantly outperforms both traditional strategies and advanced forecasting baselines. The code is available at: https://anonymous.4open.science/r/Signature-Informed-Transformer-For-Asset-Allocation-DB88

翻译：现代深度学习在资产配置领域通常将预测与优化分离。我们认为这种做法存在根本性不匹配：最小化预测误差并不能产生稳健的投资组合。为此，我们提出基于路径签名的Transformer模型，通过将特征提取与决策制定统一到单一策略中来解决这一问题。该模型采用路径签名编码复杂的路径依赖关系，并引入专门针对资产几何关系的注意力机制。通过直接最小化条件风险价值，我们确保训练目标与金融目标保持一致。我们证明了该注意力模块能严格增强基于路径签名的信号。在多样化股票池中的实验表明，我们的方法显著优于传统策略和先进的预测基线模型。代码发布于：https://anonymous.4open.science/r/Signature-Informed-Transformer-For-Asset-Allocation-DB88