Electricity price forecasting remains a critical research focus in modern power systems, where future electricity prices are influenced not only by historical electricity price patterns but also by exogenous factors. Traditional approaches often employ single-stream recurrent neural networks to integrate exogenous variables with historical electricity price sequences. However, since historical electricity prices and exogenous information inherently represent distinct modalities, single-stream architectures are suboptimal for processing such multimodal data, leading to inefficient feature extraction. To address this limitation, we propose DSFormer, a dual-stream transformer framework designed for electricity price forecasting. This dual-stream architecture employs parallel processing, with the parsing stream handling historical electricity price sequences and the exogenous stream processing exogenous variables. Specifically, DSFormer comprises an encoder and a decoder. The encoder incorporates two key components: a fusion mutual attention mechanism and a multispace feedforward network. The fusion mutual attention mechanism comprises a pair of mutual attention layers, followed by a fusion attention layer. The mutual attention layers independently inject historical electricity price information into the exogenous stream and exogenous information into the parsing stream, while the fusion attention layer further refines the features from dual streams. The multispace feedforward network projects the features from dual streams into subspaces and performs cross-subspace interactions based on the gating mechanism, thereby enhancing representation capabilities while achieving enhanced computational efficiency. Finally, the decoder infers future electricity prices from these refined features. Extensive experimental results demonstrate that DSFormer outperforms existing state-of-the-art methods in terms of prediction accuracy while maintaining lower computational complexity.