Multi-view automatic translational correction (ATC) in coronary angiography (CAG) is critical for intraoperative automatic diagnosis, in which deep learning playing a key role. However, heartbeat-induced soft matching errors and costly annotations make it difficult to build high-quality, large-scale datasets for calibration algorithm training. The training of clinical models is difficult to fulfill, as existing datasets differ significantly from real CAG in both style and structure. To address this challenge, we propose a novel high-quality data synthesis method for annotation-free ATC. We fully automated the construction of a labeled, high-fidelity dataset for training matching models. An evolutionary algorithm is introduced for global optimization of translation estimation, mitigating epipolar constraint violations caused by vascular deformation and enabling reliable correction across large viewpoint differences. Furthermore, a theoretical analysis is presented, demonstrating that error propagation between adjacent views is more accurate than direct estimation across distant views. Our experiments on clinical datasets demonstrate that our method not only significantly outperforms weakly supervised learning approaches, but also performs comparably to fully supervised methods. Moreover, it exhibits remarkable multicenter generalizability.