Visual object tracking is a popular research area in computer vision due to its diverse applications. Despite the impressive progress made by numerous state-of-the-art trackers on large-scale datasets, visual object tracking at nighttime remains challenging because of low light (brightness) conditions, lack of contrast, very low variability among feature distributions, etc. In addition, the lack of paired (labeled) data for nighttime tracking makes it infeasible for supervised learning based modeling. Unsupervised domain adaptation based tracking can resolve this issue. In this work, we proposed static image style transfer-based Reconstruction Assisted Domain Adaptation (RADA) with adversarial learning for nighttime object tracking. The main contribution of the work is two-fold. First, a reconstruction-assisted adaptation is proposed for domain invariant feature extraction and to achieve input and feature level adaptation. Secondly, static style transfer is used to generate synthetic paired images (video frames) for supervised nighttime modeling for visual object tracking. Style adversarial alignment at multiple levels helped to adapt between the styled source domain and the target domain, which do not require pseudo labels. RADA attained feature and input level adaptation without external model requirements for low-light image enhancement. Static style transfer avoids negative domain transfer and enables domain transfer learning on true labels. The effectiveness of RADA is validated on six benchmark datasets. RADA achieved state-of-the-art results on two benchmark nighttime adaptation datasets with improvements in the range of 3.7% - 11.4%. RADA also attained state-of-the-art results on three other nighttime datasets without target adaptation. The tracking results and model weights are available at https://github.com/chouhan-avinash/RADA/ .