Stock price fluctuations are characterized by randomness and complexity, making accurate prediction a challenging task in financial markets. This paper proposes a dual-modal stock price change rate forecasting framework based on pretrained models (DMFPre) to improve the accuracy of stock price change rate forecasting. The framework consists of two main modules: on one hand, it utilizes a pretrained language model combined with company-related textual data to predict the weekly stock price change rate range. The prediction accuracy is closely related to the design of the prompts, and manually designed prompts not only have significant room for optimization but also involve time-consuming and labor-intensive adjustments. To address this, the paper introduces an automatic prompt optimization method that automatically updates the prompts through algorithms. On the other hand, the framework employs a pretrained time series forecasting model (PTSFM) to predict the daily price change rate trend based on historical stock trading data. Finally, by integrating the prediction results from both modules, the framework generates more accurate stock price change rate forecasting. Experimental results show that on the CSI 100 constituent stock dataset, the DMFPre framework significantly outperforms traditional single-modal time series forecasting models in terms of prediction accuracy.