The main input characteristics of the existing wind power prediction models include meteorological data and power data. Aiming at the difficulties such as obtaining high-precision meteorological data, expressing the potential relationships between data, and slow convergence of the prediction models, the work proposed new ultra-short-term wind power prediction method which could extend the financial factors based on extreme gradient boosting regression tree algorithm (XGBoost). A prediction model which could derive financial factors based on wind power timing data was proposed. The application of XGBoost algorithm with high prediction accuracy and fast training speed in prediction could help the prediction model converge quickly. The relevant experiment was conducted for verification with the wind power data set of a wind farm in Inner Mongolia of China and that of Tennet in Germany. The results showed that taking R² score as an example, the proposed method improved by about 14.71% compared with the benchmark method. The total time for modeling and prediction of the proposed method did not exceed 500 ms.