Abstract: In the financial classification mission, due to the big noise and low information-ratio in financial data, traditional supervised-learning regime may extend the noise influence because of the over dependent on the data label. GAN(generative adversarial network) can learn the data characters and reduce the influence of noise. When it is used to analyze the financial data, it has great results. We apply GAN to the high frequency trading: set the data labeled or unlabeled based on its volatility, then use the adversarial training between generative network G and discriminative network D to learn the intrinsic characters of the data, finally use the well trained D to get the up and down classification model and the quantization strategy. The sample is based on the future data, and the final results show that the LSTM model training by GAN is better than the deep learning models such as LSTM with supervised training and the Logistic regression model.