The financial industry is increasingly seeking robust methods to address the challenges posed by data scarcity and low signal-to-noise ratios, which limit the application of deep learning techniques in stock market analysis. This paper presents two innovative generative model-based approaches to synthesize stock data, specifically tailored for different scenarios within the A-share market in China. The first method, a sector-based synthesis approach, enhances the signal-to-noise ratio of stock data by classifying the characteristics of stocks from various sectors in China's A-share market. This method employs an Approximate Non-Local Total Variation algorithm to smooth the generated data, a bandpass filtering method based on Fourier Transform to eliminate noise, and Denoising Diffusion Implicit Models to accelerate sampling speed. The second method, a recursive stock data synthesis approach based on pattern recognition, is designed to synthesize data for stocks with short listing periods and limited comparable companies. It leverages pattern recognition techniques and Markov models to learn and generate variable-length stock sequences, while introducing a sub-time-level data augmentation method to alleviate data scarcity issues.We validate the effectiveness of these methods through extensive experiments on various datasets, including those from the main board, STAR Market, Growth Enterprise Market Board, Beijing Stock Exchange, NASDAQ, NYSE, and AMEX. The results demonstrate that our synthesized data not only improve the performance of predictive models but also enhance the signal-to-noise ratio of individual stock signals in price trading strategies. Furthermore, the introduction of sub-time-level data significantly improves the quality of synthesized data.