Research on High Frequency Financial Transaction Data Modeling and Cloud Computing Implementation Based on SSA-GA-BP Model

This article aims to study the modeling of high-frequency financial trading data and its cloud computing implementationWith the swift advancement of big data, cloud computing, and AI technologies, algorithmic trading has emerged as a crucial tool in today’s financial sector. Especially in high-frequency trading, programmatic trading can capture micro changes in the market in real time, thereby improving the efficiency of trading decisions. To enhance the precision of stock price forecasting in high-frequency trading, this paper presents a forecasting approach utilizing the SSA-GA-BP model.In particular, the BP neural network is initially employed for forecasting stock prices, while the evolutionary method and sparrow optimization algorithm are applied to optimize the weights and thresholds of the network, thereby solving the problems of local extremum and convergence speed of the BP model. Secondly, combining with entanglement morphology, two high-frequency trading strategies were designed and implemented, and the strategies were optimized with the support of SSA-GA-BP model, improving the accuracy of buying and selling point recognition and trading stability. The results from the experiments demonstrate that the suggested model has strong fitting ability in predicting stock prices in high-frequency trading, and the optimized trading strategy can provide a higher Sharpe ratio, demonstrating stable return ability.