In recent years, the quantitative investment models based on artificial intelligence algorithms have been emerging in the field of quantitative finance. These models attempt to model the financial time series through artificial intelligence methods, thereby forecasting data and developing an investment strategy. Regarding the unreliable prediction of the traditional Long Short Term Memory (LSTM) model for financial time series, we propose an improved LSTM model. The attention mechanism is added into the LSTM layer to enhance the forecasting performance of the neural network, and the Genetic Algorithm (GA) is used to optimize parameters, thus improving the model’s generalization ability. The data of China’s stock indexes and futures from the January 2019 to May 2020 is selected for the comparative experiments with state-of-the-art algorithms. The results show that the improved model performs better than other models in every indicators, proving the effect application of the model to future investment.