Time series prediction methods based on deep learning have been widely used in quantitative trading. However, the price of virtual currency represented by Bitcoin has random fluctuation characteristics, which is extremely misleading for time series prediction. In this paper, a virtual currency quantitative trading model is established, which uses a convolution long short term memory (ConvLSTM) deep learning method to predict the transaction price, and uses the evaluation model composed of Chandler momentum oscillator (CMO), percentage price oscillator (PPO), stop and reverse(SAR) and other economic indicators to make further decisions. The model quantitatively classifies the random wandering characteristics by fusing economic indicators and extracts the symmetric economic laws among them, making full use of deep learning methods to extract spatial and temporal features within the data. The 2016–2021 Bitcoin value dataset published on Kaggle was used for simulated investment. The results show that compared with other existing decision models, it shows better performance and robustness, and shows good stability in dealing with the interdependence of long-term and short-term data. Our work provides a new idea for short-term prediction of long time series data affected by multiple complex factors: coupling deep learning methods with prior knowledge to complete prediction and decision making.