Research on the Feasibility of Applying GRU and Attention Mechanism Combined with Technical Indicators in Stock Trading Strategies

Abstract

The vigorous development of Time Series Neural Network in recent years has brought many potential possibilities to the application of financial technology. This research proposes a stock trend prediction model that combines Gate Recurrent Unit and Attention mechanism. In the proposed framework, the model takes the daily opening price, closing price, highest price, lowest price and trading volume of stocks as input, and uses technical indicator transition prediction as a label to predict the possible rise and fall probability of future trading days. The research results show that the proposed model and labels designed by this research can effectively predict important stock price fluctuations and can be effectively applied to financial commodity trading strategies.