A novel trading strategy based on BiLSTM prediction model

Abstract

In order to assist traders with scientific transaction strategy, this paper constructs a decision model including a prediction model and a trading model. Our predicting model is based on a BiLSTM (Bi-direction Long Short-Term Memory) neural network, and two linear layers trained by previous known data, which obtains a high accurate prediction of price. For the trading model, we construct it with several impact and quantitative artificial-selected-factors including market potential, deviation rate, risk score, etc. To use the model, we firstly uses previous known data to train the deep neural network and utilize it to make future predictions, the results of which is then imported into our trading model for decision making and better configure the portfolio. Generally speaking, our whole decision system achieves effective prediction of price, enables timely risk assessment, and makes scientific decisions by considering these factors together.