Self-Evolving Trading Strategy Integrating Internet of Things and Big Data

Abstract

In the era of Internet of Things (IoT) and big data, data has increased dramatically. Computers have been used in various fields. Algorithmic trading is beginning to develop rapidly in the trading market, more and more algorithms begin to be used in the transaction market. As a form of machine learning, neural network can fully reveal the complex trading market. Based on the characteristics of commodity futures market, this paper chooses back propagation neural network to establish price forecasting model. And then, according to the rules of futures market, a self-evolving commodity futures trading strategy is proposed. We also use the data of the Shanghai Futures Exchange and the Dalian Futures Exchange to back-testing the strategy. Finally, we compare the proposed strategies and traditional strategies, and illustrate the evolution of our strategy. Experiments show that our strategies are superior to other compared strategies in the proposed evaluation indicator. Our strategy has a good performance both in yield and risk. It also proves the feasibility of the model and the strategy. The research of this paper is significant to the research of the futures market, and it also provides a new idea for the application of machine learning in algorithmic trading.