Stock Trading System Based on Machine Learning and Kelly Criterion in Internet of Things

Abstract

The evolution of the Internet of Things (IoT) has promoted the prevalence of the financial industry as a variety of stock prediction models have been able to accurately predict various IoT-based financial services. In practice, it is crucial to obtain relatively accurate stock trading signals. Considering various factors, finding profitable stock trading signals is very attractive to investors, but it is also not easy. In the past, researchers have been devoted to the study of trading signals. A genetic algorithm (GA) is often used to find the optimal solution. In this study, a long short-term (LSTM) memory neural network is used to study stock price fluctuations, and then, genetic algorithms are used to obtain appropriate trading signals. A genetic algorithm is a search algorithm that solves optimization. In this paper, the optimal threshold is found to determine the trading signal. In addition to trading signals, a suitable trading strategy is also crucial. In addition, this research uses the Kelly criterion for fund management; that is, the Kelly criterion is used to calculate the optimal investment score. Effective capital management can not only help investors increase their returns but also help investors reduce their losses.