Portfolio Trading of Financial Products Based on Machine Learning

Abstract

In order to study how to construct a suitable portfolio trading strategy of traditional financial products and new kinds of financial products to help investors avoid risks and obtain more returns, we use pair trading models, polynomial regression models, and a machine learning-based combined model we designed to make a simulated trading. In the simulation of gold and bitcoin trading, our combined model achieved better results and avoided the shortcomings of the pair trading model and the polynomial regression model. We suggest that investors add constraints to the combined model according to the actual situation of financial products, and use it to forecast and make decisions on portfolio tradings.