Trading System using Deep Reinforcement Learning

Abstract

In recent years, investment strategies using artificial intelligence have attracted a significant amount of research attention. However, it is difficult to construct an efficient investment strategy using artificial intelligence owing to the variable factors in market prices. Therefore, this study aims to focus on a trading method called the NT ratio transaction to reduce the number of price-variable factors. This transaction is an arbitrage transaction, which utilizes the difference in the price movements between Nikkei 225 futures and TOPIX futures. These futures generally exhibit similar price movements and even if the price differences expand, they tend to return to their original separation. Using this transaction, we can target profits from this price difference while offsetting a considerable number of price-variable factors. Therefore, in this study, we construct a model to acquire an investment strategy based on NT ratio transactions via deep reinforcement learning and confirm the effectiveness of this model.