Trading System of Boom Detection using Reinforcement Learning

Abstract

In this paper, we apply reinforcement learning algorithm to enhance return from technical trading in the Japanese stock market. Specifically, we employ MACD (Moving Average Conversion Diversion) signals to make buy / sell decision. MACD trading signal generates good return when the market is in a trending state but performs poorly when in a box-range state. Reinforcement learning endeavors to identify the state ex-ante and helps traders efficiently allocate capital. We demonstrate how we design our trading system and show the results of our simulations. Our results indicate the reinforcement learning of technical trading signals dramatically improves returns.