Stocks Trading Using Relative Strength Index, Moving Average and Reinforcement Learning Techniques: A Case Study of Apple Stock Index

Abstract

With a tremendous increase of the usage of machine learning (ML) in recent years, a method called reinforcement learning (RL) which is a branch of ML has gained a huge attraction, as it has addressed the problem of learning automation of decisions making over time. In case of the financial trading, many approaches such as descriptive, fundamental, and technical analysis are used to make decision on stocks investment. Another approach that this paper aims to explore is the Deep Q-Learning which is also a suitable method to deal with the much more practical problem of financial trading. This paper applies the listed methods of analysis (Descriptive, technical and the Deep Q-Learning) on apple stocks index (AAPL). The paper founds that these techniques can be beneficial to traders and can also help making both long-term and short-term trading investment.