Trend following deep Q‐Learning strategy for stock trading. Issue 4 (29th December 2019)

• Record Type: Journal Article
• Authors: Chakole, Jagdish; Kurhekar, Manish
• Other Names: Saen Reza Farzipoor guestEditor.; Song Malin guestEditor.; Fisher Ron guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-29
• Subjects: deep learning -- reinforcement learning -- trend following; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12514
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q‐Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q‐Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting‐based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.