Commodity and Forex trade automation using Deep Reinforcement Learning

Abstract

Machine learning is an application of artificial intelligence based on the theory that machines can learn from data, discern patterns and make decisions with negligible human intervention. With today’s world being inundated by data, machine learning is very relevant due to the amount of learning potential. Machine learning caters to a variety of applications including image recognition, speech recognition, weather prediction, portfolio optimization and so on. The Forex Exchange is a market that allows traders and investors to buy, sell and exchange currencies of various nations. It is regarded as the largest financial market with over 5 trillion American dollars in daily trades, which is larger than the equity and futures markets combined. The Commodity market is a market that allows buying, selling and exchanging of raw materials or primary products. Using the concept of machine learning, this project aims to develop and introduce an agent to automate the trade of a given commodity or currency in a simulated market with the objectives of maximizing returns and minimizing losses for the trader. The model learns from trends in historical market data and is capable of buying, selling or holding a trade at a given instance. The model is validated by running the agent on unseen market data of a later period and the returns generated are analyzed.