If machine learning can be applied to trading, what will it mean for humans?

A new academic paper, Machine Learning for Trading, is the first conclusive study that shows success in having a machine learning-based trading strategy. The author, Gordon Ritter, Adjunct Professor in the Mathematics in Finance Program, New York University, constructed an artificial system which he knew would admit a profitable strategy, to see if a machine would find it. In order to train a machine-learning algorithm to behave as a rational risk-averse investor required appropriate reinforcement learning, specifically a mathematical technique called Q-learning (playing some sort of game where you are trying to maximise the reward function that may occur at several periods in the future). The machine learning agent found and exploited arbitrage opportunities in the presence of transaction costs in a simulated market proof of concept. Ritter explained: "I was really trying to answer the question, does machine learning have any application to trading at all, or no application; sort of a binary question. Can machine learning be applied to the problem of trading? "I reasoned that in a system that I know admits a profitable trading strategy, because I constructed it that way, can the machine find it.