Machine Learning for Trading
19 Pages Posted: 14 Aug 2017 Last revised: 4 Dec 2017
Date Written: August 8, 2017
In multi-period trading with realistic market impact, determining the dynamic trading strategy that optimizes expected utility of final wealth is a hard problem. In this paper we show that, with an appropriate choice of the reward function, reinforcement learning techniques (specifically, Q-learning) can successfully handle the risk-averse case. We provide a proof of concept in the form of a simulated market which permits a statistical arbitrage even with trading costs. The Q-learning agent finds and exploits this arbitrage.
Keywords: Finance, Investment Analysis, Machine Learning, Portfolio Optimization
JEL Classification: G11, C61
Suggested Citation: Suggested Citation