Machine Learning for Trading

19 Pages Posted: 14 Aug 2017 Last revised: 4 Dec 2017

See all articles by Gordon Ritter

Gordon Ritter

New York University (NYU) - Courant Institute of Mathematical Sciences; City University of New York (CUNY) - Weissman School of Arts and Sciences; Columbia University - Department of Mathematics; Cornell University - Cornell Tech NYC; University of Chicago - Department of Mathematics

Date Written: August 8, 2017

Abstract

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

Ritter, Gordon, Machine Learning for Trading (August 8, 2017). Available at SSRN: https://ssrn.com/abstract=3015609 or http://dx.doi.org/10.2139/ssrn.3015609

Gordon Ritter (Contact Author)

New York University (NYU) - Courant Institute of Mathematical Sciences ( email )

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City University of New York (CUNY) - Weissman School of Arts and Sciences ( email )

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Columbia University - Department of Mathematics ( email )

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Cornell University - Cornell Tech NYC ( email )

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University of Chicago - Department of Mathematics ( email )

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