Reinforcement Learning: a Q-Learning Algorithm for High Frequency Trading

Abstract

The scope of this work is to test the implementation of an automated trading system based on Reinforcement Learning: a machine learning algorithm in which an intelligent agent acts to maximize its rewards given the environment around it. Indeed, given the environmental inputs and the environmental responses to the actions taken, the agent will learn how to behave in best way possible. In particular, in this work, a Q-Learning algorithm has been used to produce trading signals on the basis of high frequency data of the Limit Order Book for some selected stocks.