Q-Learning-Based Financial Trading: Some Results and Comparisons

Abstract

In this paper, we consider different financial trading systems (FTSs) based on a Reinforcement Learning (RL) methodology known as Q-Learning (QL). QL is a machine learning method which real-time optimizes its behavior in relation to the responses it gets from the environment as a consequence of its acting. In the paper, first we introduce the essential aspects of RL and QL which are of interest for our purposes, then we present some original and differently configurated FTSs based on QL, finally we apply such FTSs to eight time series of daily closing stock returns from the Italian stock market.