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Q-Learning-Based Financial Trading Systems with Applications

25 Pages Posted: 11 Oct 2014 Last revised: 23 May 2015

Marco Corazza

Ca Foscari University of Venice - Dipartimento di Economia

Francesco Bertoluzzo

Ca Foscari University of Venice

Date Written: October 9, 2014

Abstract

The design of financial trading systems (FTSs) is a subject of high interest both for the academic environment and for the professional one due to the promises by machine learning methodologies. In this paper we consider the Reinforcement Learning-based policy evaluation approach known as Q-Learning algorithm (QLa). QLa is an algorithm which real-time optimizes its behavior in relation to the responses it gets from the environment in which it operates. In particular: first we introduce the essential aspects of QLa which are of interest for our purposes; second we present some original FTSs based on differently configured QLas; then we apply such FTSs to an artificial time series of daily stock prices and to six real ones from the Italian stock market belonging to the FTSE MIB basket. The results we achieve are generally satisfactory.

Keywords: Financial trading system, Reinforcement Learning, Q-Learning algorithm, daily stock price time series, FTSE MIB basket

JEL Classification: C61, C63, G11

Suggested Citation

Corazza, Marco and Bertoluzzo, Francesco, Q-Learning-Based Financial Trading Systems with Applications (October 9, 2014). University Ca' Foscari of Venice, Dept. of Economics Working Paper Series No. 15/WP/2014. Available at SSRN: https://ssrn.com/abstract=2507826 or http://dx.doi.org/10.2139/ssrn.2507826

Marco Corazza (Contact Author)

Ca Foscari University of Venice - Dipartimento di Economia ( email )

Cannaregio 873
Venice, 30121
Italy

Francesco Bertoluzzo

Ca Foscari University of Venice ( email )

Dorsoduro 3246
Veneto 30123

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