Abstract
The construction of automated financial trading systems (FTSs) is a subject of high interest for both the academic environment and the financial one due to the potential promises by self-learning methodologies. In this paper we consider Reinforcement Learning (RL) type algorithms, that is algorithms that real-time optimize their behavior in relation to the responses they get from the environment in which they operate, without the need for a supervisor. In particular, first we introduce the essential aspects of RL which are of interest for our purposes, second we present some original automatic FTSs based on differently configured RL-based algorithms, then we apply such FTSs to artificial and real time series of daily stock prices. Finally, we compare our FTSs with a classical one based on Technical Analysis indicators. All the results we achieve are generally quite satisfactory.
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Bertoluzzo, F., Corazza, M. (2014). Reinforcement Learning for Automated Financial Trading: Basics and Applications. In: Bassis, S., Esposito, A., Morabito, F. (eds) Recent Advances of Neural Network Models and Applications. Smart Innovation, Systems and Technologies, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-04129-2_20
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DOI: https://doi.org/10.1007/978-3-319-04129-2_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-04128-5
Online ISBN: 978-3-319-04129-2
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