Reinforcement Learning for automatic financial trading: Introduction and some applications

Francesco Bertoluzzo and Marco Corazza ()
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Francesco Bertoluzzo: Department of Economics, University Ca� Foscari of Venice

No 2012:33, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: The construction of automatic Financial Trading Systems (FTSs) is a subject of research of high interest for both academic environment and financial one due to the potential promises by self-learning methodologies and by the increasing power of actual computers. In this paper we consider Reinforcement Learning (RL) type algorithms, that is algorithms that 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, then we present some original automatic FTSs based on differently configured RL algorithms and apply such FTSs to artificial and real time series of daily financial asset prices.

Keywords: Financial Trading System; Reinforcement Learning; Stochastic control; Q-learning algorithm; Kernel-based Reinforcement Learning. (search for similar items in EconPapers)
JEL-codes: C61 C63 D83 G11 (search for similar items in EconPapers)
Pages: 15
Date: 2012, Revised 2012
New Economics Papers: this item is included in nep-cmp and nep-ore
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2012:33

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