A comparison among Reinforcement Learning algorithms in financial trading systems

Marco Corazza (), Giovanni Fasano (), Riccardo Gusso and Raffaele Pesenti ()
Additional contact information
Raffaele Pesenti: Department of Management, Ca' Foscari University of Venice

No 2019:33, Working Papers from Department of Economics, University of Venice "Ca' Foscari"

Abstract: In this work we analyze and implement different Reinforcement Learning (RL) algorithms in financial trading system applications. RL-based algorithms applied to financial systems aim to find an optimal policy, that is an optimal mapping between the variables describing the state of the system and the actions available to an agent, by interacting with the system itself in order to maximize a cumulative return. In this contribution we compare the results obtained considering different on-policy (SARSA) and off-policy (Q-Learning, Greedy-GQ) RL algorithms applied to daily trading in the Italian stock market. We consider both computational issues related to the implementation of the algorithms, and issues originating from practical application to real stock markets, in an effort to improve previous results while keeping a simple and understandable structure of the used models.

Keywords: Reinforcement Learning; SARSA; Q-Learning; Greedy-GQ; financial trading system; Italian FTSE Mib stock market. (search for similar items in EconPapers)
JEL-codes: C53 C54 E37 G17 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2019
New Economics Papers: this item is included in nep-cmp, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.unive.it/web/fileadmin/user_upload/dip ... so_pesenti_33_19.pdf First version, anno (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ven:wpaper:2019:33

Access Statistics for this paper

More papers in Working Papers from Department of Economics, University of Venice "Ca' Foscari" Contact information at EDIRC.
Bibliographic data for series maintained by Sassano Sonia ().