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.

Q-Learning-based financial trading: some results and comparisons

Marco Corazza
2020

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.
Progresses in Artificial Intelligence and Neural Systems
File in questo prodotto:
File Dimensione Formato  
Q_Learning_based_financial_trading_some_results_and_comparisons.pdf

non disponibili

Descrizione: Articolo.
Tipologia: Versione dell'editore
Licenza: Accesso chiuso-personale
Dimensione 1.45 MB
Formato Adobe PDF
1.45 MB Adobe PDF   Visualizza/Apri

I documenti in ARCA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3732139
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact