Reinforcement Learning for automatic financial trading: Introduction and some applications
AbstractThe 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.
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Bibliographic InfoPaper provided by Department of Economics, University of Venice "Ca' Foscari" in its series Working Papers with number 2012:33.
Date of creation: 2012
Date of revision: 2012
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Postal: Cannaregio, S. Giobbe no 873 , 30121 Venezia
Web page: http://www.unive.it/dip.economia
More information through EDIRC
Financial Trading System; Reinforcement Learning; Stochastic control; Q-learning algorithm; Kernel-based Reinforcement Learning.;
Find related papers by JEL classification:
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- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
- D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search, Learning, and Information
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-01-07 (All new papers)
- NEP-CMP-2013-01-07 (Computational Economics)
- NEP-ORE-2013-01-07 (Operations Research)
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