Reinforcement Learning for automatic financial trading: Introduction and some applications
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.
|Date of creation:||2012|
|Date of revision:||2012|
|Contact details of provider:|| Postal: |
Web page: http://www.unive.it/dip.economia
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:ven:wpaper:2012:33. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Geraldine Ludbrook)
If references are entirely missing, you can add them using this form.