Paper Info
Title
Threshold recurrent reinforcement learning model for automated trading
Abstract
This paper presents the threshold recurrent reinforcement learning (TRRL) model and describes its application in a simple automated trading system. The TRRL is a regime-switching extension of the recurrent reinforcement learning (RRL) algorithm. The basic RRL model was proposed by Moody and Wu (1997) and used for uncovering trading strategies. We argue that the RRL is not sufficiently equipped to capture the non-linearities and structural breaks present in financial data, and propose the TRRL model as a more suitable algorithm for such environments. This paper gives a detailed description of the TRRL and compares its performance with that of the basic RRL model in a simple automated trading framework using daily data from four well-known European indices. We assume a frictionless setting and use volatility as an indicator variable for switching between regimes. We find that the TRRL produces better trading strategies in all the cases studied, and demonstrate that it is more apt at finding structure in non-linear financial time series than the standard RRL.
Year
DOI
Venue
2010
10.1007/978-3-642-12242-2_22
EvoApplications (2)
Keywords
Field
DocType
trading strategy,reinforcement learning,structural break
Regime switching,Trading strategy,Computer science,Dummy variable,Artificial intelligence,Volatility (finance),Algorithmic trading,Reinforcement learning
Conference
Volume
ISSN
ISBN
6025
0302-9743
3-642-12241-8
Citations 
PageRank 
References 
5
0.58
7
Authors
2
Name
Order
Citations
PageRank
Dietmar Maringer115611.35
Tikesh Ramtohul2121.23