Paper Info
Title
Comparison of trade decision strategies in an equity market GA trader
Abstract
This paper investigates different trade decision strategies under different market conditions so that a genetic algorithm could be designed to use the appropriate decision strategy. A trade decision strategy defines how a single action is decided upon based on a number of signals where each signal is a result of a technical analysis function. Using historical market data, a population is trained using a simple genetic algorithm employing crossover and mutation. Four genetic algorithms are used to evolve agents to trade, where each genetic algorithm uses a different trade decision strategy. The best individual from each evolved population is compared using an out-of-sample data set. Results show a significant difference in performance between the four decision strategies especially within bearish to moderately bullish stocks. Populations evolved using a weighted decision strategy performs better than strategies that are not weighted when trading bearish to moderately bullish stocks. Non-weighted decision strategies appear to out-perform weighted strategies when used on extremely bullish stock. This out-performance could be attributed to fewer trades made by non-weighted strategies compared to weighted ones.
Year
DOI
Venue
2011
10.1109/CIFER.2011.5953553
Computational Intelligence for Financial Engineering and Economics
Keywords
Field
DocType
commerce,decision making,genetic algorithms,bullish stock,equity market GA trader,genetic algorithm,historical market data,nonweighted decision strategies,technical analysis function,trade decision strategies
Econometrics,Population,Financial economics,Optimal decision,Crossover,Equity (finance),Stock (geology),Market data,Genetic algorithm,Technical analysis,Business
Conference
ISSN
ISBN
Citations 
pending
978-1-4244-9933-5
0
PageRank 
References 
Authors
0.34
7
3
Name
Order
Citations
PageRank
Jason F. Nicholls100.34
Katherine Malan216212.77
Andries P. Engelbrecht366061.64