Paper Info
Title
Stateful program representations for evolving technical trading rules
Abstract
A family of stateful program representations in grammar-based Genetic Programming are being compared against their stateless counterpart in the problem of binary classification of sequences of daily prices of a financial asset. Empirical results suggest that stateful classifiers learn as fast as stateless ones but generalise better to unseen data, rendering this form of program representation strongly appealing to the automatic programming of technical trading rules.
Year
DOI
Venue
2011
10.1145/2001858.2001969
GECCO (Companion)
Keywords
Field
DocType
program representation,empirical result,daily price,stateless counterpart,stateful program representation,grammar-based genetic programming,technical trading rule,automatic programming,financial asset,binary classification,stateful classifier
Binary classification,Computer science,Genetic programming,Grammar,Artificial intelligence,Stateful firewall,Rendering (computer graphics),Stateless protocol,Machine learning,Automatic programming,Technical analysis
Conference
Citations 
PageRank 
References 
2
0.42
5
Authors
3
Name
Order
Citations
PageRank
Alexandros Agapitos121122.88
Michael O'Neill287669.58
Anthony Brabazon391898.60