Paper Info
Title
Geometric Semantic Genetic Programming for Financial Data.
Abstract
We cast financial trading as a symbolic regression problem on the lagged time series, and test a state of the art symbolic regression method on it. The system is geometric semantic genetic programming, which achieves good performance by converting the fitness landscape to a cone landscape which can be searched by hill-climbing. Two novel variants are introduced and tested also, as well as a standard hill-climbing genetic programming method. Baselines are provided by buy-and-hold and ARIMA. Results are promising for the novel methods, which produce smaller trees than the existing geometric semantic method. Results are also surprisingly good for standard genetic programming. New insights into the behaviour of geometric semantic genetic programming are also generated.
Year
DOI
Venue
2014
10.1007/978-3-662-45523-4_18
Lecture Notes in Computer Science
Keywords
Field
DocType
Automated trading,Commodity,Exchange rate,Index,Genetic programming,Semantics,Fitness landscape,Hill-climbing
Hill climbing,Fitness landscape,Computer science,Genetic programming,Autoregressive integrated moving average,Artificial intelligence,Genetic representation,Finance,Symbolic regression,Algorithmic trading,Machine learning,Semantics
Conference
Volume
ISSN
Citations 
8602
0302-9743
2
PageRank 
References 
Authors
0.38
7
4
Name
Order
Citations
PageRank
James McDermott112714.17
Alexandros Agapitos221122.88
Anthony Brabazon391898.60
Michael O'Neill487669.58