Computational intelligence for evolving trading rules

Authors:
Adam Ghandar;Zbigniew Michalewicz;Martin Schmidt;Thuy-Duong Tô;Ralf Zurbrugg
Affiliations:
School of Computer Science, University of Adelaide, Adelaide, SA, Australia;School of Computer Science, University of Adelaide, Adelaide, SA, Australia and Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland and Polish-Japanese Institute of Informati ...;SolveIT Software Pty Ltd., Adelaide, Australia;School of Commerce, University of Adelaide, Adelaide, SA, Australia;School of Commerce, University of Adelaide, Adelaide, SA, Australia
Venue:
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
Year:
2009

Citing 10
Cited 7

Combining artificial neural networks and statistics for stock-market forecasting

CSC '93 Proceedings of the 1993 ACM conference on Computer science
Noisy Time Series Prediction using Recurrent Neural Networks and Grammatical Inference

Machine Learning
Generating trading rules on the stock markets with genetic programming

Computers and Operations Research
Dynamic Asset Allocation for Stock Trading Optimized by Evolutionary Computation

IEICE - Transactions on Information and Systems
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)

Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Robust Artificial Neural Networks for Pricing of European Options

Computational Economics
A neural evolutionary approach to financial modeling

Proceedings of the 8th annual conference on Genetic and evolutionary computation
Designing safe, profitable automated stock trading agents using evolutionary algorithms

Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving short-term trading strategies using genetic programming

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Bankruptcy prediction for credit risk using neural networks: A survey and new results

IEEE Transactions on Neural Networks

Learning Fuzzy Rules with Evolutionary Algorithms -- An Analytic Approach

Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
An Introduction to Natural Computing in Finance

EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Evaluation of intelligent quantitative hedge fund management

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Application notes: dynamic physical behavior analysis for financial trading decision support

IEEE Computational Intelligence Magazine
An inter-market arbitrage trading system based on extended classifier systems

Expert Systems with Applications: An International Journal
Evolving fuzzy rules: evaluation of a new approach

SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Enhancing profitability through interpretability in algorithmic trading with a multiobjective evolutionary fuzzy system

PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results of applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.