Intraday FX Trading: An Evolutionary Reinforcement Learning Approach
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
System for foreign exchange trading using genetic algorithms and reinforcement learning
International Journal of Systems Science
Designing safe, profitable automated stock trading agents using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Evolving neural networks for static single-position automated trading
Journal of Artificial Evolution and Applications - Regular issue
Dynamic High Frequency Trading: A Neuro-Evolutionary Approach
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
An heterogeneous, endogenous and coevolutionary GP-based financial market
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
"Dead" Chromosomes and Their Elimination in the Neuro-Genetic Stock Index Prediction System
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Computationally efficient FLANN-based intelligent stock price prediction system
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An automated FX trading system using adaptive reinforcement learning
Expert Systems with Applications: An International Journal
A system for efficient portfolio management
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Evolutionary single-position automated trading
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Interday foreign exchange trading using linear genetic programming
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Genetic Programming and Evolvable Machines: ten years of reviews
Genetic Programming and Evolvable Machines
An intraday trading model based on Artificial Immune Systems
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Neuro-genetic system for stock index prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
A dynamic threshold decision system for stock trading signal detection
Applied Soft Computing
Learning and predicting financial time series by combining natural computation and agent simulation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part II
A machine learning approach to intraday trading on foreign exchange markets
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
International Journal of Business Intelligence and Data Mining
Application of artificial neural networks to predict intraday trading signals
E-ACTIVITIES'11 Proceedings of the 10th WSEAS international conference on E-Activities
A comparative study of a financial agent based simulator across learning scenarios
ADMI'11 Proceedings of the 7th international conference on Agents and Data Mining Interaction
Learning predictive models for financial time series by using agent based simulations
Transactions on Compuational Collective Intelligence VI
Online portfolio selection: A survey
ACM Computing Surveys (CSUR)
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We consider strategies which use a collection of popular technical indicators as input and seek a profitable trading rule defined in terms of them. We consider two popular computational learning approaches, reinforcement learning and genetic programming, and compare them to a pair of simpler methods: the exact solution of an appropriate Markov decision problem, and a simple heuristic. We find that although all methods are able to generate significant in-sample and out-of-sample profits when transaction costs are zero, the genetic algorithm approach is superior for non-zero transaction costs, although none of the methods produce significant profits at realistic transaction costs. We also find that there is a substantial danger of overfitting if in-sample learning is not constrained