An introduction to genetic algorithms
An introduction to genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Robust Artificial Neural Networks for Pricing of European Options
Computational Economics
Improving technical trading systems by using a new MATLAB-based genetic algorithm procedure
Mathematical and Computer Modelling: An International Journal
A Genetic Programming Approach for EUR/USD Exchange Rate Forecasting and Trading
Computational Economics
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Investors use a number of technical trading tools to help them in their decision-making. This article aims to enhance this decision making process through the application of Genetic Algorithms (GAs) and Artificial Neural Networks (ANNs). The signals generated by technical trading tools are optimised for maximum profit through the use of GAs. The optimised signals are fed into a variety of fully connected feed forward ANNs, which combine these signals and output a single set of signals of whether to buy, hold or sell in the current market state. The different solutions produced are compared and contrasted, to determine the best ANN architecture for this type of signal amalgamation problem, and the optimal population size and mutation function for the GA. The result is an autonomous trading system with intelligence. This system, as described in this article, has proven to be profitable based on data presented to it--which spans ten currencies over a five year period. The profit margins are statistically significant when compared to un-optimised trading rules as suggested by literature. Further, the margins are statistically significantly more profitable than other no-risk investment strategies.