Technology

Financial investment decision making in the stock market is extremely difficult due to inherent complexity of the domain. Many factors could affect the future prices. For example, the future price of a share may be influenced by fundamental factors, such as "price-earning ratio", "inflation rate", as well as technical factors such as "n-days moving average", "n-days trading range breakout", etc. Other influential factors may include market indices, who said what in public, etc. Prediction is made more difficult by the fact that various factors often interact with each other. To help users evaluate the impact of different factors and explore the interactions in relation to market movement, Advanced Trading Systems have developed a Genetic Algorithms (GA) and Artificial Neural Network (ANN) based system.

What are Genetic Algorithms?

Introduction

Genetic Algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.
First pioneered by John Holland in the 60s, Genetic Algorithms has been widely studied, experimented and applied in many fields in engineering worlds. Not only does GAs provide an alternative method to solving problems, it consistently outperforms other traditional methods in most of the problems link. Many of the real world problems involved finding optimal parameters, which might prove difficult for traditional methods but ideal for GAs. However, because of its outstanding performance in optimization, GAs have been wrongly regarded as a function optimizer. In fact, there are many ways to view genetic algorithms. Perhaps most users come to GAs looking for a problem solver, but this is a restrictive view.

Brief Overview

GAs were introduced as a computational analogy of adaptive systems. They are modeled loosely on the principles of the evolution via natural selection, employing a population of individuals that undergo selection in the presence of variation-inducing operators such as mutation and recombination (crossover). A fitness function is used to evaluate individuals, and reproductive success varies with fitness.

The advantage of the GA approach is the ease with which it can handle arbitrary kinds of constraints and objectives; all such things can be handled as weighted components of the fitness function, making it easy to adapt the GA scheduler to the particular requirements of a very wide range of possible overall objectives.
GAs have been used for problem-solving and for modeling. GAs are applied to many scientific, engineering problems, in business and entertainment.

Advanced Trading Systems and Genetic Algorithms

We have focused our research on three different but complementary axis. First our GA intelligent systems aimed at finding market timing strategies. Second, we used our GA systems to identify trading rules in the stock market. Finally, we applied our systems to the task of predicting the futures performances of individual security or derivative.
 

Other Resources

• Genetic Algorithm on Wikipedia
• Introduction to genetic algorithms with Java applets
• The genetic algorithm archives
• Genetic Algorithms and Evolutionary Computation
• How to choose an online stock broker

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