Genetic Algorithms Trading Strategies
Computational Economics. August , Cite as. In this paper, I present a decision-making process that incorporates a Genetic Algorithm GA into a state dependent dynamic portfolio optimization system. A GA is a probabilistic search approach and thus can serve as a stochastic problem solving technique. A Genetic Algorithm solves the model by forward-looking and backward-induction, which incorporates both historical information and future uncertainty when estimating the asset returns.
After a brief overview of the history of the development and application of genetic algorithms and related simulation techniques, this chapter describes alternative implementations of the genetic algorithm, their strengths and weaknesses. Then follows an overview of published applications in finance, with particular focus on the papers of Bauer, Pereira, and Colin in foreign exchange trading. Many other rumored applications remain unpublished. Unable to display preview. Download preview PDF.
More about this item Keywords genetic algorithms ; algorithms and investment value. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rss:jnljms:v3i9p3. See general information about how to correct material in RePEc. For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Danish Khalil. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here.
Intra-Day Trading System Design Based on
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