The Applications Of Genetic Algorithms In
Stock Market Data Mining Optimisation
Author(s)
L. Lin, L. Cao, J. Wang & C. Zhang
Abstract
In the stock market, a technical trading rule is a popular tool for analysts and
users to carry out their research and decide to buy or sell their shares. The key
issue for the success of a trading rule is the selection of values for all parameters
and their combinations. However, the range of parameters can vary in a large
domain, so it is difficult for users to find the best parameter combination. In this
paper, we present the Genetic Algorithm (GA) to overcome the problem in two
steps: first, setting a sub-domain of the parameters with GA; second, finding a
near optimal value in the sub domain with GA in a very reasonable time.
Keywords: technical trading rule; genetic algorithm; sub-domain; parameter
combination.
1 Introduction
In stock market and other finance fields, Genetic Algorithm has been applied in
many problems [1]. There have been a number of attempts to use GA for
acquiring technical trading rules, both for Foreign Exchange Trading [2][3] and
for S&P500 market. One application is how to find the best combination values
of each parameter. We know that in a trading rule there are many parameters,
when we try to find the most profit, we must test the parameter combination one
by one, which is called greedy algorithm which costs a lot of running time and
memory.
Through analyzing the stock market, we know there are some combinations
of the parameters, which can produce a near-max profit and give some
Keywords
technical trading rule; genetic algorithm; sub-domain; parameter combination.
Related Book
Other papers in this volume
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