TAILIEUCHUNG - A Stock Trading System using Genetic Approach and Object-Oriented Database Technology

There are two kinds of fence you can use, one being a point location fence as in photo 5, and the other being a long fence as in photos 6 and 7. The point location fence contacts the stock being cut at only one point along its length- a vertical line perpendicular to the length of the stock and parallel to the blade. The advantage to this kind of fence is that it allows you to swivel the stock back and forth as it is fed into the blade in order to keep the direction of the cut going properly | A Stock Trading System using Genetic Approach and Object-Oriented Database Technology Jerzy Korczak Pascal Kustner Université Louis Pasteur ERTI Illkirch France jjk kustner @ Abstract In this paper a multi-access database system for stock trading using AI-based methods is presented. Financial time series as well as stock trading experts are stored in a relational database. These experts are used to generate trading decisions. A stock trading expert is considered as a composition of technical indicators and financial decision rules that can be used to predict financial time series. Before storing experts in the database their trading rules are discovered by a genetic algorithm. The goal of the evolution is to find out the most relevant and efficient set of decision rules. Users can download information from the database and carry out trading sim ulations and take expert trading advice. The proposed methodology is validated on real-life stock data. A few snapshots of the user interface illustrate the approach and results obtained. Based on the results of experimentation it seems that the genetic algorithms are well adapted to solve these types of problems and allow interesting solutions to be discovered. Key words intelligent trading systems financial databases genetic algorithms. 1 Introduction Investors and financial experts have always focused a great deal of attention on the problem of financial price forecasting. Over the years financial experts have developed hundreds of technical and fundamental indicators to predict financial time series. Today financial markets are considered as complex time-variant and nonlinear dynamic systems. The problem of stock trading is particularly complex. The basic question is how to select from the large number of features and indicators available those that are most significant and relevant. Although many propositions of stock trading models exist 1 4 7 10 15 until now no efficient solution has been found. .

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