Electronic Trading Market Forecast Algorithm

Instead of the traditional trade mode, Electronic trade is applied to the stock, the future and many other trade fields. Mass market quotation records and the real time market quotation updating that can be used to analyze the market come force with this mode. As a result, useful information can be gotten by analyzing the records.KDD(Knowledge Discovery in Database) is a new emerging area in the research of artificial intelligence and databases .This technology is used in finance, medical treatment, retail, manufacture, engineering and science. It has already been used in market analysis.The thesis discusses how to analyze the market quotation records by solving four problems in this aspect. The methods brought forward are based on the conceptions and technologies in KDD and mathematics. The algorithms and data structures are expatiated in detail.The main work of the thesis:Firstly, an efficient algorithm and data structure is brought forward to select the optimum Bayesian network model which best represent the dependent relationships of the variables samples.Secondly, to realize the real time market analysis, a NB (naive Bayesian) classification engine based on a dynamic three-dimensional link table is realized and tested.Thirdly, the algorithms to construct the records aggregations which can be processed by Apriori algorithm are designed and tested by stock market quotation records.Fourthly, the market quotation records are transformed from time field to frequency field by FFT algorithm, the comparability is measured in frequency field.To meet the need of the He Nan province technology property trading system, the characteristic of practice use is concerned in choosing and designing the algorithms.