TAILIEUCHUNG - A Stock Trading Algorithm Model Proposal, based on Technical Indicators Signals

The SFI market was born in discussions at the Santa Fe Institute in the late 1980’s and early 1990’s. It originated as a desire of Brian Arthur and John Holland to build a financial market with an ecology of trading strategies. Successful strategies would persist and replicate, and weak strategies would go away, creating potential niches for the entry of new strategies. The market was to be a continually coevolving soup of strategies. At the foundation of this was a desire to not preload much into the system, and to let evolution do most of the work. In its purist rendition the strategies and macro. | Informatica Economică vol. 15 no. 1 2011 183 A Stock Trading Algorithm Model Proposal based on Technical Indicators Signals Darie MOLDOVAN Mircea MOCA tefan NITCHI Business Information Systems Dept. Babeậ-Bolyai University of Cluj-Napoca @ The algorithmic stock trading has developed exponentially in the past years while the automatism of the technical analysis was the main research are for implementing the algorithms. This paper proposes a model for a trading algorithm that combines the signals from different technical indicators in order to provide more accurate trading signals. Keywords Decision Support System Trading Algorithm Technical Analysis 1 Introduction The use of technical analysis indicators in decision making for stock investments stays a controversial subject being appreciated by some investors but rejected by others 4 . While professionals and researchers from the academic world developed new methods and indicators live or simulated tests are needed to validate them 5 . The price prediction is a very complex issue and selecting the right technical indicators for the analysis of a particular stock is one of the first preoccupations of the investors that use the technical analysis. One difficulty is the tuning of the parameters of these indicators in a way that makes their signals correct in a percentage as high as possible 1 . While the behavior of the stocks is different from one to another and changes during time the choice of parameters values becomes a difficult task without the help of an advanced computational method. The data mining methods are considered to be a smart choice for selecting the right technical indicators allowing tests on very large datasets an essential condition regarding the large volume of financial data available and many combinations of parameters values combining daily weekly or monthly prices for tests 2 5 . Our objective is to propose a methodology that combines .

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