یک روش SAX-GA برای تحول استراتژی های سرمایه گذاری در بازارهای مالی بر اساس تکنیک های کشف زمینه

This paper presents a new computational finance approach, combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA). The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. The achieved results show that the proposed approach outperforms both B&H and other state-of-the-art solutions.

The domain of computational finance has received an increasing attention by people from both finance and intelligent computation domains. The main driving force in the field of computational finance, with application to financial markets, is to define highly profitable and less risky trading strategies. In order to accomplish this main objective, the defined strategies must process large amounts of data which include financial markets time series, fundamental analysis data, technical analysis data, etc. and produce appropriate buy and sell signals for the selected financial market securities. What may appear, at a first glance, as an easy problem is, in fact, a huge and highly complex optimization problem, which cannot be solved analytically. Therefore, this makes the soft computing and in general the intelligent computation domains specially appropriate for addressing the problem. Recently, several works like (Krause, 2011, Parque et al., 2010, Parracho et al., 2011 and Pinto et al., 2011), have been published in the field of computational finance where soft computing methods are used for stock market forecasting, however, due to the complexity of the problem and the lack of generalized solutions this is undoubtedly an open research domain. The use of chart patterns is widely spread among traders as an additional tool for decision making, however, the problem in this case is to say how close enough should the market match a specified chart pattern to make a buy or sell decision. In this paper a new approach combining a Symbolic Aggregate approXimation (SAX) technique together with an optimization kernel based on genetic algorithms (GA) is presented. The SAX representation is used to describe the financial time series, so that, relevant patterns can be efficiently identified. The evolutionary optimization kernel is here used to identify the most relevant patterns and generate investment rules. The proposed approach was tested using real data from S&P500. Finally, the achieved results outperform the existing state-of-the-art solutions. This paper is organized as follows; in Section 2 the related work is discussed. Section 3 describes the method of dimensional reduction of the time series used in the paper, SAX. Section 4 the proposed approach that puts together the GA and SAX is explained. Section 5 describes the experiments and discusses results. Section 6 draws the conclusions.

The proposed new computational finance approach, combining a SAX technique together with an evolutionary optimization kernel shows a great potential on investment markets. The selected SAX representation allows a huge dimensional reduction of the time series and maintaining the main characteristics of the financial data. The adopted chromosome structure, inside the evolutionary optimization kernel, is extremely versatile and allows finding several different patterns with different dimensions. Moreover, GA-based evolutionary kernel proves its adaptability to find good solutions to the problem in hand. From the presented case studies it was identified that the investment method to apply is the one with the multi-chromosome structure, which was in part expected since this approach allows applying several investment strategies and profiting from bear and bull market conditions simultaneously. Finally, the tests performed with real data from S&P500 clearly outperforms both B&H and other state-of-the-art solutions.