Sieci neuronowe forex trading
Grimm Die Professoren des Fachbereichs: Prof. Burkhardt, Prof. Diller, Prof. Ebert, Prof. Furbach, Prof. Grimm, Prof. Hampe, Prof. Harbusch, jProf. Kilian, Prof. Lautenbach, Prof. Oppermann, Prof. Paulus, Prof. Priese, Prof. Rosendahl, Prof. Schubert, Prof. Staab, Prof. Steigner, Prof. Sure, Prof. Troitzsch, Prof. Walsh, Prof. Wimmer, Prof. Kryuchin, Alexander A. Arzamastsev, Klaus G. Fachbereich Informatik Nr. Troitzsch Abstract This paper describes the analysis of a neural network used to predict currency exchange rates in comparison to technical analysis.
The neural network structures used are a multilayer perceptron and a Volterra network. There is a group of parameters for such a system which characterizes the system condition and allows to calculate system values at every point of time from initial values by some special convention see eq.
The state of the currency exchange rates is characterized by the time series 2. The latter analysis is not limited by the character of the input information, which may be a time series or information about the behavior of other market factors. To achieve this aim, several tasks have to be solved. There are two kinds of time series forecasting methods. The most popular tool of technical analysis is the moving average MA which calculates the mean value of prices in a given time interval.
So this tool is a method of smoothing price factors which are cumulated over several periods. The MA can be calculated for each serial data array, such as opening and closing prices, maximum and minimum quotations, the trade size or values of other indicators for example the MA itself. Another tool used is a stochastic oscillator which builds two series. Often a third series is added which is the MA of the second.
The crossing of smoothed and non- smoothed means changes the direction of the moment of the quotations. Dutta S. Edwards T. Fahlman S. Freeman J. Geman S. Goldberg D. Grabowski M. Grudnitski G. Hagan M. Hampel F. Harrison D. Jr, Rubinfeld D. Hassibi B. Touretzky ed. Hassoun M. Haykin S. Hebb D. Hecht-Nielsen R. Hertz J. Hoptroff R. Hornik K. Jajuga K. Instrumenty finansowe. Ryzyko finansowe. Kiziniewicz A. Kolmogorov A.
Korbicz J. Krose B. Kunka W. Kurkova V. Lin K. Lorie J. Irwin, Homewood, IL, cyt. Louis A. Lula P. Teoria i zastosowania, z. Masters T. McCulloch W. Mehta M. Rogowski, A. Szymanowski, PWN, Warszawa. Pociecha, B. Podolec, A. Milo W. Minsky M.
Moller M. Leshno, V. Ya Lin, A. Pinkus, S. Schocken, "Neural Networks", Vol. Musielak J. Neural Networks in Finance and Investing [], R. Neural Networks in the Capital Markets [], A. Neurocomputing: Foundations of Research [], J. Anderson, E. Odom M. Turban eds , Probus Publ. Musavi, W. Ahmed, K. Chan, K. Faris, D. Hummels, "Neural Networks", Vol. Osowski S. Park J. Parker D. Technology, Cambridge, MA. Peters E. Poddig T. Polikar R. Raghupathi W.
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