1. Stanislav Zaitsev

3. TECHNICAL INDICATOR – MOVING AVERAGE Market Price Movement Analysis FUNDAMENTAL ANALYSIS TECHNICAL ANALYSIS analysis of price dynamic based on the price history and volumes CHAOS THEORY Bill Williams, Malkiel Elliot Waves Theory Ralph N. Elliot Multifractal Analysis Benoit B. Mandelbrot CYCLES THEORY J.M. Hurst Trend-Following Analysis Including Frequency Filtration approaches Harmonic Analysis GRAPHICAL ANALYSIS

4. SMA = SUM (CLOSE (i), N) / N EMA = (CLOSE (i) * P) + (EMA (i - 1) * (100 - P)) SMMA (i) = (SUM1 - SMMA (i - 1) + CLOSE (i)) / N LWMA = SUM (CLOSE (i) * i, N) / SUM (i, N) Simple Moving Average (SMA) Exponential Moving Average (EMA) Smoothed Moving Average (SMMA) Linear Weighted Moving Average (LWMA) TECHNICAL INDICATOR – MOVING AVERAGE

5. COMPARING JMA (Jurik Research) with EMA Jurik Research www.jurikres.comwww.jurikres.com

6. TREND FOLLOWING EFFICIENCY According to Jurik research (http://www.jurikres.com/), the best MA filter indicator should have:http://www.jurikres.com/ 1) Minimal distance between price line and filter line. This will impact the speed for decision making. 2) Minimal gap between price and filter lines when uptrend is being changed to downtrend. If not, the prediction of the price will not be precise 3) Minimal distance when there is uptrend. Otherwise it will take a time for convergence. 4) Maximal smoothness. Otherwise, there will be too many false signals generated.

7. COMPARING DIFFERENT TYPES OF MA Jurik Research www.jurikres.comwww.jurikres.com

8. WAVELET TRANSFORM (CONTINUOUS) Wavelet ”Mexican Hat” and normalized wavelet family Continuous wavelet transform: Decomposition, where 1 2 3 Reconstruction

9. ORTHOGONAL DISCRETE WAVELET TRANSFORM 1 2 3

10. OUTPUT DATA WAVELET FILTRATION ALGORYTHM LOADING TIME SERIES HANDLE COEFFICIENTS REMOVING DETALIZATION MAKE DETALIZATION COEFFICIENTS LOWER OR EQUAL TO 0 RECONSTRUCT THE TIME SERIES BY REVERSE WAVELET TRANSFORM USING MODIFIED COEFFICIENTS Choose Transform Type Choose Wavelet CHOOSE COEFFICIENTS HANDLING ALGORYTHM PARAME TERS INPUT DATA

11. “WAVELET FILTRATION STUDIO” TOOL

12. CREATE WAVELET BY ENTERING COEFFICIENTS

13. CREATE FILTER

14. IMPORT FINANCIAL DATA

15. APPLY FILTER TO TIME SERIES

16. CLASSES HIERARCHY AND STORAGE

17. OPEN SOURCE PROJECT http://code.google.com/p/wavelet-filtration-studio/ Wavelet Filtration Studio is available for free on Google Code with all sources as a open source project

18. TO IMPLEMENT IN FUTURE… DIFFERENT WAVELET TRANSFORMS CONTINUOUS DISCRETE REDUNDANT W. T. (FRAMES) MULTIRESOLUTIONAL ANALYSIS (MRA) THIS IS DONE NON-STATIONARY WAVELET TRANSFORM BIORTAGONAL WAVELET TRANSFORM COMPARISION OF THE DIFFERENT FILTERS BY THE KNOWN 4 CRITERIA Make Wavelet Filtration Studio to support any input data (1d, 2d etc), not only financial Implement support for 2D (and possibly nD) transformations and include all types of prices Open/Close/Hi/Low to allow analyzing financial data by 2 dimmentional wavelet transforms (including support for directional wavelets)