Stanislav Zaitsev

TECHNICAL INDICATOR – MOVING AVERAGETECHNICAL INDICATOR – MOVING AVERAGE

Market Price Movement AnalysisF

UN

DA

ME

NTA

L A

NA

LYS

IS

TECHNICAL ANALYSISanalysis of price dynamic based on the price history and volumes

CHAOS THEORYBill Williams, Malkiel

Elliot Waves TheoryRalph N. Elliot

Multifractal AnalysisBenoit B. Mandelbrot

CYCLES THEORYJ.M. Hurst

Trend-Following AnalysisIncluding Frequency Filtration approaches

Harmonic Analysis

GRAPHICAL ANALYSIS

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 AVERAGETECHNICAL INDICATOR – MOVING AVERAGE

COMPARING JMA (Jurik Research) with EMACOMPARING JMA (Jurik Research) with EMA

Jurik Research www.jurikres.com

TREND FOLLOWING EFFICIENCYTREND FOLLOWING EFFICIENCY

According to Jurik research(http://www.jurikres.com/), the best MA filter indicator should have:

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.

COMPARING DIFFERENT TYPES OF MACOMPARING DIFFERENT TYPES OF MA

Jurik Research www.jurikres.com

WAVELET TRANSFORM (CONTINUOUS)WAVELET TRANSFORM (CONTINUOUS)2

)21( 2 xex Wavelet ”Mexican Hat”and normalized wavelet family

2

2

; 211

a

bx

ba ea

bx

ax

0,, aRba

bafdxa

bxxf

abaW ;,)(

1),(

Continuous wavelet transform:

Decomposition

2; )(,

1)(

a

dbdaxbaW

Cxf ba

)(^

C, where

1

2

3 Reconstruction

ORTHOGONAL DISCRETE WAVELET TRANSFORMORTHOGONAL DISCRETE WAVELET TRANSFORM

1

2

3

Znn nxw

x)(

22

1

OUTPUT DATA

WAVELET FILTRATION ALGORYTHMWAVELET 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

PARAMETERS

INPUT DATA

“WAVELET FILTRATION STUDIO” TOOL“WAVELET FILTRATION STUDIO” TOOL

CREATE WAVELET BY ENTERING COEFFICIENTSCREATE WAVELET BY ENTERING COEFFICIENTS

CREATE FILTERCREATE FILTER

IMPORT FINANCIAL DATAIMPORT FINANCIAL DATA

APPLY FILTER TO TIME SERIESAPPLY FILTER TO TIME SERIES

CLASSES HIERARCHY AND STORAGECLASSES HIERARCHY AND STORAGE

OPEN SOURCE PROJECTOPEN 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

TO IMPLEMENT IN FUTURE…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)