wavelet filtration for financial data analysis
DESCRIPTION
WAVELET FILTRATION FOR FINANCIAL DATA ANALYSIS. Stanislav Zaitsev. 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 - PowerPoint PPT PresentationTRANSCRIPT
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)