Pleased to meet you.
Would you like to collaborate with me on the following ?
I will pay you for your work. I have studied Physics + Stats for my
B.Sc. so have an idea on the broad approach but my maths is not
The analysis needs to be done separately for the BSE Sensex Index and
the BSE 100 Index so if my theory is correct there will be two sets
of the five component frequencies below.
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The file contains the end of day values of the BSE Sensex and BSE
100 Index from 1st January 2002 tiil 1st February 2019.
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I have inserted missing dates of bank holidays manually these are
flagged as "EXTRADATE" in yellow cells in column N. When an extradate
has been interpolated for a business holiday I have simply repeated
the immediately preceeding data. e.g, for a Saturday and Sunday I
repeat the immediately preceeding Friday data.
Can you use fourier analysis o any other analysis to decompose the
data in component sine waves or any other periodic function ?
Ideally, I am hoping that it can be decomposed into a sine wave of the
y(t)=[login to view URL](ωt+φ)
A = Amplitude of the Wave
ω = the angular frequency, specifies how
many oscillations occur in a unit time interval in radians per second
φ, the phase,
t = variable time in days
φ can be taken as zero for your analysis.
My theory is that there should be several component sine frequencies.
1)y(weekly) corresponding to the 7 day week.
2)y(monthly) because derviative positions have to be settled on the
last thursday of each month
3) y(annual) since business follows a fixed financial year.
4) y(five year) corresponding to our election cycle.
5) any other long term trend you can find.
If you can find predictable waveforms for the above I can sum them to
extrapolate and predict the stock market index in the future for the
BSE sensex and the BSE 100 index.
Alternatively if you can derive any composite equation that will allow
me to extrapolate or predict the index that will also do.
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