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Howell - PuetzUWS model for extended SP500
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Puetz Universal Wave Series (UWS) model for a back-extended SP500
"... History never repeats itself, but it rhymes. ..." (which author(s) down through history?)
Table of Contents
NOTE : The model here is DEFINITELY NOT suitable for application to [trade, invest]ing! It's far too [early, incomplete, immature, erroneous]! See Stephen Puetz for the investment side ot the UWS.
Model results
"SP500 & DJIA indexes back to 1872"
Linear semi-log segments [1872-1926, 1926-2020]
Fibonnaci levels versus log-levels
Larger Fibonnaci's at greater timescales
How do I get negative numbers from a log transformation of the data?
"Key [results, comments]"
"PuetzUWS 2011 [normal, double]-wave series combined"
More and more like Fourier series analysis, but potentially with greater predictive capability? (see section below)
"Conventional semi-log plot of the PuetzUWS model"
"Conventional semi-log plot of the PuetzUWS model"
"SP500 time-segment overlays"
My first semi-log detrended plot of the SP500 1872-2020 is in the webPage title?. On that webPage, I overlay SP500 series from
Puetz's UWS as a highly-constrained Fourier wave series analysis
???
???
???
Was 2007-2009 the modern equivalent to the 1929 crash?
Was 2007-2009 the moderen equivalent to the 1929 crash, less the socialist abuse of the free market?
??MacKay?? reference
??MacKay??'s commments are certainly unconventional, but merit closer attention.
Key points :
How can the S&P 500 stock market index be correlated with 'dead' system?
What's [missing from, wrong with] the current model?
- The data set :
- Currently uses historical data of the [SP500,
- Needs to include the London Stock Exchange (LSE) and Bourse de Paris?
- Should perhaps be the SP International index back-extended?
- Pre-stock exchange proxies - agrultural commodities going back to Babylon
- Update the UWS dataset -
- currently uses the more complete parameters from Puetz & Borchardt 2011, for both the "normal" and "double" UWS series
- A 2014 paper changes the base UWS parameters, and applies a Half-wave (HUWS) seeries, rather thanDouble-UWS (DUWS) series
-
- Why have I "eyeballed" the linear-log segments, yet I used Particle Swarm Optimization (PSO) for the parameter? I even have routines for linear regression analysis, which are standard and very easy. Laziness is the reason, but I also a fear of getting "artificially precise" at the start of the project when the basic concepts are being explored. I'm not looking for false accuracy at this point. It's much more important to simply get my hands dirty with the data and look for [challenges, questions, anomalies], and to see different approaches by eye.
- Using a hybrid [linear regression, PSO or other] optimisation approach
- Down to the hour data - Trading models
-
- Multi-fractal (Benoit Mandelbrot) and Fractional Order Calculus (Gottfried Liebniz) approaches
Inspirations
Inspired by many historians and financial market analysts, most notably but not limited to (in order of appearance on the world stage) :
Ibn Khaldun "The Muqquadimmah"
?? Tijevski?
?? Kondriatieff
?? Elliot's waves
Arnold J. Toynbee "A Study of History"
(the full 10-12 volumes, I was missing one or two, now I have only the abridged two-volume set
Ivanka Charvova - best concept I've seen of solar activity over 5-10 ky
Steven Yaskell and Willie Soon "Grand phases on the Sun" - wonderful descriptions of the Maunder grand solar minimum and many scientists and others
Harry Dent - the most successful 10-40 year [economic, financial] forecaster that I am aware of
Robert Prechter's Socionomics, the first quantitative Sociology
Peter Fischers "The Great Wave" - I forget which author (probably Stephen Puetz who used some of the data) mentioned Fischer's book, but it is a real gem, built on the meticulous work of numerous historians. I consider it to be one of the great historical works. Stephen Puetz, and especially co-authors [Glen Borchardt, Andreas Prokoph, ??],
and the system of 20+ Mayan calendars
Ray Dalio of ?BlackRock" Hedge fund "??"
??Jan2020 initial posting
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