Anyone else think Elliot Wave is a bunch of hooey?

" Elliot Wave is a Fibonacci system all dressed up."

Ho, ho, ho. Of course it is ! The point being it was Elliott that introduced it.

Elliott Wave ISN'T a trading system.

EW is price movement analysis — a 'Law of Nature' Elliott was unable to explain except by describing its waving manifestations measured with the fibonacci ratio.


[waving smiley ; ) ]
 
Anybody tried MT Predictor (www.mtpredictor.com)?

This is an EW black box, which is claimed to generate clear trade indicators, apparently based on an algorithm that makes a clear choice which wave it is in, and ignores all other waves that might confuse the picture (or vitiate the prediction, if you are being cynical).
 
Quote from MackieMesser:

yes.

But then most TA is hooey. But it is a big business, so don't expect the truth to ever come out.

Look at how many people take astrology seriously?



Or believe in a literal interpretation of ancient religious texts, etc etc.

m
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Find some parts of elliot wave useful;
dont plan on throwing out moving averages just because some dont like them in sideways trends[ranges.]

Some may be suprised at the % of those experienced businessmen/women who have proved ;

''In all labor ther is profit;''
''Literal interpretation of ancient Proverb text.''
Solomon, literal trader king

:cool:
 
Poor steve46 as always he is retarded ! Last time he pretended that according to "his" theory everything tends towards normal law which is completely false as I answered that only a sum of distributions with FINITE MEAN AND VARIANCE do so and as for STOCK MARKET many researchers doubt that's why there are so many modelisations using Levy's distribution which has no variance and no mean.

And now he pretends that garch is a good model laughable even ARFIMA which is an improvment is not usable in practice and I have already pinpointed this here:
http://www.elitetrader.com/vb/showthread.php?s=&postid=436861&highlight=garch#post436861
Re: Econometrics and practice

The very basic reason why stock market time series are reputed to be one of the most difficult arena of forecast is because these classical time series techniques don't work, mathematically these techniques are based on autocorrelation of errors since these autocorrelations are very low in stock market time series they are not worth at least used in traditional way. Now low autocorrelation is not equivalent to independancy, it has been showned for a long time since Mandelbrott that the Market exhibits "long term memory effect" so that the latest kind of stochastic model taking into account that effect is ARFIMA's model. But the performance still is poor. All in all I say it is an error to use stochastic models to do market's forecast because only a deterministic model can do it (ie mine of course ), the problem is to find it and the reason that researchers didn't find it is because they try to extract knowledge from pure datas which is an idiocy from paradigm point of view because the model's knowledge is transcendant to the datas that is to say you cannot deduce it from the datas alone but only if you have the idea of how market really works or you will play with datas and stochastic models much like a monkey see:
http://www.elitetrader.com/vb/showt...&threadid=28614

ANNs (Neural Net): A Little Knowledge Can Be A Dangerous Thing
http://www.secondmoment.org/articles/ann.php

ANNs: A Little Knowledge Can Be A Dangerous Thing
Posted by Dr. Halbert White



Quote from DT-waw:

I've posted similar questions on Wilmott forum, but I didn't get what I wanted. Maybe ET'ers will know more.

Do you know of any trading system (excl. arbitrage) and it's historical hypothetical or real performance on most popular markets (futures, stocks, forex; in 1-30 min. intervals) based on GARCH, ARIMA, Bayesian analysis, Kalman filtering? I wonder whether these tools can outperform classic technical analysis tools. There's a lot of academic research on these models, however their robustness in the real trading isn't described.

I found some research by Olsen http://www.olsen.ch/research/workin...319_real-r1.pdf but it only deals with FX 1990-1996 and trading costs aren't specified. Performance is poor when compared to equity futures systems. Somewhere on the internet I found that Mr. Pierre Lequeux made performance analysis on Dax and Nikkei, but I can't find these papers via google, many pdf's are written in french.

When academic people apply econometric methods into the financial data series, their conclusions are always related to volatility or many statistical properties. I would like to see simple figures like P&L, drawdowns, Sharpe ratio, profit factor...

__________________


Quote from steve46:

I notice that Harry is fond of copying large articles off the internet and posting them here. Often they have as the main subject and text ideas that require either a specialized scientific or math background. This reminds me of students of mine who after reading several chapters of the class text, decide to portray themselves authorities on the subject (only to that select group that has yet to take the class of course).
The truth of the matter is that markets exhibit both random and non-random behavior. There is no controversy in this statement. Simply look at what happens to the market after reports come out. That is but one example of non-random behavior. Inquiry into the idea of cyclical behavior has a long history and a rigorous math that has been developed. However that mathematical structure has yet to provide a VIABLE method of describing cyclical behavior that can be used to predict price. Theses methods range from simple Fourier analysis to kernel regression to GARCH (look them up Harry, I am always glad to read more full page posts on subjects you don't understand). As regards, fibonacci, the reading I have done reveals that there is insufficient evidence to suggest that this offers an quantifiable edge to traders. This is not surprising. In fact, the bottom line is that most indicators we see in charting packages do not offer a statistical edge. This is not just my opinion, but the opinion of learned, experienced traders and researchers. One good example of a thoughtful view on this subject can be found by reading Ben Warwick's books on "event trading". Other viewpoints are published periodically in academic research and by the NY Bank staff. Sorry about the long post. I realize this is Harry's trademark. I could not resist. I look forward to all thoughtful comments (including yours Harry). :D Steve46

P.S. By the way, I do notice examples of cyclical behavior all the time. If you start with a 5 or 10 min chart and look at how price moves, then move to a 1 or 3 min chart of the same time period, often you will be able to identify "pops" of price movement. also in the popular literature there are references to cyclical entry set-up's to this day ("3 little indians" from Linda Raschke's books). I am not saying that it (cyclical activity) doesn't exist). Only that currently, there is no systematic, accurate way to use it. Thanks Steve46
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Btw I have made a basic faq on normal law notably for those who makes confusion between MATHEMATICALITY and PHYSICALITY: PHYSICALITY (PHYSICS / REALITY) HAS NOT TO CONFORM TO MATHEMATICALITY when the PREMISCES OF MATHEMATICAL THEOREM ARE NOT FULFILLED !!!


http://www.econometric-wave.com/faqs/probability/home.html.html

Is Normal Law always true ?
Normal Law is qualified as "Natural Law" because of the "Central Limit Theorem" which says that the sum of n random variables belonging to any random law as long as it has a mean and a variance, will tend towards the Normal Law as n grows. Physically it is a good approximation when among multiple causes none is preponderant so that the deviation from the main cause will be symetrical. When some causes become preponderant so as to perturbate the usual (normal) behavior of the phenomena, it is not astonishing that normal law can be inadequate. This depends on the degree of approximation needed. When a multiple-causes phenomena exhibits non-normal behavior, this is indicative of abnormal forces: anomalies detection are often the main decision criteria in some applications.

Quote from harrytrader:

Poor steve46 as always he is retarded ! Last time he pretended that according to "his" theory everything tends towards normal law which is completely false as I answered that only a sum of distributions with FINITE MEAN AND VARIANCE do so and as for STOCK MARKET many researchers doubt that's why there are so many modelisation using Levy's distribution which has no variance and no mean.

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A true scientific model goes from PHYSICALITY to MATHEMATICALITY not the INVERSE.

Quote from harrytrader:

Btw I have made a basic on normal law notably for those who makes confusion between MATHEMATICALITY and PHYSICALITY: PHYSICALITY (PHYSICS / REALITY) HAS NOT TO CONFORM TO MATHEMATICALITY when the PREMISCES OF MATHEMATICAL THEOREMS ARE NOT FULFILLED !!!


http://www.econometric-wave.com/faqs/probability/home.html.html

Is Normal Law always true ?
Normal Law is qualified as "Natural Law" because of the "Central Limit Theorem" which says that the sum of n random variables belonging to any random law as long as it has a mean and a variance, will tend towards the Normal Law as n grows. Physically it is a good approximation when among multiple causes none is preponderant so that the deviation from the main cause will be symetrical. When some causes become preponderant so as to perturbate the usual (normal) behavior of the phenomena, it is not astonishing that normal law can be inadequate. This depends on the degree of approximation needed. When a multiple-causes phenomena exhibits non-normal behavior, this is indicative of abnormal forces: anomalies detection are often the main decision criteria in some applications.
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Quote from ArchAngel:

EW is the most perfect system ever developed - for hindsight analysis :)
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This is probably the only thing that needs to be said about EW. Anyone who trys to honestly forward trade these "perfect waves" quickly understands how it never works out due to all the ever changing rule sets. These "Million rules for every rule" explain away repeated failures of all the other previous predictions to again try to look perfect.

A giant frickin waste of time.
 
Learning without understanding: this is well illustrated by Feynman (excerpt from his autobiography "Surely You're Joking, Mr. Feynman!"):

"I often liked to play tricks on people when I was at MlT. One time,
in mechanical drawing class, some joker picked up a French curve (a
piece of plastic for drawing smooth curves - a curly, funny-looking
thing) and said, "I wonder if the curves on this thing have some
special formula?"

I thought for a moment and said, "Sure they do. The curves are very
special curves. Lemme show ya," and I picked up my French curve and
began to turn it slowly. "The French curve is made so that at the
lowest point on each curve, no matter how you turn it, the tangent is
horizontal."

All the guys in the class were holding their French curve up at
different angles, holding their pencil up to it at the lowest point
and laying it along, and discovering that, sure enough, the tangent is
horizontal. They were all excited by this "discovery" even though they
had already gone through a certain amount of calculus and had already
"learned" that the derivative (tangent) of the minimum (lowest point)
of any curve is zero (horizontal). They didn't put two and two
together. They didn't even know what they "knew."

I don't know what's the matter with people: they don't learn by
understanding, they learn by some other way-by rote, or something.
Their knowledge is so fragile."



Quote from harrytrader:

Btw I have made a basic faq on normal law notably for those who makes confusion between MATHEMATICALITY and PHYSICALITY: PHYSICALITY (PHYSICS / REALITY) HAS NOT TO CONFORM TO MATHEMATICALITY when the PREMISCES OF MATHEMATICAL THEOREM ARE NOT FULFILLED !!!


http://www.econometric-wave.com/faqs/probability/home.html.html

Is Normal Law always true ?
Normal Law is qualified as "Natural Law" because of the "Central Limit Theorem" which says that the sum of n random variables belonging to any random law as long as it has a mean and a variance, will tend towards the Normal Law as n grows. Physically it is a good approximation when among multiple causes none is preponderant so that the deviation from the main cause will be symetrical. When some causes become preponderant so as to perturbate the usual (normal) behavior of the phenomena, it is not astonishing that normal law can be inadequate. This depends on the degree of approximation needed. When a multiple-causes phenomena exhibits non-normal behavior, this is indicative of abnormal forces: anomalies detection are often the main decision criteria in some applications.
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