A good mathematician should be good in forex. - page 3

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graziani:

In a way, point is that you have to constantly adapt the system for changes in distribution.

But are you not interested in which these two rules that are valid for PA would be? :)

One is obvious, and one is conducted out of the random walk model.

As they might be more for which i don't know or at least i am not aware of, i would like everybody to give their educated guesses, and i will confirm if you are right.

So ... let the games begin! :)

 

There are so many rules I've read about price-action that I wouldn't know which two are the most important.

Plus, I do-not want to make a guess which sag-way this topic off-course.

 
graziani:


It seems that you failed at the very beginning, by not understanding the consequences of the fact that PA is non-stationary stochastic process, which can be concluded out of claims you made.

I used that methods not because I claim something, I research them because someone with mathematical proof claims those are works on Financial data. so you think, Engle is failed, Russel is failed many others, they didnt and doesnt understand what they are doing. 

and sorry, my bad I failed, I gues I couldnt learn the famous simple transform of a non-stationary process into a stationary one, called "difference". or an advenced stuff which can be used with non-stationary processes, called wavelet, sorry. 

Or did you mention  PA's nonstationary nature doesnt allow that kind of research, So in addition, Mandelbrot is failed, because he research and claim this kind of stuff on PA during his life, a math genius couldnt see the consequences what you see, he is not a real mathematician (really? that arrogant?)

the matter here is not stationarity it is the distribution and randomness: what you know about advanced mathematics is useless eventually, because of a mathematically undefined distribution and absolute randomness. 

I didnt claim this kind of stuff could be used to analyse the financial data (the math stuff i mentioned before), in adverse I just research the available usages, and found this stuff and gone deep, an I claim : intermadiate statistics is enough to explain little things on financial data, the rest are a lie. here no one really needs know advanced mathematics.

Like you said:

Mathematics serves for analytical description of processes and relations.

If you can describe in a formal language what you see, you have a large advantage over someone who doesn't know to do that.

And I claim it cant be described with the available methods of that language. But If you know that language well, you can say lies. Scientific issues consists many fatal points (mainly axioms), some of that points never analised for validity in many researches.

many A-class published article about financial matematics doesnt confront axioms but they claim their method explains something. and they are still published because everyone dreams about that redicilous staff. 

look for mandelbrot hypothesis, if you really can understand, it leads you what I claim.

and look for aticle Engle and Russel 2004 - "Analysis of High Frequency Financial Data".

and compare their ideas with others, the greatest exapmles which explains this dream. 

 
graziani:

NSSP means that the PA (Price Action) is completely random and that every moment in the market is unique.

So there is no transformation function or analytical expression that can predict the value or give you the edge in any way, based on PA, i.e. the stream of values you get from broker.

PA is not random,  there is cause and effect even if it is incredibly difficult to understand . . . and even harder to predict.
 
Ubzen:

This is a very interesting topic. I've searched for articles about Engle and would be spending some time reading his conclusions.

If you would, could you please expand upon what is the Lie vs the Truth about this dream.

I tell you shortly,

  • Mandelbrot (if I do remember truely the date is 1960) claims the price returns dont conform the central limit theorem, he told -by his research- this is a known issue since early 1900s but financial mathematicians ignore the findings because there is no other way forward. and they still use the normality axiom. and develop their models and methods accordin to normality axiom.  the real distributions of financial data (which is stationarized) is not distrubuted normally nor aproximate to normal. they are called heavy-tailed distrubutions.
  • why  is a distribution is so important: because there is no standard way to proof financial mathematical models on real conditions unless you have a standard mathematically defined distrubution
  • Almost all models on the financial data are based continious methods even the discrete models (discrete signal processing methods were originally created for analysing of sampled continious signals). Engle in 1990s told the truth and said "actually all economic data are discrete by it's nature, irregularly sapaced, and isnt distrubuted normal. so it cant be analised the way we did before." He developed a new model, useless in real market analysis. but beter than nothing. a few followed him.
  • normally all of us used resampled financial data, resampled to make it regularly spaced in time,  but with every resampling the data explains less of its nature. (the daily data explains almost nothing about the original process.) But if we use it irregularly, we should solve the random time problem. we cant for now, we can only analyse it and Engle did that
  •  the other issue is the linear randomness, probably because of its nature, prices mostly produce completely random signals (linearly speaking). I can enter the linear randomness but only if it isnt much for the topic? (a bit mathematics comes)

Well, there are so many fatal points in a scientific research (mainly axioms) and most researches failed at basic axioms about normality, continuity, randomness. but as you can see even in the A-class publishers they can ignore them. statistics is the art of lying. and those financial models based on advanced mathematical methods are tested with the statistics in real life, not considering the validity of some axioms. few are true most are not.

everyone escapes the truth because they like magical mathematics, it is the dream, but eventually there is no magic in real conditions.

PS: personally I love the magics of mathematics but I couldnt lie that much, dream is over for me, it is boring, no fun left 

 

forex is more of metal alertness, psycholigy and emotion than being brilliant or intelligent, i will say forex is more of NATURAL WISDOM.

it has nothing to do with being skilfully tallented other than acquring the accurate knowledge of it and strictly applying it with practical wisdom.

 
i dont get it all the people who posted seems like on the false side but why so many true???
 
erdogenes:

I tell you shortly,

  • Mandelbrot (if I do remember truely the date is 1960) claims the price returns dont conform the central limit theorem, he told -by his research- this is a known issue since early 1900s but financial mathematicians ignore the findings because there is no other way forward. and they still use the normality axiom. and develop their models and methods accordin to normality axiom.  the real distributions of financial data (which is stationarized) is not distrubuted normally nor aproximate to normal. they are called heavy-tailed distrubutions.
  • why  is a distribution is so important: because there is no standard way to proof financial mathematical models on real conditions unless you have a standard mathematically defined distrubution
  • Almost all models on the financial data are based continious methods even the discrete models (discrete signal processing methods were originally created for analysing of sampled continious signals). Engle in 1990s told the truth and said "actually all economic data are discrete by it's nature, irregularly sapaced, and isnt distrubuted normal. so it cant be analised the way we did before." He developed a new model, useless in real market analysis. but beter than nothing. a few followed him.
  • normally all of us used resampled financial data, resampled to make it regularly spaced in time,  but with every resampling the data explains less of its nature. (the daily data explains almost nothing about the original process.) But if we use it irregularly, we should solve the random time problem. we cant for now, we can only analyse it and Engle did that
  •  the other issue is the linear randomness, probably because of its nature, prices mostly produce completely random signals (linearly speaking). I can enter the linear randomness but only if it isnt much for the topic? (a bit mathematics comes)

Well, there are so many fatal points in a scientific research (mainly axioms) and most researches failed at basic axioms about normality, continuity, randomness. but as you can see even in the A-class publishers they can ignore them. statistics is the art of lying. and those financial models based on advanced mathematical methods are tested with the statistics in real life, not considering the validity of some axioms. few are true most are not.

everyone escapes the truth because they like magical mathematics, it is the dream, but eventually there is no magic in real conditions.

PS: personally I love the magics of mathematics but I couldnt lie that much, dream is over for me, it is boring, no fun left 

That's what I call a good Finance class. 

I only disagree when you say that Statistics is the art of lying... actually we have good statistical models for returns distribution, for instance the Stable Distribution. The interpretation and usage you make out of statistical models can lead to lies, but this has nothing to do with Statistics itself...

 
Malacarne:

That's what I call a good Finance class. 

I only disagree when you say that statistics is the art of lying... actually we have good statistical models for returns distribution, for instance the Stable Distribution. The interpretation and usage you make out of statistical models can lead to lies, but this has nothing to do with statistics itself...

I dont really ment it for all statistics actually, it is something I remember from a statistician joke , so if it means more than what I mean, sorry, 
 
erdogenes:

I used that methods not because I claim something, I research them because someone with mathematical proof claims those are works on Financial data. so you think, Engle is failed, Russel is failed many others, they didnt and doesnt understand what they are doing. 

and sorry, my bad I failed, I gues I couldnt learn the famous simple transform of a non-stationary process into a stationary one, called "difference". or an advenced stuff which can be used with non-stationary processes, called wavelet, sorry. 

Or did you mention  PA's nonstationary nature doesnt allow that kind of research, So in addition, Mandelbrot is failed, because he research and claim this kind of stuff on PA during his life, a math genius couldnt see the consequences what you see, he is not a real mathematician (really? that arrogant?)

I didnt claim this kind of stuff could be used to analyse the financial data (the math stuff i mentioned before), in adverse I just research the available usages, and found this stuff and gone deep, an I claim : intermadiate statistics is enough to explain little things on financial data, the rest are a lie. here no one really needs know advanced mathematics. 

First of all, it seems that you are taking this personally. Well, don't. :)

Point is that it is a random process. Function of a random variable is a random variable: so it can only be described with standard descriptors of stochastic processes.
Results cannot be calculated or predicted.
(Well, actually it could be partially described in a way like weather forecasts is, if the inputs could get collected in similar way)

So i don't know what you have tried to achieve, but it is meaningless to claim that it cannot be described mathematically.

Actually i believe that we agree about this.

 
This topic inspires me to a more embracing question ...

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