Sure man .. are you shocked that I need some results to give some credits to a theory that is yours but which could be anyone else's one ?

Don't you think the less you can expect from a tool here around is mathematical coherence, a number ?

I examine that picture which is lorem ipsum-free less than a minute and I can directly benefit from, that's how Elliott gains in
credibility as a financial theorist.

You want to say no ? Okay! But then bring something consistent enough to replace Elliott !

Once again you are trolling. This is a topic about Neural Network.

Sorry dude, "a troll" about neural networks. The thread has started the 03.08.2019, 3 months & a 1001 lorem ipsum later Alan ...
nothing, just nothing

I have nothing to say because I don't feel competent, yet, in this field. You should do the same, you have nothing to say but you keep continuing to
post, can't you refrain and give us a break ? I am tired to read your stupidities.

Sure man .. are you shocked that I need some results to give some credits to a theory that is yours but which could be anyone else's one ?

Don't you think the less you can expect from a tool here around is mathematical coherence, a number ?

I examine that picture which is lorem ipsum-free less than a minute and I can directly benefit from, that's how Elliott gains in
credibility as a financial theorist.

You want to say no ? Okay! But then bring something consistent enough to replace Elliott !

1. I'm perfectly calm, nobody is shocked. But I must say, comments like yours take the fun out and make me want to leave this forum. They just
interrupt the discussion without an actual contribution.

This is different for example with @Young
Ho Seo. Although he has ideas that I don't share, at least he was really trying to be part of a discussion.

2. Please understand that I appreciate original content more then cluttering a thread with random latin or youtube links.

Where was your latin comment about the comparison of activation functions? I spend personal time with these things and share
information for free, without any hidden benefits,

so if you're not interested please ignore this thread but don't ridicule it with lorem ipsum stuff. That's offensive!

I have no obligation to deliver anything or "bring something", like you say, and before you can make demands, please show some more original
content yourself, like many other people on this forum do, too.

3. Neural networks are not "my" theory and clearly work with numerous examples that we all use every day.

4. You can draw the most picturesque Elliott Waves into a random zig zag and will believe what you see if you stare long enough.

5. I don't owe you anything. And for sure it's not my job to make your $$$. If nothing written here is useful for your own expert advisors, this
isn't my problem.

6. If you're not interested in neural networks, don't read it. Waste your own time with whatever you want, but don't waste other people's
time.

Sorry dude, "a troll" about neural networks. The thread has started the 03.08.2019, 3 months & a 1001 lorem ipsum later Alan ...
nothing, just nothing

... which is because this is a journey. You can learn with me on the way or you just don't. Like anybody, I make mistakes all the time, for example
the original model with autoencoder+LSTM with not the best idea, or e.g. I have learned that models often need more training time.

This thread contains very much information, so "nothing, just nothing" is a slap in the face. What it doesn't contain is free money for Icham
Aidibe.

No such thing as free lunch, you gotta do some work yourself.

... which is because this is a journey. You can learn with me on the way or you just don't. Like anybody, I make mistakes all the time, for
example the original model with autoencoder+LSTM with not the best idea, or e.g. I have learned that models often need more training
time.

This thread contains very much information, so "nothing, just nothing" is a slap in the face. What it doesn't contain is free money for
Icham Aidibe.

No such thing as free lunch, you gotta do some work yourself.

Okay,... enough said, I'm out for today.

Please don't give up because guys like this one. This topic is very helpful and informative.

I'm really sorry man .. I need to know how many of these buying/sellings signals are valids and how many aren't to give some credit. It can't be
taken seriously scientifically without this result.

I talk for me & only for me, not for others, Chris.

I don't need the fruit of your researches, I'm already enjoying a NN (a full-MQL one, not a pattern one), and I do have the elegance to not show
off.

That's all the curiosity I have about what you called the "next level" neural network.

The distribution curves that you link at the bottom look pretty random to me. The problem probably comes with not enough data, as daily
prices were used --> how many fractals(! not prices) will you see per year and what does that say about the accuracy of the
distribution curve? The internet is full of other examples with perfectly smooth price retracement distribution curves, without the
humps. Yes, I'm aware that pure price and fractals derived from price are not exactly the same, but we should at least see some
irregularities. By the way: I tried this experiment a while a go, because I was curious myself.

About How many fractal ?

Finanial market is the system of fractal. you can express the financial market with endless number of fractal pattern. The financial market has
loose self similarity instead of strict self similarity. This is something very contrast to the strict self similarity found in nature. But
even in nature there are many things are in fact loose self similarity like coastal line and mountain, etc. So this is nothing to surprise.

How to visuallize this fractal patterns for our understading ?

We use Peak Trough Transformation or (Peak Trough Analysis). The peak trough transformtion will help us to transform the price series into
fractal wave, that is more visually readable. Especially, some good peak trought transformation tool is zig zag indicator or renk
charting.

To help you to understand better, in cycle study, we will use Fourier analysis or other exploratory analysis tools to reveal the cycles
inside price series. You can think Peak Trough Analysis in fractal wave study is the equivalent tool to fourier anlaysis in cycle
study.

When you apply peak trough analysis to price series obtained from Forex and Stock Market, now you can see each peaks and troughs alternating.
That helps you to count the triangular form of fractal wave.

I am still explaining about how many fractal in your question.

Basically you have to count every small and big triangles in your price series.

You will start from smallest fracal triangle you can. Then you count bigger triangle made up from these small triangle. And you keep going on
until you are counting the biggest triangles in your price series.

Typically at the end, you are only having one triangle or two triangles in your price series as it can be seen in grid spacing 84.

After you have counted all of these triangles, you will count the occurances of triangle with particular ratio you want to study.

For example, if you are curious with the significance of fibonaaci ratio 0.618, then you just count all the fractal triangle with 0.618
ratio.

For example, if the number of triangle with 0.618 ratio was 60 and number of all triangles in price series were 200. Then its frequency
is 30% = 60/200.

Here you will count small triangle and big triangle if they both has the ratio of 0.618. For example, if small triangle has Y1/Y2 = 61.8 pips /100
pips = 0.618, this will be counted.

At the same time, if big triangle has Y1/Y2 = 618 pips/1000 pips = 0.618, this will be counted too. Likewise, if we have some triangle with ratio
Y1/Y2 = 6.18 pips/ 10 pips = 0.618 is still counted.

In practice you will count the triangle if their ratio is near enough to 0.618. Hence, this entire process is still based on
some approximation.

There are two important points to understand the fractal wave.

Point 1.In Fractal wave, we omitt many data in the price series. We only concerns the data points that actually form the
fractal wave structure (i.e. peaks and troughs). Since many data scientiests emphasise to use entire data in the price series, this
can be contrasting. In factal, we ignore many data, does not contribute to wave structure. Hence, price distribution is hard to tell
the significance of Fibonacci ratio or other ratios.

Point 2.Fractal wave study like Harmoic Pattern, Elliott Wave and X3 pattern are scale independent. For example, small
triangle with Y1/Y2 = 50 pips/100 pips and bigger triangle with Y1/Y2 = 100 pips/ 200 pips are equal fractal triangle with 0.500 ratio.
It is same for rest of study. Small and big Gartley pattern with structure R0: 1.618, R1: 0.382 -0.886, R2: 0.618 can be considered as
the same. This goes the same for small and big Elliot wave pattern too.

But you have to note that many trading tools like techncial indicators is not scale independent. For example, moving average with
period 20 is averge of the past 20 historical data. 20 period (i.e. 20 candle bars) is the size of window you are consistently
viewing your data.

In fractal wave study, you have to concern the triangle made up from 10 candle bars as well as triangle made up 100 or 2000 candle bars in the same
study.

In summary, Pattern Study (or Fractal wave study) is very different animal from most of existing technical indicators we
know and we use. The important question to ask is do you believe that fractal wave is actually existing in price series or not ?

First of all: thank you for your comprehensive answer. You give good and interesting explanations. Just one thing:
according to the title, people expect to read about neural networks; please open a new thread that only deals with fractals, Eliott Waves
and/or Fibonacci numbers (or use one of the existing threads about this), if you want to discuss more in detail (apart from that.. sure,
we can do that). Out of respect for your detailed answer, I nevertheless won't leave you without a response here, but let's continue
elsewhere if that is your interest.

I have a good understanding of how fractals in trading are made, so there is no need for an explanation, but still: I respect your answer and it
will probably be informative for many who haven't heard much about fractals.

My point about "how many fractals" simply was that you always need at least three datapoints (in that example: candles from a daily time
chart) to build a triangle, so you always end up with a lot less fractals than datapoints. When using a daily chart, this obviously isn't that
many fractals per year, which also sets a limit to the expected accuracy of distribution curves collected from just ten years of
daily candles, because this sets the upper(!) limit of how many fractals you can have. It is obvious, according to the
hypothesis of scale-independence, that the
lower limit is just 1, meaning you have zoomed completely out of the picture.

Just to avoid confusion: Although there is no theoretical upper limit in fractal geometry and although we could
infinitely "zoom in" into the picture in computer models, praxis often tells us otherwise, i.e. if we zoom into a cauliflower or the bronchi
branches inside a lung, we see self-repetition on many levels, but there is a limit at least once you reached the cellular level. It's the same
in trading: fractals could not go beyond the tick level. Of course, this isn't a problem in praxis - I just wanted to clearify this if I talk
about "limits".

I also understand - if we talk about something like "daily" candles - that it's not important to take the time factor for x-axis scaling.
Infact, I remember an interview I once saw with Mandelbrot where he said (this was about finance) that he was more pleased with his findings
once he took the time factor out of the equation, so it's a good point that you mention renko charts.

You see, as I'm talking about fractals myself, obviously I don't doubt their existence, even not in trading. I worked with fractals myself
(just in a different way) and think they can be useful to divide prices into meaningful portion on whatever detail level and, more so,
peak/trough analysis probably is the go-to method to scan for support and resistance levels. This is why I have no doubt that fractals are
useful.

My point of critique is about the kind of self-repetition and predictability. Because fractals in trading (and also often in
nature) have an uncanny simple base structure - just think of Mandelbrot's famous z²+c formula - it doesn't come as a suprise that we see
self-similarity on all levels/scales. We need only very few parameters to define a fractal in trading, like the amount of retracement and
the momentum/steepness of the triangle edges (or anything that relates the x-axis to the y-axis). With only so little parameters, it is
impossible not to see similar triangles over and over again. But you never know which triangle appears when. What's missing is regularity. I
know, Mandelbrot didn't like the opposing term "irregularity" and prefered "roughness". So let's take examples from nature again: the
cauliflower has a measurable roughness and near-perfect self-repetition [edit: I'm talking about this "Romanesco" type stuff... sorry,
no vegetable expert ;-) ]. If you know what the global shape looks like, you will find almost the same shapes on lower levels and therefore it
isn't hard to unravel their probable shape almost perfectly, without ever having seen them. This is different with the mountain or
coastline examples: you could computer-generate them by only giving the roughness as a single variable. But the problem is: you will
generate
some mountain, but not always the same mountain. Every newly generated mountain will be completely different. We
have self-similarity, but also complete randomness at the same time. This doesn't mean that other traits of fractals aren't true: you will
find mountain-like shapes on any scaling level, maybe even down to an atomic lower limit. But with near-constant roughness and
self-similarity only we don't automatically have self-identity and predictability, too. Back to trading, price fractality is like the
mountain, but not the cauliflower, if you like. [edit: you're probably referring to the same thing as "loose similarity", that's all fine,
but it's a problem for trading decisions; it's a big step from just recognizing fractals or building a fractal-based notation system to
valid (repeatable) signals; with the latter I'm missing the empirical proof that this is possible with statistic significance]

I hope this was a good explanation about the limitations of fractals in trading. Just because we often observe near-perfect
self-repetition in nature, there are also many differing examples, i.e. self-repeating near-chaos. The latter isn't useful for
predictability at all - and this is the problem we have in trading.

I mentioned several times in this thread that I don't actually believe in perfect chaos in trading. If I did,
trading would be pointless - I should walk away and never trade again. There is at least a small amount of non-randomness, mostly during
extreme events (e.g. if the price has just dropped by 100 pips within a minute, who's gonna catch the knife and what's the more probable
direction of the next tick?). My personal philosophy therefore is that trading works best by reacting to the exception (machine learning is
just a method for automatic finding such higher probability situations) - and by the way: even Mandelbrot, as an expert on distribution-
tails(!) would have agreed with me on this. Self-repetition on the other hand is pretty much the opposite: we react to expected
regularity. This just doesn't work in trading! You "trade the mountain, not the cauliflower". This is why fractals (and Elliott Waves!)
fail in trading. But to stay in the analogy: what happens if a (specific) moutain is the exception of the expected fractal geometry, i.e. if we
find areas of much less chaos than the average of the rest of the mountain? Just imagine a steep cliff that has just dropped by 1000 feet 90
degrees down: what's more likely... that out of pure chance you have just found exactly the point where the cliff ends or might it continue
down by another few feet? I hope you understand how this relates to trading: it's much better to trade the scenarios where fractality rules
apply the least.

By the way: what seems like a good idea in order to actually get something useful out of fractals (instead of some Elliott voodoo
predictions): finding a measure of fractality "roughness" in trading (or simply copy this from Mandelbrot's work), then measure its
variation and take the exceptions, i.e. those regions with extremly high or low roughness for generating trading signals (e.g. using
roughness as trend/range separation). Something like a "roughness-standard-deviation-oscillator". Interesting, right?

I think I gave some undeniable arguments, but I'm sure I'm not going to convince you if you are deep into the subject for many years and I'm now
asking you to think out of the box again. We both have our convictions and that's okay.

(again, just please: open another thread if you feel that a further discussion is necessary (although I don't think myself that there is much left
to clarify)).

I'm really sorry man .. I need to know how many of these buying/sellings signals are valids and how many aren't to give some credit. It can't
be taken seriously scientifically without this result.

I talk for me & only for me, not for others, Chris.

I don't need the fruit of your researches, I'm already enjoying a NN (a full-MQL one, not a pattern one), and I do have the elegance to not
show off.

That's all the curiosity I have about what you called the "next level" neural network.

This apart.... bof.

I'm participating on this forum for some intellectual exchange; your credit is the least of my concerns

you have taken my screenshot/.gif completely out of its context; this wasn't even showing a trading session or any tradable "signals",
but a network
training session, just to illustrate how a network visualization can be helpful; this will probably exceed your comprehension
if you see everything through the dollar filter, but for designing a neural network it is for example useful to know how many neurons per
layer I need. If for example I see that I find more apoptosis (selective neuron "death" due to low relative activation scoring) in the
deeper layers (there is a tendency that infact I do) and which I can spot at a single glance via the used color scheme, then I know I'd get
away with using less neurons in the deeper layers in the first place, just to give an example. If understanding the usefulness goes
beyond you, please don't bother me and continue in some "hundred bucks to 10 million in a year" martingale thread

I have no idea what a "pattern-one" is; my neural network library accepts any inputs that you can put into numbers, there is no
"pattern" limitation; and what again isn't full-MQL ???

you just completely disqualified yourself with the backtest that you have the elegance not to show off with - just look at line 2
(path 19): >3 millions from 1000 ($?) with a profit factor of >4 averaged over almost 10000 trades and with an incredibly low
drawdown of just 0.25% (!!!) .... that's a lot of risk-free Pina Colada. If you don't see how there's a problem with that, then we don't
share a common language and I suggest you to enjoy the fairytale elsewhere while it lasts

Icham Aidibe:Sure man .. are you shocked that I need some results to give some credits to a theory that is yours but which could be anyone else's one ?

Don't you think the less you can expect from a tool here around is mathematical coherence, a number ?

I examine that picture which is lorem ipsum-free less than a minute and I can directly benefit from, that's how Elliott gains in credibility as a financial theorist.

You want to say no ? Okay! But then bring something consistent enough to replace Elliott !

Alain Verleyen:Once again you are trolling. This is a topic about Neural Network.

Sorry dude, "a troll" about neural networks. The thread has started the 03.08.2019, 3 months & a 1001 lorem ipsum later Alan ... nothing, just nothing

Icham Aidibe:Sorry dude, "a troll" about neural networks. The thread has started the 03.08.2019, 3 months & a 1001 lorem ipsum later Alan ... nothing, just nothing

Icham Aidibe:Sure man .. are you shocked that I need some results to give some credits to a theory that is yours but which could be anyone else's one ?

Don't you think the less you can expect from a tool here around is mathematical coherence, a number ?

I examine that picture which is lorem ipsum-free less than a minute and I can directly benefit from, that's how Elliott gains in credibility as a financial theorist.

You want to say no ? Okay! But then bring something consistent enough to replace Elliott !

1. I'm perfectly calm, nobody is shocked. But I must say, comments like yours take the fun out and make me want to leave this forum. They just interrupt the discussion without an actual contribution.

This is different for example with @Young Ho Seo. Although he has ideas that I don't share, at least he was really trying to be part of a discussion.

2. Please understand that I appreciate

originalcontent more then cluttering a thread with random latin or youtube links.Where was your latin comment about the comparison of activation functions? I spend personal time with these things and share information for free, without any hidden benefits,

so if you're not interested please ignore this thread but don't ridicule it with lorem ipsum stuff. That's offensive!

I have no obligation to deliver anything or "bring something", like you say, and before you can make demands, please show some more

originalcontent yourself, like many other people on this forum do, too.3. Neural networks are not "my" theory and clearly work with numerous examples that we all use every day.

4. You can draw the most picturesque Elliott Waves into a random zig zag and will believe what you see if you stare long enough.

5. I don't owe you anything. And for sure it's not my job to make your $$$. If nothing written here is useful for your own expert advisors, this isn't my problem.

6. If you're not interested in neural networks, don't read it. Waste your own time with whatever you want, but don't waste other people's time.

7. Entire religions are built around credibility.

Icham Aidibe:Sorry dude, "a troll" about neural networks. The thread has started the 03.08.2019, 3 months & a 1001 lorem ipsum later Alan ... nothing, just nothing

... which is because this is a journey. You can learn with me on the way or you just don't. Like anybody, I make mistakes all the time, for example the original model with autoencoder+LSTM with not the best idea, or e.g. I have learned that models often need more training time.

This thread contains very much information, so "nothing, just nothing" is a slap in the face. What it doesn't contain is free money for Icham Aidibe.

No such thing as free lunch, you gotta do some work yourself.

Okay,... enough said, I'm out for today.

Chris70:... which is because this is a journey. You can learn with me on the way or you just don't. Like anybody, I make mistakes all the time, for example the original model with autoencoder+LSTM with not the best idea, or e.g. I have learned that models often need more training time.

This thread contains very much information, so "nothing, just nothing" is a slap in the face. What it doesn't contain is free money for Icham Aidibe.

No such thing as free lunch, you gotta do some work yourself.

Okay,... enough said, I'm out for today.

I'm really sorry man .. I need to know how many of these buying/sellings signals are valids and how many aren't to give some credit. It can't be taken seriously scientifically without this result.

I talk for me & only for me, not for others, Chris.

I don't need the fruit of your researches, I'm already enjoying a NN (a full-MQL one, not a pattern one), and I do have the elegance to not show off.

That's all the curiosity I have about what you called the "next level" neural network.

This apart.... bof.

Just short Comment on this.

The distribution curves that you link at the bottom look pretty random to me. The problem probably comes with not enough data, as daily prices were used --> how many fractals(! not prices) will you see per year and what does that say about the accuracy of the distribution curve? The internet is full of other examples with perfectly smooth price retracement distribution curves, without the humps. Yes, I'm aware that pure price and fractals derived from price are not exactly the same, but we should at least see some irregularities. By the way: I tried this experiment a while a go, because I was curious myself.

About How many fractal ?Finanial market is the system of fractal. you can express the financial market with endless number of fractal pattern. The financial market has loose self similarity instead of strict self similarity. This is something very contrast to the strict self similarity found in nature. But even in nature there are many things are in fact loose self similarity like coastal line and mountain, etc. So this is nothing to surprise.

How to visuallize this fractal patterns for our understading ?

We use Peak Trough Transformation or (Peak Trough Analysis). The peak trough transformtion will help us to transform the price series into fractal wave, that is more visually readable. Especially, some good peak trought transformation tool is zig zag indicator or renk charting.

To help you to understand better, in cycle study, we will use Fourier analysis or other exploratory analysis tools to reveal the cycles inside price series. You can think Peak Trough Analysis in fractal wave study is the equivalent tool to fourier anlaysis in cycle study.

When you apply peak trough analysis to price series obtained from Forex and Stock Market, now you can see each peaks and troughs alternating. That helps you to count the triangular form of fractal wave.

I am still explaining about how many fractal in your question.Basically you have to count every small and big triangles in your price series.

You will start from smallest fracal triangle you can. Then you count bigger triangle made up from these small triangle. And you keep going on until you are counting the biggest triangles in your price series.

Typically at the end, you are only having one triangle or two triangles in your price series as it can be seen in grid spacing 84.

After you have counted all of these triangles, you will count the occurances of triangle with particular ratio you want to study.

For example, if you are curious with the significance of fibonaaci ratio 0.618, then you just count all the fractal triangle with 0.618 ratio.

For example, if the number of triangle with 0.618 ratio was 60 and number of all triangles in price series were 200. Then its frequency is 30% = 60/200.

Here you will count small triangle and big triangle if they both has the ratio of 0.618. For example, if small triangle has Y1/Y2 = 61.8 pips /100 pips = 0.618, this will be counted.

At the same time, if big triangle has Y1/Y2 = 618 pips/1000 pips = 0.618, this will be counted too. Likewise, if we have some triangle with ratio Y1/Y2 = 6.18 pips/ 10 pips = 0.618 is still counted.

In practice you will count the triangle if their ratio is near enough to 0.618. Hence, this entire process is still based on some approximation.

There are two important points to understand the fractal wave.Point 1.In Fractal wave, we omitt many data in the price series. We only concerns the data points that actually form the fractal wave structure (i.e. peaks and troughs). Since many data scientiests emphasise to use entire data in the price series, this can be contrasting. In factal, we ignore many data, does not contribute to wave structure. Hence, price distribution is hard to tell the significance of Fibonacci ratio or other ratios.Point 2.Fractal wave study like Harmoic Pattern, Elliott Wave and X3 pattern are scale independent. For example, small triangle with Y1/Y2 = 50 pips/100 pips and bigger triangle with Y1/Y2 = 100 pips/ 200 pips are equal fractal triangle with 0.500 ratio. It is same for rest of study. Small and big Gartley pattern with structure R0: 1.618, R1: 0.382 -0.886, R2: 0.618 can be considered as the same. This goes the same for small and big Elliot wave pattern too.But you have to note that many trading tools like techncial indicators is not scale independent. For example, moving average with period 20 is averge of the past 20 historical data. 20 period (i.e. 20 candle bars) is the size of window you are consistently viewing your data.

In fractal wave study, you have to concern the triangle made up from 10 candle bars as well as triangle made up 100 or 2000 candle bars in the same study.

In summary, Pattern Study (or Fractal wave study) is very different animal from most of existing technical indicators we know and we use. The important question to ask is do you believe that fractal wave is actually existing in price series or not ?@Young Ho Seo

First of all: thank you for your comprehensive answer. You give good and interesting explanations.

Just one thing: according to the title, people expect to read about neural networks; please open a new thread that only deals with fractals, Eliott Waves and/or Fibonacci numbers (or use one of the existing threads about this), if you want to discuss more in detail(apart from that.. sure, we can do that). Out of respect for your detailed answer, I nevertheless won't leave you without a response here, but let's continue elsewhere if that is your interest.I have a good understanding of how fractals in trading are made, so there is no need for an explanation, but still: I respect your answer and it will probably be informative for many who haven't heard much about fractals.

My point about "how many fractals" simply was that you always need at least three datapoints (in that example: candles from a daily time chart) to build a triangle, so you always end up with a lot less fractals than datapoints. When using a daily chart, this obviously isn't that many fractals per year, which also sets a limit to the expected accuracy of distribution curves collected from just ten years of

dailycandles, because this sets theupper(!)limit of how many fractals you can have. It is obvious, according to the hypothesis of scale-independence, that thelowerlimit is just 1, meaning you have zoomed completely out of the picture.Just to avoid confusion: Although there is no

theoreticalupper limit in fractal geometry and although we could infinitely "zoom in" into the picture in computer models, praxis often tells us otherwise, i.e. if we zoom into a cauliflower or the bronchi branches inside a lung, we see self-repetition on many levels, but there is a limit at least once you reached the cellular level. It's the same in trading: fractals could not go beyond the tick level. Of course, this isn't a problem in praxis - I just wanted to clearify this if I talk about "limits".I also understand - if we talk about something like "daily" candles - that it's not important to take the time factor for x-axis scaling. Infact, I remember an interview I once saw with Mandelbrot where he said (this was about finance) that he was more pleased with his findings once he took the time factor out of the equation, so it's a good point that you mention renko charts.

You see, as I'm talking about fractals myself, obviously I don't doubt their existence, even not in trading. I worked with fractals myself (just in a different way) and think they can be useful to divide prices into meaningful portion on whatever detail level and, more so, peak/trough analysis probably is the go-to method to scan for support and resistance levels. This is why I have no doubt that fractals are useful.

My point of critique is about the kind of

self-repetition and predictability. Because fractals in trading (and also often in nature) have an uncanny simple base structure - just think of Mandelbrot's famous z²+c formula - it doesn't come as a suprise that we see self-similarity on all levels/scales. We need only very few parameters to define a fractal in trading, like the amount of retracement and the momentum/steepness of the triangle edges (or anything that relates the x-axis to the y-axis). With only so little parameters, it is impossible not to see similar triangles over and over again. But you never know which triangle appears when. What's missing is regularity. I know, Mandelbrot didn't like the opposing term "irregularity" and prefered "roughness". So let's take examples from nature again: the cauliflower has a measurable roughness and near-perfect self-repetition [edit: I'm talking about this "Romanesco" type stuff... sorry, no vegetable expert ;-) ]. If you know what the global shape looks like, you will find almost the same shapes on lower levels and therefore it isn't hard to unravel their probable shape almost perfectly, without ever having seen them. This is different with the mountain or coastline examples: you could computer-generate them by only giving the roughness as a single variable. But the problem is: you will generatesomemountain, but not always thesamemountain. Every newly generated mountain will be completely different. We have self-similarity, but also complete randomness at the same time. This doesn't mean that other traits of fractals aren't true: you will find mountain-like shapes on any scaling level, maybe even down to an atomic lower limit. But with near-constant roughness and self-similarity only we don't automatically have self-identity and predictability, too. Back to trading, price fractality is like the mountain, but not the cauliflower, if you like. [edit: you're probably referring to the same thing as "loose similarity", that's all fine, but it's a problem for trading decisions; it's a big step from just recognizing fractals or building a fractal-based notation system to valid (repeatable) signals; with the latter I'm missing the empirical proof that this is possible with statistic significance]I hope this was a good explanation about the limitations of fractals in trading. Just because we often observe near-perfect self-repetition in nature, there are also many differing examples, i.e. self-repeating near-chaos. The latter isn't useful for predictability at all - and this is the problem we have in trading.

I mentioned several times in this thread that I don't actually believe in

perfectchaos in trading. If I did, trading would be pointless - I should walk away and never trade again. There is at least a small amount of non-randomness, mostly during extreme events (e.g. if the price has just dropped by 100 pips within a minute, who's gonna catch the knife and what's the more probable direction of the next tick?). My personal philosophy therefore is that trading works best by reacting to the exception (machine learning is just a method for automatic finding such higher probability situations) - and by the way: even Mandelbrot, as an expert on distribution-tails(!) would have agreed with me on this. Self-repetition on the other hand is pretty much the opposite: we react to expected regularity. This just doesn't work in trading! You "trade the mountain, not the cauliflower". This is why fractals (and Elliott Waves!) fail in trading. But to stay in the analogy: what happens if a (specific) moutain is the exception of the expected fractal geometry, i.e. if we find areas of much less chaos than the average of the rest of the mountain? Just imagine a steep cliff that has just dropped by 1000 feet 90 degrees down: what's more likely... that out of pure chance you have just found exactly the point where the cliff ends or might it continue down by another few feet? I hope you understand how this relates to trading: it's much better to trade the scenarios where fractality rules apply the least.By the way: what seems like a good idea in order to actually get something useful out of fractals (instead of some Elliott voodoo predictions): finding a measure of fractality "roughness" in trading (or simply copy this from Mandelbrot's work), then measure its variation and take the exceptions, i.e. those regions with extremly high or low roughness for generating trading signals (e.g. using roughness as trend/range separation). Something like a "roughness-standard-deviation-oscillator". Interesting, right?

I think I gave some undeniable arguments, but I'm sure I'm not going to convince you if you are deep into the subject for many years and I'm now asking you to think out of the box again. We both have our convictions and that's okay.

(again, just please: open another thread if you feel that a further discussion is necessary (although I don't think myself that there is much left to clarify)).

Icham Aidibe:I'm really sorry man .. I need to know how many of these buying/sellings signals are valids and how many aren't to give some credit. It can't be taken seriously scientifically without this result.

I talk for me & only for me, not for others, Chris.

I don't need the fruit of your researches, I'm already enjoying a NN (a full-MQL one, not a pattern one), and I do have the elegance to not show off.

That's all the curiosity I have about what you called the "next level" neural network.

This apart.... bof.

trainingsession, just to illustrate how a network visualization can be helpful; this will probably exceed your comprehension if you see everything through the dollar filter, but for designing a neural network it is for example useful to know how many neurons per layer I need. If for example I see that I find more apoptosis (selective neuron "death" due to low relative activation scoring) in the deeper layers (there is a tendency that infact I do) and which I can spot at a single glance via the used color scheme, then I know I'd get away with using less neurons in the deeper layers in the first place, just to give an example. If understanding the usefulness goes beyond you, please don't bother me and continue in some "hundred bucks to 10 million in a year" martingale thread