I made some experiments with different kernels and I would like to share among friends some ideas.

Well it is about to use a function expressing chaotic behaviour itself as a kernel.

The simplest thing was to look at the most simple logistic map.

Xn+1 = r Xn (1 - Xn)

That is the equation of the logistic map. What we can do with that.

What is interesting for us is the parameter r. If we vary the parameter we will have different behaviour (sensitivity of the initial conditions). I keep it short here but we can get some very interesting stretching-and-folding structures , please look at the Wikipedia article.

This is a 3d plot of the logistic map (with just iteration ) for x varying between 0.001 and 1. and r from 1 to 2.

And this is with just one cycle. It is necessary to use parameters from 0 to 1 if more iterations are going to be used.

And this can be used as a kernel for some specific trading strategies. I have in mind some strategies like EURCHF strategy.

However we can go further.

So the idea as as follows:

Given the logistic map equation:

Xn+1 = r Xn (1 - Xn)

we can vary three parameters and to use this as a kernel:

1. We can vary x (from 0 to 1 with step 0.01, or 0.001 for example): this is giving us the initial conditions

2. We can vary the r parameter (from 1 to with step 0.01, or 0.001 for example) this is the parameter governing the chaos. r approximately 3.57 is the onset of chaos, so we can limit from 1 to 3.

3. And of course finally we can vary the limitmap parameter governing the number of iterations. This is the third parameter for the difference equation. Here I need some help because the code I made is missing to plot the values of first two iterations and it is beginning to plot from the third.

So We can call this chaos kernel, in fact I do not know even if that has a name. I did not find in the available litterature anything of this kind. So it is possible that this appears on this site for the first time. I know crazy it is.

The two MAIN ideas ARE:

- TO USE RECURRENT DIFFERENCE EQUATION GENERATING COMPLEX BEHAVIOR with GENETIC OPTIMIZER

- TO USE SOME PORTION OF CHAOS OF THE KERNEL ITSELF

If that is true with just one kernel we have something extremely powerfull. And even more, there are many other maps waiting to be exploited as kernels.

I am using here the logistic map because it is something extremely simple but generating extremely complex mathematical behavior.

Using chaos kernel to harness the chaos itself ;). Why not after all?

Below you can see the plot under MT5 of the logistic map.

This is the MT5 code (I miss the first two iterations any help would be welcome).

Basically this can be used as a chaotic kernel.

Here is the code of the logistic kernel (chaos kernel, in fact it does not have a name yet) you can implement in your trading strategies:

double chaoskernel()

{

//---

double w, j;

w=(r*x*(1-x));

for(j=0;j<=limitmap;j++)

{

w=(r*w*(1-w));

}

double p1 = iCustom(TimeFrame,Symbol(),"PFE",x5,true,5,0,0);// This is a NORMALIZED input

return(w* p1);// We multiply the input value by the weight

}

The 3-D technology that's currently used in movies and VR requires two visual images, one from each eye, combining in the viewer's brain to produce 3-D's extra layer of depth. Actually believe it or not for 3D experience you do not need two images. The scientist who first discovered it is named Dr Vishwanath.

I can make a reference in mainstream media.

http://edition.cnn.com/2013/10/14/tech/innovation/3d-one-eye/

The problem with Vishwanath discovery is that you can have 3 D experience but you need to use only one eye. And that is cool, maybe fun but not practical for playing games or watching some videos on youtube.

So I found a way to have 3D experience using both eyes with a mind trick. The idea is to trick the mind that a flat image from a flat screen is actually a window. However a friend of mine who experienced that for the first time got scared.

The reason is that you use your brain to create depth, and once experienced you like it. And when you like it you just keep those new brain settings. I mean whenever you see a flat image with sufficient depth information your brain interprets that information as 3D image. That does not mean that you fake 3D, you only see if there is enough information as depth perspective; shadows etc.

I discovered this by accident 4 moths ago. I was thinking should I make a business out of it. But it is a skill it is not really a technology. And it belongs to every human on this planet for free.

I use this for playing flight simulators games. Well for me it is amazing because I have a better depth perception that allows me to get those snap shots I found so hard before and anticipate trajectories in space much better.

There are some aids that can be used but the cost is barely 3 USD and everybody perhaps has already the materials to build them at home.

If there is any interest I can go into further details by describing here on the forum how to do that.

This is just my theory explaining how things do work.

Whenever you have a flat screen and you are watching with both eyes. The 2 pictures make figure out the brain encoding that you have a flat screen in front of you. As the screen is flat the image should be flat too.

The one eye experiment makes a 3 D by playing with the image processing of the brain. With one eye the processing system cannot know if this is a flat screen or it is window. The higher the resolution the higher chance to figure out that it is a window. To make so the original experiment uses a hole with diameter of 1 cm. in order to hide every other reference the image on the screen. Within a minute or two for every person you can switch the encoding and your visual cortex in the brain will start to process the image as 3D. (I use a different technique, my technique is to use tilted hand that hides the image of the screen for just one allowing peripheral vision, it works too.).

My surprise was that some people making this exercise from one eye and the other eye. Kept this new brain encoding with both eyes open. Go pro camera clips on you tube and Battlefield videos work best. A very good example is the crane mounting videos with gopro on youtube with 1440 resolution.

However things do not end there. I was not able to see 3d on a flat screen with both eyes open. I needed something to keep the 1 eye 3d. I did something simple. Just take cheap sunglasses. Remove one glass and watch like that. I created a visual differential as illumination between the left and right picture. And that works like a charm. But first it is needed to be able to have the 1 eye 3D effect through exercise.

I use 3 D glasses from cinema with linear polarizatin filter. I remove the one glass and put it on the other sidme. By rotating I can gradually change the illumination. In that case I can use the lowest possible but working differential.

I was afraid that this may be dangerous. So I tested on me for 4 months before writing publicly about that. I even made my grandmother see the 3 D and a lot of friends.

This works for distant things for close objects the brain still requires the 2 images.

I have been examiming at the iVAR indicator for some time now and I seriously have some doubts regarding its eficacy in accurately detecting the trend of the market .

Attched below is a screenshot which I just pulled out from my MT4 clearly showing a flat market. But looking that the iVAR beneath, one can clealy see that it failed to detect it.

Any comments and suggestions is welcome for better or similar indicator to detect such a market would be appreciated.

Thank you.

]]>The today markets are largely dominated by the machines and algorythmic trading. Big fundamental stakes are also present, and the mass psychology cannot be exluded aswell.

The Elliott waves principle was created in order to take into account the big mass psychology trading patterns. The fractal structure of the market was noted expirically way before formal scientific publications.

The classical Elliottists either continue to apply the principle as nothing changed or completely gave up. The experiments of modifing the classic works are not conclusive e.g. Neo Waves.

What you can do as a trader is to switch markets and use the principle in markets away from Forex and Stocks. Think crypto currencies. Take a look, make some counts. See if it works.

Another idea is to use the Elliott test.

The hypothesis is as follows. The market is fractal. At different levels different logics may be present. In the old times at all levels all participants were human today it is not so.

Normally the high frames are dominated by big and well thought interventions. The low frames are algorythmic.

The problem is that at high frames the human considerations of the big players are different from the constitutive ingredients fear and greed of the Elliott wave theory.

At low frames the algorythmic executions are also alien to the fear and greed paradigm.

So you can see now the limitations of the Elliott theory in the modern markets.

What we can call Elliott test is just another hypothesis.

**The idea is that the count has to be obvious. If the count is not obvious this does not mean it is complicated or hidden. It means it does not exist at all.**

HI all, Salut à tous

Remember : - Medicine is just applied biology, biology is applied chemistry, chemistry is applied physics. And physics is just applied maths. THERE IS ONLY MATHEMATICS. (sic CB)

La médecine est de la biologie appliquée, la biologie est de la chimie appliquée, la chimie est de la physique appliquée. la physique n'est que des mathématiques appliquées. Seules les mathématiques comptent

Goal of my life

************

Creating a micro HF

Meeting VED

building new futur

Reality is whatever refuses to go away when I stop believing in it. Ph.K.Dick

take a look on http://www.eainstall.com/

It's nice and simple

]]>In our trading methods and experiments here on Forex-TSD and Forex Factrory that way basically to move from complexity to simplicity.

What does it mean in practice? We used some advanced methods in order to preprocess the time series. There are different methods: as for me I was interested in the practical implementations of the jurik type of digital filters (the most popular among traders) (Thanks to Kositsin) and the SSA (Singulat Spectrum Analysis Thanks to Klot).

The idea came as I saw an isolated project in the Russian language Alpari forums for using digital filters in order to smooth the time series and then putting it into more complex trading strategies. This was the first digital Brain Trend.

After that this was popullarized on Forex - TSD, and many with my friends here we were together (Camaron, Jaguar , Francis, Albert etc.) into this. On Forex Factory we experimented with SSA and feeding it into more complex strategies like Trend magic, TDI (Traders dynamic index), ASCTrend become the Digital ASCTrend.

Later on, I was surprised to learn that this had an effect, on the mql5 database, the MT5 version of the digital Brain Trend become one of the most rated systems (the second and the third place).

We turned into the complex SSA indicators as the ultimate form of sophistication for an oscillator. The oscillator idea in its best.

In fact in the SSA approach (end-pointed and Noxa) they use complex matrix calculations: eigen decomposition of a matrix.

SSA uses singular value decomposition and extracts the trend, the cycle (seasonal components ) and noises.

Then the Noxa CSSA for example is using a genetic optimizer in order to find the best parameters of the

NOXA CSSA for buy and sell. However that does not work so well in practice and is away from the expected money mochine I have believed to be.

Yes it works but you need to carefully examine the market state using a discretionary approach (I did not find yet a way to make an algorythm for market state detection).

Here are example of scripts using metatrader for Support Vector Machine. There is also a matrix indicator, one of a kind.

So the classical implementation of the SSA is using a method to decompose and simplify the

indormation. And from then to use a direct approach to derive by genetic algorythm or other ai learning way trading rules.

However ithas been argued that **Generalizing a problem can make the solution simpler or more complicated, and it’s often hard to predict which beforehand. **

So it may sound as a paradox but it may appear that using a complex algorythm like SSA, Independant Component Analysis, Wavelets etc. may actually leave you with a bigger problem.

In fact those preprocessing tools are used because the direct approach of feeding the price into a Neural networks makes it close to impossible to make a good prediction because of the specifics of the neural nets that make a lot of local optima.

However with the rise of the kernel machines, they use a complete different paradigm. They say, yes it exist a simple solution, but in order to find that solutions we may need to map it into high dimensional space and they (the kernel does exactly that), we may find a linear solution into this space.

And for that you do not need to map all the points into this multidimensional mathematical space (that is the kernel trick, brilliant).

So here I make a loop to the beginning, if we have a light matrix indicator we may feed it within the kernel. With SSA ep we cannot practically do that because it is very demanding of computational ressources to make a lot of passes.

]]>

**Well it is about to use a function expressing chaotic behaviour itself as a kernel.**

The simplest thing was to look at the most simple logistic map.

That is the equation of the logistic map. What we can do with that.

What is interesting for us is the parameter r. If we vary the parameter we will have different behaviour (sensitivity of the initial conditions). I keep it short here but we can get some very interesting stretching-and-folding structures , please look at the Wikipedia article.

От Поле за пускане |

#property copyright "Copyright 2011, MetaQuotes Software Corp."

#property link "http://www.mql5.com"

#property version "1.00"

//--- input parameters

input double x=0.5;input double r=1;

//+------------------------------------------------------------------+

//| Tester function |

//+------------------------------------------------------------------+

double OnTester()

{

//---

double w0 = r*x*(1-x);

double w1 = r*w0*(1-w0);

double w2 = r*w1*(1-w1);

double w3 = r*w2*(1-w2);

double w4 = r*w3*(1-w3);

//---

return(w2);

}

This is the code that can be used to make mathematical simulations in Metatrader 5.

Here I do not use for and while operators, because I want the code to be mathematically explicit for the non coders here.

Well what we can do next and yes we can replace the x by the gaussian kernel (the gaussian kernet iteslf has x and y and the output varies from 0 to 1). So it is fine let take a look what we get.

От Поле за пускане |

The code is here:

#property copyright "Copyright 2011, MetaQuotes Software Corp."

#property link "http://www.mql5.com"

#property version "1.00"

//--- input parameters

input double x=-3.0;

input double y=-3.0;

input double r=1;

//+------------------------------------------------------------------+

//| Tester function |

//+------------------------------------------------------------------+

double OnTester()

{

//---

double w0 = MathExp(-x*x-y*y);

double w1 = r*w0*(1-w0);

double w2 = r*w1*(1-w1);

double w3 = r*w2*(1-w2);

double w4 = r*w3*(1-w3);

//---

return(w2);

So in the chaos kernel we have 3 parameters (look at the comments). x and y are coming from the gaussian kernel the r is from the logistic map equation.

But there is something more, lol, look at the recurrence relation, we can use the first cycle w1, the second, w2, the thirs w3 (it is up to us to choose how far we can go). So w2 is more complex that w0 and w3 has a more complex structure than w2.

The logistic map stretching-and-folding structures combined with an input coming gaussian kernel are able to produce weird things lol.

От Поле за пускане |

So can we use this as a kernel? The r parameter is the kernel's chaotic parameter than can be limited or expanded.

In my imagination the real science fiction artificial intelligence need to use some kind of chaotic parameter, stepping from the deterministic to the non deterministic.

The question is:

Can we use kernels with chaotic behavior to model chaotic systems?

I think yes too, if that is true that is another kernel trick.

]]>The idea is that we as human we are limited in our perception abilities, when we are presented with several stimuli. It is proven that when you drive and talk on the phone your cognition miss several things.

However you can see for your self the effect. You can make a very simple experiment.

And you will see for your self how the yellow dots disapear when you are looking at the dots.

So don't talk too much while driving and that may save your life.

Well how that relates to the trading world. Well imagine you are scalping and yiou are watching more than 4 technical indicators. Can you make it?,

Maybe you will enter into motion induced blindless, and you will miss something :).

Some epxerienced traders call it paralysis form analysis, but this has a larger approach.

What I think about the indicatros of any kind is that they are just preprocessing the time series.

]]>

We are still at the experimental stage.

**1. The sample percetrone code with gaussian transfer function**

A percetron code would look like this.

double perceptron()

{

double w1 = MathExp(-x1*x1/2);// This is the Gaussian transfer function

double w2 = MathExp(-x2*x2/2);// Here we can specify the number if the hidden neurones

double w3 = MathExp(-x3*x3/2);

double w4 = MathExp(-x4*x4/2);

double p1 = iCustom(NULL,Symbol(),"PFE",x5,true,5,0,0);// This is the input

double p2 = iCustom(NULL,Symbol(),"PFE",x6,true,5,0,0);

double p3 = iCustom(NULL,Symbol(),"PFE",x7,true,5,0,0);

double p4 = iCustom(NULL,Symbol(),"PFE",x8,true,5,0,0);

return(w1*w2*w3*w4* p1 + w1*w2*w3*w4* p2 + w1*w2*w3*w4*p3 + w1*w2*w3*w4* p4);

}

// From this line depends the whole architecture of the percetrone. We multiply the input by the value

//coming from the transfer function, in our example it is between 0 and 1.

So this is a sample code. Here we do not devide the output because we are interested if the value is above or below 0.

It is also important that the inputs are normalized and scaled properly. The technical indicator PFE is very appropriate because it is properly scaled and normalized varying frome -1 to +1.

You can see a practical example of the scaling and normalization in the neural indicator code by Jaguar.

Available for registered users.

The use of genetic optimizer limits the number of hidden neurones we can practically use. In the Neuroshell Trader as a commercial software a simmilar application limits itself in the neural net architecture only to two hidden neurones (). And that is considered as a good neural net implementation practice. (Neural indicators for Neuroshell are available as additional addon, you can refer to the help files).

** 2. The transfer function **

As it was previously stated we can choose different transfer functions in this part of the code.

The gaussian transfer function is:

double w1 = MathExp(-x1*x1/2);

x1 means in practice that we are looking for the best avalues of x1 using the genetic optimizer.

the value of x would be from -3 tp +3. In practice I can suggest a step size of 0.01.

От 08 ноември 2011 |

**3. Towards a more sophisticated kernel function.**

Yesterday I watched a very impressive video about the possibilities of Metatrader 5 for visualisation of the 3 D visualisation space.

So then came into my mind the idea, as we are limited with the genetic optimizer by the number of the hidden neurones we can use (of course you can use many hidden neurones but you will otpimize forever).

So why not to use just one, or two hidden neurones but we will have two parameters in the transfer function, not just one.

So the code will look like this:

double w1 = MathExp(-x1*x1-y1*y1);

Here we optimize two parameters x1 and x2.

On the first shot you can see what you will get if you do not use a genetic optimizer.

От 23 септември 2011 |

Here on the second shot is what you will practically get using a genetic optimizer searching for the best values of x and y.

For the variable x the start from -3, the step is 0.01 and the stop is 3

For the variable y the start from -3, the step is 0.01 and the stop is 3

**So the idea is that we just one hidden neurone using a genetic optimization with two variables we could achieve 3 dimensional hypersphere with just one radial unit.**

От 23 септември 2011 |