(For more on moving averages, see: Simple Moving Averages Make Trends Stand Out.) Algo-trading provides the following benefits: Trades executed at the best possible prices.

### Twitter's Feed for #Trading algorithms work

### Video Trading algorithms work

Hello, I will explain how SVM algorithm works.
This video will explain the support vector

machine for linearly separable binary sets
Suppose we have this two features, x1 and

x2 here and we want to classify all this elements
You can see that we have the class square

and the class rectangle
So the goal of the SVM is to design a hyperplane,

here we define this green line as the hyperplane,
that classifies all the training vectors in

two classes
Here we show two different hyperplanes which

can classify correctly all the instances in
this feature set

But the best choice will be the hyperplane
that leaves the maximum margin from both classes

The margin is this distance between the hyperplane
and the closest elements from this hyperplane

We have the case of the red hyperplane we
have this distance, so this is the margin,

which we represent by z1
And in the case of the green hyperplane we

have the margin that we call z2
We can clearly see that the value of z2 is

greater than z1
So the margin is higher in the case of the

green hyperplane, so in this case the best
choice will be the green hyperplane

Suppose we have this hyperplane, this hyperplane
is defined by one equation, we can state this

equation as this one
We have a vector of weights plus omega 0 and

this equation will deliver values greater
than 1 for all the input vectors which belongs

to the class 1, in this case the circles
And also, we scale this hyperplane so that

it will deliver values smaller than -1 for
all values which belongs to class number 2,

the rectangles
We can say that this

distance to the closest

elements will be at least 1, the modulus is
1

From the geometry we know that the distance
between a point and a hyperplane is computed

by this equation
So the total margin which is composed by this

distance will be computed by this equation
And the aim is that minimizing this term will

maximize the separability
When we minimize this weight vector we will

have the biggest margin here that will split
this two classes

To minimize this weight vector is a nonlinear
optimization task, which can be solved by

this conditions (KKT), which uses Langrange
multipliers

The main equations state that the value of
omega will be the solution of this sum here

And we also have this other rule. So when
we solve these equations, trying to minimize

this omega vector, we will maximize the margin
between the two classes which will maximize

the separability the two classes
Here we show a simple example

Suppose we have these 2 features, x1 and x2,
and we have these 3 values

We want to design, or to find the best hyperplane
that will divide this 2 classes

So we know that we can see clearly from this
graph that the best division line will be

a parallel line to the line that connects
these 2 values here

So we can define this weight vector, which
is this point minus this other point. So we

have the constant a and 2 times this constant
a

Now we can solve this weight vector and create
the hyperplane equations considering this

weight vector
We must discover the values of this a here

Since we have this weight vector omega here,
we can substitute the values of this point

and also using

### Read more

Best binary option burks falls...

Binaryoptionsasia com...

Forex magnates bitcoin...

### Partner's Publications

### Other News

Discount binary options indicator oak island...

Mega options virtual trade...

Price binary options trading brighton...

Order binary options signals grand saline...

Purchase binary options signals bundoran...