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##### پیش بینی فارکس از طریق هوش مصنوعی (شبکه های عصبی) با الگوریتم پیشرفته برای اولین بار در جهان فارکس

بیش از 200 % درصد سود در سال

##### Forex forecasting by artificial intelligence (neural networks), using a special algorithm,for the first time in the Forex world.

ABM Trader’s signals prediction method is a novel and different one in contrast to other common and conventional signal predictors in the Forex world. You can receive ABM Trader predictions once a month (or a week) as a general forecast for upcoming events. In the diagram of these predicted signals you will observe predicted flow until next period and then, based on observation of a real flow which is happening, one can make the best decision for a successful trade.

We will ensure you that the ABM Trader signals are the most low-risk and the best possible automatic forecasts in the Forex market.

In our work we use artificial intelligence methods (mostly based on artificial neural networks) which provides us with the closest and the most similar diagram to the real market flow.

Handling a position (transaction) properly, needs to be aware of the general market flow which is going to happen in the upcoming future by means of a reliable forecast.

The flow in the prediction diagram should be considered as a general market flow which declares that you must avoid small (slight) market movements, and buy or sell according to the general market flow. Maximum and minimum points in this forecast are determined by us.

In a lot you will enter the market with 1/20 capital which means if you have 10000$ to invest for example, you can enter the market with 0.5 lot. With this amount of turnover you will get over 40% in a month.

**More details:**

Put a series of observations in a tandem over time, one has the so called *Time Series* which can be expressed as vectorial or numerical array. In the general case a time series can represent the properties like non-linearity, chaotic, Non-stationary and alternative such as seasonality. In addition, the observations can be polluted by noise. Since many natural and industrial processes have time-varying nature , black-box models can be used for modeling time series which their prediction for engineers and scientists are favorite subjects to study. In the general case you can regard the prediction topic by making a black-box model (input-output) for identifying systems and then you can predict time series by using estimation function methods. In this case the inputs of model are past and present patterns of time series and its outputs are future values of time series.

In the general case consecutive samples of a time series are dependent to values of past samples , so , we can use of past values to predict future values of samples. This fact can be expressed in the following equation :

Where x(t) the time series samples at the moment f , t is also a function between time series value at the moment t+1

And time series past values and (N1) is also the number of time series values witch are suitable to predict a step ahead x(t+1)

Prediction issues can be assumed as identification nonlinear dynamic systems

Issue and we can utilize algorithms and Criterion used in this field .

a time series nature , being dependent or independent observation is, and therefore , the sequence of observations in that is important . so , statistical methods and techniques , no longer apply and the different method are needed. The statistical methodology configuration refers to Time Series Analysis .

To study the time series , there are different objectives which contain understanding and expression of the production mechanism , predicting future value and control a system .

Mechanism description

When a mechanism is shown by a time series , the first step in analyzing is drawing data and thereupon receiving a series of major series properties.

For example, the regular seasonal changes can be seen in Figure 2.

For some of the time series that their variation is associated with such clear features and a sample model that is only trying to describe these trends and

Seasonal variations.

For other series, more sophisticated techniques are required to provide more accurate analysis.

A time series with seasonal variation : fig 2

Mechanism Analysis

When observations are received in two or more variables we analyze a series of time according to other series variations . this leads us to a deeper understanding of producer data mechanism. For example , this can be interesting if you know how the sea surface affected by the temperature and pressure. And or selling a product or how it is affected by price and economic conditions.

prediction

with a time series may be that we want to predict its future values .that is very considered as a very important matter in economic and industrial prediction. The issue raised here is.

Use word “prediction ” or ” forecasting ” is interchangeably . word “prediction” is used to describe non- objective method and to do its systematic process while word “forecasting” is used for objective methods and look to the future.

Prediction in many application is closely related to the control .

For example we can predict whether a productive process is running or stopped, and if so, corrective action should be done.

Multilayer neural networks

Overview of a neural network is shown in below Fig.

Fig 5:Overview of a neural network

The general structure of a neural network multilayer perceptron neural network is the most used type of neural network structures . they use comprehensive basic functions that is toward nonlinear parameter .

Multilayer neural network for a wide range of issues can be used with different specifications , that it may not show the efficient performance of their own about most of them. One of the most important feature of this network is auto tuning it in the different directions in nonlinear that is available in the process that in fact , most especial benefit is about its issue with high dimensions .

One of the most important activities of ABM trader group is developing and providing relating accurate prediction for the currency trading market that are used of intelligent techniques of neural network and signal s statistical studies .

Currently , prediction of H1 minutes signals Euro to Dollar , (EURUSD, H1) is offered a week prediction

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