Experienced traders are well aware of the fact that most time-consuming things in trading are not opening and tracking positions but selecting symbols and looking for entry points.
Of course, these are not huge issues if you work only with 1-2 symbols. But if your trading toolkit consists of hundreds of stocks and dozens of Forex symbols, it may take several hours only to find suitable entry points.
In this article, we will develop an EA simplifying the search for stocks. The EA is to be helpful in three ways:
it is to pre-filter stocks providing us with a list of the ones that meet our conditions;
is should simplify navigation through the generated stock list;
it is to display additional data necessary to make a decision.
Signals based on real accounts are available only by paid Subscription; signals based on demo accounts can be received only by free Subscription.
Signals based on cent accounts cannot have paid Subscription. Such Signals are available only in the MetaTrader terminal for free. A cent account is defined automatically. In case a Signal is defined incorrectly, the service Administration may assign this property to the Signal manually.
However, in practical terms, using Kohonen networks just for algorithmic trading has been confined with only one approach, namely the same visual analysis of topology maps built for the EA optimization results. In this case, one's value judgment, or rather one's vision and ability to draw reasonable conclusions from a picture turns out to be, perhaps, the crucial factor, sidelining the network properties regarding representing data in terms of nuts-and-bolts matters.
In other words, the features of neural network algorithms were not used to the full, i.e., they were used without automatically extracting knowledge or supporting decision making with specific recommendations. In this paper, we consider the problem of defining the optimal sets of robots' parameters in a more formalized manner. Moreover, we are going to apply Kohonen network to forecasting economic ranges. However, before proceeding to these applied problems, we should revise the existing source codes, get something fixed, and make some improvements.
It is highly recommended to read the above articles first, if you are not familiar with the terms such as 'network', 'layer', 'neuron' ('node'), 'link', 'weight', 'learning rate', 'learning range', and other notions related to Kohonen networks. Then we will have to saturate ourselves in this matter, so re-teaching the basic notions would lengthen this publication significantly.
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Developing the symbol selection and navigation utility in MQL5 and MQL4
Experienced traders are well aware of the fact that most time-consuming things in trading are not opening and tracking positions but selecting symbols and looking for entry points.
Of course, these are not huge issues if you work only with 1-2 symbols. But if your trading toolkit consists of hundreds of stocks and dozens of Forex symbols, it may take several hours only to find suitable entry points.
In this article, we will develop an EA simplifying the search for stocks. The EA is to be helpful in three ways:
Hi
Iam a beginner want to follow cut and paste strategy. How do l start . I need step by step approach
please
108344
Hi
Iam a beginner want to follow cut and paste strategy. How do l start . I need step by step approach
please
Where Do I start from?
https://www.mql5.com/en/forum/212020
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Forum on trading, automated trading systems and testing trading strategies
How to Start with Metatrader 5
Sergey Golubev, 2013.09.20 08:21
Summaries :
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108344
Forum on trading, automated trading systems and testing trading strategies
Some question about Signals
Sergey Golubev, 2016.12.30 20:14
Just some information about the Signal Service:
This is the information about where to start to.
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From the rules -
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The subject of Kohonen neural networks was approached to in some articles on the mql5.com website, such as Using Self-Organizing Feature Maps (Kohonen Maps) in MetaTrader 5 and Self-Organizing Feature Maps (Kohonen Maps) - Revisiting the Subject. They introduced readers to the general principles of building neural networks of this type and visually analyzing the economic numbers of markets using such maps.
However, in practical terms, using Kohonen networks just for algorithmic trading has been confined with only one approach, namely the same visual analysis of topology maps built for the EA optimization results. In this case, one's value judgment, or rather one's vision and ability to draw reasonable conclusions from a picture turns out to be, perhaps, the crucial factor, sidelining the network properties regarding representing data in terms of nuts-and-bolts matters.
In other words, the features of neural network algorithms were not used to the full, i.e., they were used without automatically extracting knowledge or supporting decision making with specific recommendations. In this paper, we consider the problem of defining the optimal sets of robots' parameters in a more formalized manner. Moreover, we are going to apply Kohonen network to forecasting economic ranges. However, before proceeding to these applied problems, we should revise the existing source codes, get something fixed, and make some improvements.
It is highly recommended to read the above articles first, if you are not familiar with the terms such as 'network', 'layer', 'neuron' ('node'), 'link', 'weight', 'learning rate', 'learning range', and other notions related to Kohonen networks. Then we will have to saturate ourselves in this matter, so re-teaching the basic notions would lengthen this publication significantly.