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Every novice trader dreams of steadily making a lot of money in the financial markets. How to do that without excessive risk and start-up budget? Where can a trader find the necessary information and get help from more experienced colleagues? MQL5 services provide unique profit opportunities for developers and traders from all over the globe, while beginners are able to find their own way in the world of algorithmic trading. These services are available to millions of traders directly from the MetaTrader trading terminal.

Market sellers won't tell you about their earnings, but TOP 10 Sellers make between $15,000 and $100,000 per month.

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Spreadsheets are a fairly old invention. Modern programs of this type have tremendous power and allow you to visually analyze data presented in tabular form. The analysis can be done from different angles and is performed quite quickly. It includes graphs, summary tables, what-if analysis, conditional cell formatting and much more.

I suggest testing some of this power to analyze custom strategies.

Personally, I use LibreOffice Calc because it is free and it works wherever I work :-) However, the same approach works for other spreadsheets: Microsoft Excel, Google Sheets, etc. Currently, they all allow converting into each other and feature the same principles of constructing equations.

So, I assume, you have some kind of spreadsheet program. You also have data in a text file format (*.txt or *.csv) you want to analyze. The article briefly describes how to import such files. I will use history from MetaTrader terminal, however, any other data will do, like Dukascopy or Finam. Obviously, you should have a strategy to configure signals. This is all that is required to apply article propositions in trading.

I hope, the article will be useful to different categories of traders, so I will try to write it so that it is understandable even for people who have never seen programs of this type before. At the same time, it will cover a range of issues even some experienced traders are not familiar with.

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Contents

• Introduction
• 1. Attention Mechanisms
• 2. Self-Attention Algorithm
• 3. Implementation
  
  • 3.1. Upgrading the Convolutional Layer
  • 3.2. Self-Attention Block Class
  • 3.3. Self-Attention Feed-Forward
  • 3.4. Self-Attention Feed-Backward
  • 3.5. Changes in the Neural Network Base Classes
  
  
• 4. Testing
• Conclusions
• References
• Programs Used in the Article

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1. Neural networks made easy - MT5
2. Neural networks made easy (Part 2): Network training and testing - MT5
3. Neural networks made easy (Part 3): Convolutional networks - MT5
4. Neural networks made easy (Part 4): Recurrent networks - MT5
5. Neural networks made easy (Part 5): Multithreaded calculations in OpenCL - MT5
6. Neural networks made easy (Part 6): Experimenting with the neural network learning rate - MT5
7. Neural networks made easy (Part 7): Adaptive optimization methods - MT5
8. Neural networks made easy (Part 8): Attention mechanisms - MT5
9. Neural networks made easy (Part 9): Documenting the work - MT5
10. Neural networks made easy (Part 10): Multi-Head Attention - MT5

Any trading algorithm is generally a tool which may bring profit to an experienced trader or instantly destroy a deposit of an inexperienced one. The issue of creating a profitable and reliable algorithm is that we cannot understand what needs to be done in order to earn money and what methods are used by "successful traders". While HFT, arbitrage, option strategies and calendar spread-based systems boast a solid theoretical basis clearly stating what needs to be done to make profit, the algorithms based on price analysis and fundamental data are much more ambiguous. This area has no full-fledged theoretical basis that would describe pricing making it extremely difficult to create a stable trading algorithm. Trading turns into art here, while science helps systematizing everything.

But is it possible to create a fully automated trading algorithm based only on the analysis of price changes working on any trading instrument without optimization and with no need to manually adjust parameters for each trading instrument separately? Is there an algorithm you can simply apply to a necessary trading instrument chart so that it immediately defines profitable parameters for it?

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Contents

• Introduction
• 1. The basic principles of creating documentation
• 2. Selecting tools
• 3. Documenting in the code
• 4. Preparation in the code source file
• 5. Generating documentation
• Conclusions
• References
• Programs Used in the Article

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Two other articles devoted to seasonal pattern search have already been published (1, 2). I was wondering how machine learning algorithms can cope with the pattern search task. Trading systems in the above-mentioned articles were built on the basis of statistical analysis. The human factor can be eliminated now by simply instructing the model to trade at a certain hour of a certain day of the week. Pattern search can be provided by a separate algorithm.

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Before reading this article, I recommend that you study the first article "Developing a self-adapting algorithm (Part I): Finding a basic pattern". This is not necessary, since the main point will still be clear, but reading will be more interesting.

In the previous article, I detected a simple pattern and developed a very simple algorithm that exploits it. But the algorithm has no flexible structure, and it makes no sense to expect any outstanding results from it.
We need to greatly improve it so that it becomes more flexible and adjusts its operation parameters depending on the market situation so that it is possible to achieve better results and stability.

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Table of Contents

• Introduction
• 1. Multi-Head Attention
• 2. A Bit of Math
• 3. Positional Encoding
• 4. Implementation
  
  • 4.1. Eliminating Keys Tensor
  • 4.2. Multi-Head Attention Class
  • 4.3. Feed-forward
  • 4.4. Feed-backward
  • 4.5. Changes in the Neural Network Base Classes
  
  
• 5. Testing
• Conclusion
• References
• Programs Used in the Article

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An essential problem in preparing data to training neural networks designed for trading, is related the preparation of the necessary input data. For example, consider the case when we use a dozen indicators. These indicators may represent a set of several informative charts. If we calculate these indicators to a certain depth, then as a result we will get up to a hundred entries, and in some cases even more. Can we make neural network training easier by using computer vision? To solve this problem, let us use convolutional neural networks, which are often utilized to solve classification and recognition problems.

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Before reading this article, I recommend that you study the second article in the series "Developing a self-adapting algorithm (Part II): Improving efficiency". The methodology applied in the current article differs significantly from everything discussed earlier, but it will be useful to read the previous articles to understand the topic.

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