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Traders often wonder how to improve a trading system or create a new one through machine learning. Despite the abundance of publications, a simple and intuitive method is yet to be found for creating models that cannot be analytically estimated without resorting to computer-aided calculations. Fuzzy logic is a window to the world of machine learning. Combined with genetic algorithms, it is able to expand the capabilities of creating self-learning or easily optimizable trading systems. At the same time, fuzzy logic is intuitive, as it encapsulates crisp numerical information in fuzzy (blurred) terms, just like a person does in the process of thinking.

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This article is mainly aimed at beginners who cannot wait to trade on a demo account and test their strategies.

Naturally, you might wonder: if there is a history chart where any strategy can be tested quickly and effectively, why are such difficulties needed? In practice, however, this does not always work: it often happens that a strategy with splendid backtest results works very poorly in the "live" market for some reason. At any rate, it is better to learn trading in systems more or less close to the reality. For instance, market emulators are quite sufficient (such programs can be bought on the Internet).

In this article, I want to discuss my own implementation of such a system in MetaTrader 5. I have written the "Mini Market Emulator" indicator with a limited functionality compared to the full version of the terminal. It is designed for theoretical verification of strategies.

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Topics devoted to the triangular arbitrage appear on forums with unfailing regularity. So, what is it exactly?

More common are the cases when we are able to buy one side cheaper but are not able to sell it with a profit right now. Then we wait for this imbalance to disappear. Being in a trade is safe for us, since our position is almost zero, meaning we are out of the market. Although, note the word "almost" here. For a perfect leveling of trade volumes, we need a precision that is not available to us. Trade volumes are most often rounded to two decimal places which is too rough for our strategy.

Now that we have considered the theory, it is time to write an EA. The EA is developed in a procedural style, so it is understandable for both novice programmers and those who for some reason do not like OOP.

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Table of contents

• Introduction
• Types of moving averages
  
  • Moving Average technical indicator
  • Exponential moving average (EMA) - based technical indicators
    
  
  
• Comparison of various types of moving averages
  
  • Trading strategy specification
  • Creation of Expert Adviser
  • Expert Adviser testing and performance
  
  
• Conclusion

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Table of Contents

• Introduction
• Criticism of common statistics for trading system evaluation
• The behavior of common in testing of trading systems
• Requirements for the testing criterion of the trading system
• Linear Regression
• Correlation
• Coefficient of determination R^2
• The arcsine theorem and its contribution to the estimation of linear regression
• Collecting the strategy equity
• Calculating the coefficient of determination R^2 using AlgLib
• Using R-squared in practice
• Advantages and limitations of use
• Conclusion

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Who might benefit from the article:

cryptocurrency exchange traders;
investors familiar with MetaTrader 5 and portfolio investments;
freelance programmers who can now execute the customers' orders related to cryptocurrency trading in a simpler (and cheaper) way;
everyone who follows the new MetaTrader 5 and MQL5 language features.
First, we need to choose a cryptocurrency exchange providing a web API.

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Just like in life in the market everything is in continuous changing. Something which did perfrectly yesterday today appears not to show interesting results. However, there remain some fundamental strategies which experience some minor corrections over time, but do not change their ideological basis. One of them is “DiNapoli levels”, a strategy named after its founder. It represents stripped down realization of Fibonacci retracements.

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MetaTrader 5 has many useful features that a trader would need, regardless of their trading style, including a possible access to a live news feed. It provides traders with invaluable context that may have some effect on the markets. The only limitation is the scope of the news provided. I believe, traders could benefit from having access to a more flexible news feed that allows the ability to not only choose the kind of news but also its source.

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In this article, we continue programming of trading strategies described in a section of the book by L. Raschke and L. Connors Street Smarts: High Probability Short-Term Trading Strategies, devoted to testing of range limits by price. The last of full-fledged TS in the section is Momentum Pinball, which operates the pattern consisting of two daily bars. By the first bar, trade direction on the second day is defined, and price movement in the beginning of the second bar should specify certain trade levels for entries and exits from the market.

The purpose of this article is to demonstrate to the programmers who have already mastered MQL5, one of the variants for realizing Momentum Pinball TS, in which simplified methods of object-oriented programming will be applied. From the full-fledged OOP, the code will differ by the absence of classes - they will be replaced by structures. As opposed to classes, design in the code and application of objects of this type differs minimally from the procedural programming familiar to most starting coders. On the other hand, features being provided by structures are more than enough to resolve such tasks.


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