Articles on strategy testing in MQL5

The article considers applying the separate optimization method during various market conditions. Separate optimization means defining trading system's optimal parameters by optimizing for an uptrend and downtrend separately. To reduce the effect of false signals and improve profitability, the systems are made flexible, meaning they have some specific set of settings or input data, which is justified because the market behavior is constantly changing.

The article dwells on the development of an application for selecting the best optimization passes using several possible options. The application is able to sort out the optimization results by a variety of factors. Optimization passes are always written to a database, therefore you can always select new robot parameters without re-optimization. Besides, you are able to see all optimization passes on a single chart, calculate parametric VaR ratios and build the graph of the normal distribution of passes and trading results of a certain ratio set. Besides, the graphs of some calculated ratios are built dynamically beginning with the optimization start (or from a selected date to another selected date).

The article provides an overview of the terminal's capabilities for creating and working with custom symbols, offers options for simulating a trading history using custom symbols, trend and various chart patterns.

Efficiency of any trading robot depends on the correct selection of its parameters (optimization). However, parameters that are considered optimal for one time interval may not retain their effectiveness in another period of trading history. Besides, EAs showing profit during tests turn out to be loss-making in real time. The issue of continuous optimization comes to the fore here. When facing plenty of routine work, humans always look for ways to automate it. In this article, I propose a non-standard approach to solving this issue.

This article describes the implementation of a self-optimization mechanism under MetaTrader 5.

The article deals with the algorithm of developing stock indicators based on real volumes using the CopyTicks() and CopyTicksRange() functions. Some subtle aspects of developing such indicators, as well as their operation in real time and in the strategy tester are also described.

In the article, we continue to develop the MQL application for working with optimization results. This time, we will show how to form the table of the best results after optimizing the parameters by specifying another criterion via the graphical interface.

Before launching a robot on a trading account, we usually test and optimize it on quotes history. However, a reasonable question arises: how can past results help us in the future? The article describes applying the Monte Carlo method to construct custom criteria for trading strategy optimization. In addition, the EA stability criteria are considered.

This article presents a visual strategy builder. It is shown how any user can create trading robots and utilities without programming. Created Expert Advisors are fully functional and can be tested in the strategy tester, optimized in the cloud or executed live on real time charts.

This is a continuation of the idea of processing and analysis of optimization results. This time, our purpose is to select the 100 best optimization results and display them in a GUI table. The user will be able to select a row in the optimization results table and receive a multi-symbol balance and drawdown graph on separate charts.

The article provides an example of an MQL application with its graphical interface featuring multi-symbol balance and deposit drawdown graphs based on the last test results.

The article implements an MQL application with a graphical interface for extended visualization of the optimization process. The graphical interface applies the last version of EasyAndFast library. Many users may ask why they need graphical interfaces in MQL applications. This article demonstrates one of multiple cases where they can be useful for traders.

The Strategy Tester in the MetaTrader 5 trading platform provides only two optimization options: complete search of parameters and genetic algorithm. This article proposes a new method for optimizing trading strategies — Simulated annealing. The method's algorithm, its implementation and integration into any Expert Advisor are considered. The developed algorithm is tested on the Moving Average EA.

The article describes the way to create a custom strategy tester and a custom analyzer of the optimization passes. After reading it, you will understand how the math calculations mode and the mechanism of so-called frames work, how to prepare and load custom data for calculations and use effective algorithms for their compression. This article will also be interesting to those interested in ways of storing custom information within an expert.

Different situations happen in trader’s life. Often, the history of successful trades allows us to restore a strategy, while looking at a loss history we try to develop and improve it. In both cases, we compare trades with known indicators. This article suggests methods of batch comparison of trades with a number of indicators.

Mini Market Emulator is an indicator designed for partial emulation of work in the terminal. Presumably, it can be used to test "manual" strategies of market analysis and trading.

In this article, we consider yet another custom trading strategy optimization criterion based on the balance graph analysis. The linear regression is calculated using the function from the ALGLIB library.

The article deals with a simple approach to creating an automated trading system based on the chart linear markup and offers a ready-made Expert Advisor using the standard properties of the MetaTrader 4 and 5 objects and supporting the main trading operations.

Creating custom symbols pushes the boundaries in the development of trading systems and financial market analysis. Now traders are able to plot charts and test trading strategies on an unlimited number of financial instruments.

The article provides the analysis of the following patterns: Flag, Pennant, Wedge, Rectangle, Contracting Triangle, Expanding Triangle. In addition to analyzing their similarities and differences, we will create indicators for detecting these patterns on the chart, as well as a tester indicator for the fast evaluation of their effectiveness.

The article deals with the approaches enabling accurate simulation of walk forward optimization using the built-in tester and auxiliary libraries implemented in MQL.

In this article, we develop and tests several strategies based on the Donchian channel using various indicator filters. We also perform a comparative analysis of their operation.

The article highlights several methods for trend identification aiming to determine the trend duration relative to the flat market. In theory, the trend to flat rate is considered to be 30% to 70%. This is what we'll be checking.

The article provides a brief overview of ten trend following strategies, as well as their testing results and comparative analysis. Based on the obtained results, we draw a general conclusion about the appropriateness, advantages and disadvantages of trend following trading.

The MetaTrader 5 platform allows developing and testing trading robots that simultaneously trade multiple financial instruments. The built-in Strategy Tester automatically downloads required tick history from the broker's server taking into account contract specifications, so the developer does not need to do anything manually. This makes it possible to easily and reliably reproduce trading environment conditions, including even millisecond intervals between the arrival of ticks on different symbols. In this article we will demonstrate the development and testing of a spread strategy on two Moscow Exchange futures.

Analysis of the trade history and plotting distribution charts of trading results in HTML depending on position entry time. The charts are displayed in three sections - by hours, by days of the week and by months.

Scalping automatic systems are rightfully regarded the pinnacle of algorithmic trading, but at the same time their code is the most difficult to write. In this article we will show how to build strategies based on analysis of incoming ticks using the built-in debugging tools and visual testing. Developing rules for entry and exit often require years of manual trading. But with the help of MetaTrader 5, you can quickly test any such strategy on real history.

Before the first single test, every trader faces the same question — "Which of the four modes to use?" Each of the provided modes has its advantages and features, so we will do it the easy way - run all four modes at once with a single button! The article shows how to use the Win API and a little magic to see all four testing chart at the same time.

The article provides the results of testing a simple trading strategy in three modes: "1 minute OHLC", "Every tick" and "Every tick based on real ticks" using actual historical data.

Before any product is published in the Market, it must undergo compulsory preliminary checks in order to ensure a uniform quality standard. This article considers the most frequent errors made by developers in their technical indicators and trading robots. An also shows how to self-test a product before sending it to the Market.

How to make the testing process more visual? The answer is simple: you need to use one or more indicators in the Strategy Tester, including a tick indicator, an indicator of balance and equity, an indicator of drawdown and deposit load. This solution will help you visually track the nature of ticks, balance and equity changes, as well as drawdown and deposit load.

We continue the series of articles on MQL5 programming. This time we will see how to get results of each optimization pass right during the Expert Advisor parameter optimization. The implementation will be done so as to ensure that if the conditions specified in the external parameters are met, the corresponding pass values will be written to a file. In addition to test values, we will also save the parameters that brought about such results.

Whatever trading strategy you use, there will always be a question of what parameters to choose to ensure future profits. This article gives an example of an Expert Advisor with a possibility to optimize multiple symbol parameters at the same time. This method is intended to reduce the effect of overfitting parameters and handle situations where data from a single symbol are not enough for the study.

When communicating in various forums, I often used examples of my test results displayed as screenshots of Microsoft Excel charts. I have many times been asked to explain how such charts can be created. Finally, I now have some time to explain it all in this article.

In this article, we will create a pattern that uses a single set of parameters for optimization of a trading system, while allowing for unlimited number of parameters. The list of symbols will be created in a standard text file (*.txt). Input parameters for each symbol will also be stored in files. This way we will be able to circumvent the restriction of the terminal on the number of input parameters of an Expert Advisor.

This article will describe an implementation of a simple approach suitable for a multi-currency Expert Advisor. This means that you will be able to set up the Expert Advisor for testing/trading under identical conditions but with different parameters for each symbol. As an example, we will create a pattern for two symbols but in such a way so as to be able to add additional symbols, if necessary, by making small changes to the code.

In this article, we will develop a framework for a trading system based on the Triple Screen strategy in MQL5. The Expert Advisor will not be developed from scratch. Instead, we will simply modify the program from the previous article "MQL5 Cookbook: Using Indicators to Set Trading Conditions in Expert Advisors" which already substantially serves our purpose. So the article will also demonstrate how you can easily modify patterns of ready-made programs.

In this article, we will continue to modify the Expert Advisor we have been working on throughout the preceding articles of the MQL5 Cookbook series. This time, the Expert Advisor will be enhanced with indicators whose values will be used to check position opening conditions. To spice it up, we will create a drop-down list in the external parameters to be able to select one out of three trading indicators.

It is time to briefly summarize the information provided in the previous articles on position properties. In this article, we will create a few additional functions to get the properties that can only be obtained after accessing the history of deals. We will also get familiar with data structures that will allow us to access position and symbol properties in a more convenient way.

This article is intended primarily for the programmers who have already learned the language but have not fully mastered the program development yet. It reveals some debugging techniques and presents a combined experience of the author and many other programmers.