Articles on trading system automation in MQL5

The article discusses a popular MVC pattern, as well as the possibilities, pros and cons of its usage in MQL programs. The idea is to split an existing code into three separate components: Model, View and Controller.

In this article, I will create the signal collection class of the MQL5.com Signals service with the functions for managing signals. Besides, I will improve the Depth of Market snapshot object class for displaying the total DOM buy and sell volumes.

In the previous articles, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. In the seventh part, we added tracking StopLimit orders activation and prepared the functionality for tracking other events involving orders and positions. In this article, we will develop the class for tracking order and position modification events.

The project involves using Python for deep learning-based forecasting in financial markets. We will explore the intricacies of testing the model's performance using key metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R2) and we will learn how to wrap everything into an executable. We will also make a ONNX model file with its EA.

Is it possible to predict the behavior of the market for a short upcoming interval of time, based on the recurring tendencies of candle directions, at specific times throughout the day? That is, If such an occurrence is found in the first place. This question has probably arisen in the mind of every trader. The purpose of this article is to attempt to predict the behavior of the market, based on the statistical recurrences of candle directions during specific intervals of time.

Finding rules for a trade system and programming them in an Expert Advisor is a half of the job. Somehow, you need to correct the operation of the Expert Advisor as it accumulates the results of trading. This article describes one of approaches, which allows improving performance of an Expert Advisor through creation of a feedback that measures slope of the balance curve.

In the first part, we started creating a large cross-platform library simplifying the development of programs for MetaTrader 5 and MetaTrader 4 platforms. Further on, we implemented the collection of history orders and deals. Our next step is creating a class for a convenient selection and sorting of orders, deals and positions in collection lists. We are going to implement the base library object called Engine and add collection of market orders and positions to the library.

In this article, we will consider combining the lists of bar objects for each used symbol period into a single symbol timeseries object. Thus, each symbol will have an object storing the lists of all used symbol timeseries periods.

In this article I will try to expand the classic concept of swap trading methods. I will explain why I have come to the conclusion that this concept deserves special attention and is absolutely recommended for study.

The article deals with realization of charts without skipped bars.

Using virtual trading, you can create an adaptive Expert Advisor, which will turn on and off trades at the real market. Combine several strategies in a single Expert Advisor! Your multisystem Expert Advisor will automatically choose a trade strategy, which is the best to trade with at the real market, on the basis of profitability of virtual trades. This kind of approach allows decreasing drawdown and increasing profitability of your work at the market. Experiment and share your results with others! I think many people will be interested to know about your portfolio of strategies.

In this article, I will create two classes (the class of DOM snapshot object and the class of DOM snapshot series object) and test creation of the DOM data series.

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.

The article covers the peculiarities of night flat trading on cross currency pairs. It explains where you can expect profits and why great losses are not unlikely. The article also features an example of the Expert Advisor developed for night trading and talks about the practical application of this strategy.

In the previous articles of this series, we have seen two reinforced learning algorithms. Each of them has its own advantages and disadvantages. As often happens in such cases, next comes the idea to combine both methods into an algorithm, using the best of the two. This would compensate for the shortcomings of each of them. One of such methods will be discussed in this article.

The article considers the methodology for developing trading algorithms, in which a consistent scientific approach is used to analyze possible price patterns and to build trading algorithms based on these patterns. Development ideals are demonstrated using examples.

Today's article shows intermediate MQL5 programmers how they can get more profit from their linear trading systems (Fixed Lot) by easily implementing the so-called technique of exponentiation. This is because the resulting equity curve growth is then geometric, or exponential, taking the form of a parabola. Specifically, we will implement a practical MQL5 variant of the Fixed Fractional position sizing developed by Ralph Vince.

In this article we will check the well-known statement that "The whole day trading depends on how the Asian session is traded".

What is social trading? It is a mutually beneficial cooperation of traders and investors whereby successful traders allow monitoring of their trading and potential investors take the opportunity to monitor their performance and copy trades of those who look more promising.

Efficient extraction and integration of long-term dependencies and short-term features remain an important task in time series analysis. Their proper understanding and integration are necessary to create accurate and reliable predictive models.

In this article, the author continues to familiarize the readers with the Lite_EXPERT2.mqh functions using real Expert Advisor implementation examples. The article deals with the idea of using floating pending orders and pending orders that vary dynamically from deal to deal which are determined based on Average True Range (ATR) indicator values.

The "area method" trading system works based on unusual interpretation of the RSI oscillator readings. The indicator that visualizes the area method, and the Expert Advisor that trades using this system are detailed here. The article is also supplemented with detailed findings of testing the Expert Advisor for various symbols, time frames and values of the area.

Trading Signals service recently introduced in MetaTrader 5 allows traders to copy trading operations of any signals provider. Users can select any signal, subscribe to it and all deals will be copied at their accounts. Signals providers can set their subscription prices and receive a fixed monthly fee from their subscribers.

The article focuses on one-step-ahead forecasting for EURUSD using EViews software and a further evaluation of forecasting results using the programs in EViews. The forecast involves regression models and is evaluated by means of an Expert Advisor developed for MetaTrader 4.