Metatrader 5 / Metatrader 4 for MQL5 / MQL4 articles preview

In our previous article, we explored creating an expert advisor (EA) using the Trend Constraint V1.09 indicator, complemented by the manually executed Trend Constraint R-R script for placing risk and reward rectangles. While this setup provided insightful trading signals and enhanced visualization, it required manual intervention that could be streamlined. With the fast-paced nature of trading environments, the need for a more efficient solution becomes apparent. Many traders seek integrated systems that function autonomously, reducing the need for constant supervision and manual execution.


This article takes the next step in our series by guiding you through the development of an independent expert advisor (EA) that not only incorporates the trend analysis capabilities of Trend Constraint V1.09, but also integrates risk-reward functionalities directly into the EA. Our goal is to empower traders with an all-in-one solution using MQL5 on the MetaTrader 5 platform, offering enhanced automation and seamless operation to keep pace with market demands.

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In the preceding article of this series, we delved into the process of sending chart snapshots with captions from MetaTrader 5 to Telegram. Our approach, while effective, was rather straightforward and somewhat inflexible. We chained together the components necessary to capture a screenshot, convert or encode it into a message-friendly form, and send it along to Telegram. Although this setup worked, it resulted in a fair bit of code that was repetitive and not all that manageable. So, what can we do to improve this implementation? Move to a more modular codebase! This is the first step toward both a more flexible and a more maintainable system.

In this fourth part of our series, we will focus on enhancing the reusability of our program through code modularization. We will undertake a detailed discussion on the principles of code modularization and, more specifically, how these principles apply to our project. Following that, we will present step-by-step instructions for reorganizing our existing mql5 script into separate, well-defined functions. In the end, you will have the choice of using the old, monolithic program or a new, modular Expert Advisor (EA) with the same output.

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This project aims to familiarize you with the useful applications of chart objects in MQL5. This practical approach will teach you how to manage trades, improve visual analysis, and display important trading data right on your charts by efficiently integrating and modifying chart objects within your Expert Advisor (EA). To help you better understand how to use these tools for trading decisions and performance tracking, this project will walk you through the process of creating, modifying, and managing various chart objects.

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Forecasting plays an important role in time series analysis. Deep models have brought significant improvement in this area. In addition to successfully predicting future values, they also extract abstract representations that can be applied to other tasks such as classification and anomaly detection.
The Transformer architecture, which originated in the field of natural language processing (NLP), demonstrated its advantages in computer vision (CV) and is successfully applied in time series analysis. Its Self-Attention mechanism, which can automatically identify relationships between elements of a time series, has become the basis for creating effective forecasting models.

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This article dives into making our MetaQuotes Language 5 (MQL5) Expert Advisor more responsive and interactive for users through Telegram. In the fifth installment of this series, we laid the groundwork for our bot by implementing the ability to respond to commands and messages from Telegram and by creating custom keyboard buttons. In this segment, we’re upping the interactivity of our bot by integrating inline buttons that trigger various actions and respond dynamically to user inputs.

The article is organized to address a few key components. First, we will introduce inline buttons in Telegram bots, including what they are, their usefulness, and the benefits they provide over other methods of creating a bot interface. Then, we will transition to discussing how to use these inline buttons in MQL5, so that they can be part of our Expert Advisor's user interface.

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Probably all known neural network architectures have been studied in terms of their ability to solve time series forecasting problems, including recurrent, convolutional and graph models. The most notable results are demonstrated by models based on the Transformer architecture. Several such algorithms were also presented in this series of articles. However, recent research has shown that Transformer-based architectures might be less powerful than expected. On some time series forecasting benchmarks, simple linear models can show comparable or even better performance. Unfortunately, however, such linear models have shortcomings because they are not suitable for modeling nonlinear relationships between a sequence of time series and time-independent covariates.

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We stick to this format for this article, where we go over pattern by pattern for the parabolic SAR before concluding with a test run that combines multiple patterns as we did in the last articles. The parabolic SAR is computed almost independently with each new bar, since some of the parameters that go into its formula need to be adjusted, as we will see below. This trait, though, makes it very sensitive to price changes and trends in general, which in turn makes the case for its use within a custom signal class. For this article, we are going to explore 10 separate patterns of this indicator by testing each independently and then concluding, as in the recent articles, with a test run that combines a selection of these patterns.

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In this article, we will build on the progress made in the previous part (Part 6), where we integrated responsive inline buttons to enhance bot interaction. Now, our focus shifts to automating the addition of indicators on MetaTrader 5 charts using commands sent from Telegram. We will create a system where the Expert Advisor captures user-defined indicator parameters via Telegram, parses the data, and applies the specified indicators to trading charts in real-time.

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From the beginning of this article series, our emphasis has been on aligning our experts with the prevailing sentiment of the daily (D1) candles. The shape of the daily candle has served as the primary guiding feature. However, we needed to scale down to lower timeframes to identify entry levels within the D1 market. For example, at the M1 timeframe, we wanted the market to reach extreme levels on the Relative Strength Index (RSI) to signal potential trades for the Expert Advisor. At this early stage, we did not introduce too many strategies to keep the content easy to understand for beginners.

However, there is a vast collection of strategies to study and integrate into the algorithm of our Trend Constraint Expert Advisor. Today, we will take a close look at some well-known strategies developed by influential figures in trading, whom I refer to as the "Market Fathers."

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The ADX indicator is an oscillator that is meant to measure how strong a given trend in the market is for the security to which it is attached. This article thus, like some that were preceding it, examines the various patterns exhibited by this indicator in a custom signal class file, for wizard assembled Expert Advisors. We review one pattern at a time as we have in the previous articles covering RSI, Bollinger Bands, and the RSI and then conclude with a test that includes all patterns to get a sense of which has the highest weighting.

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