Articles on the MQL5 programming and use of trading robots

In this article, we will provide a simple and easy guide to anyone who needs to create one of the most valuable and helpful tools in trading which is the graphical panel to simplify and ease doing tasks around trading which helps to save time and focus more on your trading process itself without any distractions.

The SMC (Order Block) are key areas where institutional traders initiate significant buying or selling. After a significant price move, fibonacci helps to identify potential retracement from a recent swing high to a swing low to identify optimal trade entry.

In this article, we will look at the use of a trailing stop in trading. We will assess how useful and effective it is, and how it can be used. The efficiency of a trailing stop largely depends on price volatility and the selection of the stop loss level. A variety of approaches can be used to set a stop loss.

We all know the importance of the Moving Average indicator for a lot of traders. There are other Moving average types that can be useful in trading, we will identify these types in this article and make a simple comparison between each one of them and the most popular simple Moving average type to see which one can show the best results.

In this article, we introduce a trade layering strategy that combines MACD and RSI indicators with statistical methods to automate dynamic trading in MQL5. We explore the architecture of this cascading approach, detail its implementation through key code segments, and guide readers on backtesting to optimize performance. Finally, we conclude by highlighting the strategy’s potential and setting the stage for further enhancements in automated trading.

In this article, we explore the Grid-Mart Scalping Strategy, automating it in MQL5 with a dynamic dashboard for real-time trading insights. We detail its grid-based Martingale logic and risk management features. We also guide backtesting and deployment for robust performance.

In my previous article, I showed you how to create a Chart Trade using MetaTrader 5 objects and thus to turn the platform into a RAD system. The system works very well, and for sure many of the readers might have thought about creating a library, which would allow having extended functionality in the proposed system. Based on this, it would be possible to develop a more intuitive Expert Advisor with a nicer and easier to use interface.

This is the first part of the new order system. Since we started documenting this EA in our articles, it has undergone various changes and improvements while maintaining the same on-chart order system model.

Old trading strategies. This article presents one of the strategies used to follow the trend in a purely technical way. The strategy is purely technical and uses a few technical indicators and tools to deliver signals and targets. The components of the strategy are as follows: A 14-period stochastic oscillator. A 5-period stochastic oscillator. A 200-period moving average. A Fibonacci projection tool (for target setting).

This is a new article in the series in which we learn how to design a trading system based on popular technical indicators. This time we will cover Bill Williams' Market Facilitation Index (BW MFI).

A new article in our series about learning how to design a trading system based on popular technical indicators will be about the Gator Oscillator technical indicator and how to create a trading system through simple strategies.

There are many technical tools that can be used to visualize a channel surrounding prices, One of these tools is the Donchian Channel indicator. In this article, we will learn how to create the Donchian Channel indicator and how we can trade it as a custom indicator using EA.

In this article I am going to attempt to use our logistic model to predict the stock market crash based upon the fundamentals of the US economy, the NETFLIX and APPLE are the stocks we are going to focus on, Using the previous market crashes of 2019 and 2020 let's see how our model will perform in the current dooms and glooms.

In the previous article, we started considering methods aimed at improving neural network training quality. In this article, we will continue this topic and will consider another approach — batch data normalization.

Welcome to a new article in our series about how to design a trading system by the most popular technical indicators. In this article, we will learn about a new technical indicator and how to create a trading system using the Force Index indicator.

Today I want to introduce you to a slightly different learning method. We can say that it is borrowed from Darwin's theory of evolution. It is probably less controllable than the previously discussed methods but it allows training non-differentiable models.

Data Classification is a crucial thing for an algo trader and a programmer. In this article, we are going to focus on one of classification logistic algorithms that can probability help us identify the Yes's or No's, the Ups and Downs, Buys and Sells.

In this article we will continue discussing the brute force approach. I will try to provide a better explanation of the pattern using the new improved version of my application. I will also try to find the difference in stability using different time intervals and timeframes.

Todays trader is a philomath who is almost always (either consciously or not...) looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. This clearly places a premium on the trader's time and the need to avoid mistakes. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders. Why? Because not only does the trader save time by assembling his new ideas with the MQL5 wizard, and greatly reduce mistakes from duplicate coding; he is ultimately set-up to channel his energy on the few critical areas of his trading philosophy.

Here is a new article in our series about how to design a trading system by the most popular technical indicators. In this article, we will present how to create a trading system by the DeMarker indicator.

This article focuses on taking advantage of in-built meta trader 5 indicators to screen out off-trend signals. Advancing from the previous article we will explore how to do it using MQL5 code to communicate our idea to the final program.

In this article, we develop a Rapid-Fire Trading Expert Advisor in MQL5, leveraging the Parabolic SAR and Simple Moving Average (SMA) indicators to create a responsive trading strategy. We detail the strategy’s implementation, including indicator usage, signal generation, and the testing and optimization process.

While some concepts may seem straightforward at first glance, bringing them to life in practice can be quite challenging. In the article below, we'll take you on a journey through our innovative approach to automating an Expert Advisor (EA) that skillfully analyzes the market using a mean reversion strategy. Join us as we unravel the intricacies of this exciting automation process.

In this article, we develop a Zone Recovery System integrated with an Envelopes trend-trading strategy in MQL5. We outline the architecture for using RSI and Envelopes indicators to trigger trades and manage recovery zones to mitigate losses. Through implementation and backtesting, we show how to build an effective automated trading system for dynamic markets

The article provides an example of using a perceptron as a self-sufficient price prediction tool by showcasing general concepts and the simplest ready-made Expert Advisor followed by the results of its optimization.

Welcome to the new article from our series about learning how to design trading systems based on the most popular technical indicators. In this article, we will learn about a new technical indicator called Accumulation/Distribution indicator and find out how to design an MQL5 trading system based on simple AD trading strategies.

In this article, we implement trade execution and risk management for the Envelopes Trend Bounce Scalping Strategy in MQL5. We implement order placement and risk controls like stop-loss and position sizing. We conclude with backtesting and optimization, building on Part 18’s foundation.

Markov chains are a powerful mathematical tool that can be used to model and forecast time series data in various fields, including finance. In financial time series modelling and forecasting, Markov chains are often used to model the evolution of financial assets over time, such as stock prices or exchange rates. One of the main advantages of Markov chain models is their simplicity and ease of use.

This article is the beginning of a series of developments for a library called “Connexus” to facilitate HTTP requests with MQL5. The goal of this project is to provide the end user with this opportunity and show how to use this helper library. I intended to make it as simple as possible to facilitate study and to provide the possibility for future developments.