History of MQL5.community development

In this article, we will talk about the C_ChartFloatingRAD class. This is what makes Chart Trade work. However, the explanation does not end there. We will complete it in the next article, as the content of this article is quite extensive and requires deep understanding. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

In this article, we will consider Market Profile indicator. We will find out what lies behind this name, try to understand its operation principles and have a look at its terminal version (MarketProfile).

Candlestick patterns are fundamental to price-action trading, offering valuable insights into potential market reversals or continuations. Envision a reliable tool that continuously monitors each new price bar, identifies key formations such as engulfing patterns, hammers, dojis, and stars, and promptly notifies you when a significant trading setup is detected. This is precisely the functionality we have developed. Whether you are new to trading or an experienced professional, this system provides real-time alerts for candlestick patterns, enabling you to focus on executing trades with greater confidence and efficiency. Continue reading to learn how it operates and how it can enhance your trading strategy.

The LSEAttention framework offers improvements to the Transformer architecture. It was designed specifically for long-term multivariate time series forecasting. The approaches proposed by the authors of the method can be applied to solve problems of entropy collapse and learning instability, which are often encountered with vanilla Transformer.

Non-linear regression models on the stock exchange: Is it possible to predict financial markets? Let's consider creating a model for forecasting prices for EURUSD, and make two robots based on it - in Python and MQL5.

Today we have a very funny and quite interesting article. We will look at Union and will try to solve the problem discussed earlier. We'll also explore some unusual situations that can arise when using union in applications. The materials presented here are intended for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

In this discussion, we’ll explore additional advancements as we integrate refined event‑alerting logic for the economic calendar events displayed by the News Headline EA. This enhancement is critical—it ensures users receive timely notifications a short time before key upcoming events. Join this discussion to discover more.

This article explains the Wolfe Wave pattern in detail, covering both the bearish and bullish variations. It also breaks down the step-by-step logic used to identify valid buy and sell setups based on this advanced chart pattern.

This article explores how determining the optimal number of strategies in an ensemble can be a complex task that is easier to solve through the use of the MetaTrader 5 genetic optimizer. The MQL5 Cloud is also employed as a key resource for accelerating backtesting and optimization. All in all, our discussion here sets the stage for developing statistical models to evaluate and improve trading strategies based on our initial ensemble results.

This article is meant as a guide for those unfamiliar with the concept of Singular Spectrum Analysis and who wish to gain enough understanding to be able to apply the built-in tools available in MQL5.

This article continues the series for beginners. Here we will discuss the basic principles of developing Expert Advisors (EAs). We will create two EAs: the first one will trade without indicators, using pending orders, and the second one will be based on the standard MA indicator, opening deals at the current price. Here I assume that you are no longer a complete beginner and have a relatively good command of the material from the previous articles.

We follow up on our last article, where we introduced the indicator pair of the Ichimoku and the ADX, by looking at how this duo could be improved with Supervised Learning. Ichimoku and ADX are a support/resistance plus trend complimentary pairing. Our supervised learning approach uses a neural network that engages the Deep Spectral Mixture Kernel to fine tune the forecasts of this indicator pairing. As per usual, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

In this article, we will modify the last code shown in this series about Chart Trade. These changes are necessary to adapt the code to the current replay/simulation system model. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

Dijkstra's algorithm, a classic shortest-path solution in graph theory, can optimize trading strategies by modeling market networks. Traders can use it to find the most efficient routes in the candlestick chart data.

In this article, we enhance our Zone Recovery System by introducing trailing stops and multi-basket trading capabilities. We explore how the improved architecture uses dynamic trailing stops to lock in profits and a basket management system to handle multiple trade signals efficiently. Through implementation and backtesting, we demonstrate a more robust trading system tailored for adaptive market performance.