MQL5 Programs Market

This article explores the development of an ensemble algorithmic trading strategy for the EURUSD market that combines the Bollinger Bands and the Relative Strength Indicator (RSI). Initial rule-based strategies produced high-quality signals but suffered from low trade frequency and limited profitability. Multiple iterations of the strategy were evaluated, revealing flaws in our understanding of the market, increased noise, and degraded performance. By appropriately employing statistical learning algorithms, shifting the modeling target to technical indicators, applying proper scaling, and combining machine learning forecasts with classical trading rules, the final strategy achieved significantly improved profitability and trade frequency while maintaining acceptable signal quality.

In this article, we build a correlation matrix dashboard in MQL5 to compute asset relationships using Pearson, Spearman, and Kendall methods over a set timeframe and bars. The system offers standard mode with color thresholds and p-value stars, plus heatmap mode with gradient visuals for correlation strengths. It includes an interactive UI with timeframe selectors, mode toggles, and a dynamic legend for efficient analysis of symbol interdependencies.

This is a specialized trend-following EA that makes both short and long-term analyses, trading decisions, and executions based on the overall trend and its strength. This article will explore in detail an EA that is specifically designed for traders who are patient, disciplined, and focused enough to only execute trades and hold their positions only when trading with strength and in the trend direction without changing their bias frequently, especially against the trend, until take-profit targets are hit.

This article explores a method that combines Heikin‑Ashi smoothing with EMA20 High and Low boundaries and an EMA50 trend filter to improve trade clarity and timing. It demonstrates how these tools can help traders identify genuine momentum, filter out noise, and better navigate volatile or trending markets.

This article demonstrates how to automate Larry Williams’ volatility breakout strategy in MQL5 using a practical, step-by-step approach. You will learn how to calculate daily range expansions, derive buy and sell levels, manage risk with range-based stops and reward-based targets, and structure a professional Expert Advisor for MetaTrader 5. Designed for traders and developers looking to transform Larry Williams’ market concepts into a fully testable and deployable automated trading system.

In this article, we introduce functions similar to those provided by the Python-MetaTrader 5 module, providing a simulator with a familiar interface and a custom way of handling bars and ticks internally.

Given everything that has been shown so far, I think we can now start implementing some kind of application to run some symbol directly on the chart. However, first we need to talk about a concept that can be rather confusing for beginners. Namely, it's the fact that applications developed in MQL5 and intended for display on a chart are not created in the same way as we have seen so far. In this article, we'll begin to understand this a little better.