History of MQL5.community development

The article considers a new population optimization algorithm - Cyclic Parthenogenesis Algorithm (CPA), inspired by the unique reproductive strategy of aphids. The algorithm combines two reproduction mechanisms — parthenogenesis and sexual reproduction — and also utilizes the colonial structure of the population with the possibility of migration between colonies. The key features of the algorithm are adaptive switching between different reproductive strategies and a system of information exchange between colonies through the flight mechanism.

In this article, we develop a Trendline Breakout System in MQL5 that identifies support and resistance trendlines using swing points, validated by R-squared goodness of fit and angle constraints, to automate breakout trades. Our plan is to detect swing highs and lows within a specified lookback period, construct trendlines with a minimum number of touch points, and validate them using R-squared metrics and angle constraints to ensure reliability.

This article proposes an asset screening process for a statistical arbitrage trading strategy through cointegrated stocks. The system starts with the regular filtering by economic factors, like asset sector and industry, and finishes with a list of criteria for a scoring system. For each statistical test used in the screening, a respective Python class was developed: Pearson correlation, Engle-Granger cointegration, Johansen cointegration, and ADF/KPSS stationarity. These Python classes are provided along with a personal note from the author about the use of AI assistants for software development.

This article follows up ‘Part-74’, where we examined the pairing of Ichimoku and the ADX under a Supervised Learning framework, by moving our focus to Reinforcement Learning. Ichimoku and ADX form a complementary combination of support/resistance mapping and trend strength spotting. In this installment, we indulge in how the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm can be used with this indicator set. As with earlier parts of the series, the implementation is carried out in a custom signal class designed for integration with the MQL5 Wizard, which facilitates seamless Expert Advisor assembly.

This article explains how to design and optimise a trading system using the Detrended Price Oscillator (DPO) in MQL5. It outlines the indicator's core logic, demonstrating how it identifies short-term cycles by filtering out long-term trends. Through a series of step-by-step examples and simple strategies, readers will learn how to code it, define entry and exit signals, and conduct backtesting. Finally, the article presents practical optimization methods to enhance performance and adapt the system to changing market conditions.

In this article, we develop a Shark pattern system in MQL5 that identifies bullish and bearish Shark harmonic patterns using pivot points and Fibonacci ratios, executing trades with customizable entry, stop-loss, and take-profit levels based on user-selected options. We enhance trader insight with visual feedback through chart objects like triangles, trendlines, and labels to clearly display the X-A-B-C-D pattern structure

The MQL5 Standard Library plays a vital role in developing trading algorithms for MetaTrader 5. In this discussion series, our goal is to master its application to simplify the creation of efficient trading tools for MetaTrader 5. These tools include custom Expert Advisors, indicators, and other utilities. We begin today by developing a trend-following Expert Advisor using the CTrade, CiMA, and CiATR classes. This is an especially important topic for everyone—whether you are a beginner or an experienced developer. Join this discussion to discover more.