History of MQL5.community development

This article presents a complete MQL5 Expert Advisor that implements Jesse Livermore's market key as a deterministic state machine. It detects pivotal levels from consolidations using ATR and volume expansion, scales in across four tranches, and exits on abnormal behavior defined by range and volume. The EA validates inputs in OnInit, requires a hedging account, and compiles out of the box for testing Livermore's rules on daily data.

This article presents reusable MQL5 utilities for managing trailing and break-even stops. It covers fixed-point, moving average, ATR, Parabolic SAR, money-based, and time-periodic trailing, plus break-even by points and by money, with activation thresholds, step logic, reverse-move protection, and broker-level validation. Code examples and Bootstrap classes show how to integrate these helpers into Expert Advisors to standardize position control and reduce duplicate code.

SEAL (Self-Evolving Adaptive Learning) is a system for the continuous adaptation of large language models (LLMs) for algorithmic trading, designed to address the problem of rapid model degradation in changing markets. Instead of periodic retraining, which takes hours and erases old patterns, SEAL learns from every closed trade, maintains priority memory for important examples, and automatically initiates incremental fine-tuning when accuracy drops or a market regime change occurs.

Risk-based lot sizing can still exceed what free margin allows. The article adds a margin-aware cap using OrderCalcMargin(), an optional adaptive cap that scales with ACCOUNT MARGIN LEVEL, and a single pre-trade validation gate that unifies position limits, risk sizing, and margin checks. Readers get concrete code to prevent order rejections and over-committing margin, with clear logs when a trade is reduced or skipped.

Part 5 moves risk control from single trades to a basket-level framework. The EA aggregates its own positions, computes volume‑weighted entry, floating P/L including swap, and used margin, then enforces limits on combined loss, margin, position count, and time underwater, while logging maximum adverse excursion. A companion mean‑reversion EA demonstrates target‑based sizing and caps on implied risk that remains hidden when trades are evaluated in isolation.

The article discusses the ECO optimization algorithm, which is based on ecological concepts: populations are grouped into habitats based on territorial proximity, exchange genetic material within habitats, and migrate between them. Despite its wide range of operators and elegant biological metaphor, the algorithm produced a certain result discussed below.

This article is intended to be as educational as possible, since the topic we will be discussing often causes considerable confusion in itself. Therefore, dear reader, please try to put what is explained here into practice. If you have any questions, be sure to leave a comment—after all, understanding destructors is no easy task.

The article will show you how Mamba4Cast turns theory into a working trading algorithm and lays the groundwork for your own experiments. Do not miss this opportunity to gain a full range of knowledge and inspiration for developing your own strategy.

We build a robust MQL5 detector for double tops and double bottoms that first confirms the H4 trend, then validates six conditions (point equality, neckline placement, ordering, width, height, and ATR‑based tolerances). The neckline break is timed on the chart's timeframe, and a three-state machine ensures each pattern trades once. The measured‑move target translates structure into clear exits.

This article develops a Fisher‑style Indicator in MQL5 from first principles: normalize price within a recent high/low window, smooth and clamp the value, then apply a logarithmic transform. We cover buffer wiring, calculation‑buffer state management across bars, and seeding for stable starts. An accompanying EA implements threshold and reversal confirmation to show how to act on the signal.