History of MQL5.community development

The article considers simple options strategies and their implementation in MQL5. We will develop a basic EA that will be modernized and become more complex.

For this article we look at a custom MQL5 Wizard class for Trailing Stops. Our implemented custom class ‘CTrailingConvexHullGRU’, is built from merging the Convex Hull algorithm with a GRU network. As always we seek to develop a model that is testable with MQL5 Wizard-Assembled Expert Advisors and can be tuned with various Money Management and entry Signals classes. Our testing is with the 'Envelopes' and the RSI classes for Signal.

AlternativeBarsViewer is a subwindow indicator that renders all ten alternative bar types as color‑coded candles using the same CBarConstructor hierarchy as BarBuilderEA, ensuring identical bars. It supports three data sources (real ticks, synthetic OHLC ticks, or the EA's CSV) and two render modes (TIME and INDEX) toggleable at runtime. Degenerate bars are highlighted and summarized on a compact panel, enabling live calibration without leaving the terminal.

We introduce MPO4, a pressure-based oscillator that emphasizes the body and direction of candles in the context of current volatility. The article details its mathematics, normalization into a bounded range, and the EMA smoothing, then builds a pivot-driven divergence module designed not to repaint. You get complete MQL5 implementation and practical guidance for interpreting signals, including a comparison with RSI as an alternative source.

Cross-cutting concerns like logging, timing, and threshold filtering should not live inside indicator classes. We show how to apply the decorator pattern in MQL5 with a shared IIndicator interface, an owning CBaseDecorator, and concrete CLoggingDecorator, CTimingDecorator, and CThresholdFilterDecorator layers. You can stack behaviors per EA, keep computation code closed to modification, and get deterministic cleanup by deleting only the outermost decorator.

We continue to implement the DA-CG-LSTM framework, which offers innovative methods for time series analysis and forecasting. The use of CG-LSTM and dual attention allows for more accurate detection of both long-term and short-term dependencies in data, which is particularly useful for working with financial markets.

Many people tend to underestimate SQL, or even not use it at all, because they do not fully understand how it actually works. When running queries against an SQL database, we are not always looking for a universal answer; in some cases, we need a very specific and practical answer. If a database is created with a proper structure and data model, almost any type of information can be integrated into it.

This article belongs to the category of materials where simply looking through and studying the code is definitely not enough to understand the processes involved. In fact, you need to create an executable application and run it on any chart. This is done so that you can understand small details that would otherwise be extremely difficult to grasp, such as using the keyboard and mouse together to create certain elements.

Secrets of effective optimization of trading strategies in metaheuristic approaches. Community of Scientists Optimization is a new population-based algorithm inspired by the mechanisms of the scientific community. Unlike traditional nature-inspired metaphors, CoSO models unique aspects of human scientific activity: publishing results in journals, competing for grants, and forming research teams.

The article presents a practical foundation for shape analysis of price series in MQL5. It implements Takens time‑delay embedding to build a phase‑space point cloud and computes the full pairwise distance matrix under selectable norms. The CTDAPointCloud and CTDADistance classes are provided with a demo script that embeds chart data and outputs results, preparing inputs for downstream topological tools.

We add a descriptor-driven property stack and a floating ribbon that binds to the current selection on the drawing layer. The article covers the descriptor list for each tool, the engine get/set API with snapshot-and-restore live preview, and widget renderers for color, opacity, line width, line style, fonts, and level visibility. You get in-place, real-time editing of object appearance via a compact, draggable panel.

We extend the weekend gap toolkit with an indicator that turns gap structure into tradeable signals. When price confirms back into the gap, the indicator issues buy/sell arrows, sets TP at the opposite edge, and places SL using current-week extremes. It maintains non-repainting behavior, reconstructs historical signals, updates live, and provides EA-ready buffers for entry markers and TP/SL to support automation.

This article implements the NLMS-based Self-Adaptive Moving Average as a working MQL5 indicator. It provides the complete source code and explains the key design choices, including inline execution, uniform weight seeding, closed‑bar updates, and stability bounds, along with installation, usage, and limitations. The result is a compiled, chart‑ready SAMA_NLMS indicator and a clear basis for subsequent EA benchmarking.

This article shows how to build a hands-free trading workflow for MetaTrader 5 by translating webcam-tracked hand gestures into MQL5 trade commands. We cover the architecture (MediaPipe/OpenCV in Python plus an MQL5 EA), gesture-to-action mapping, and interprocess communication via Global Variables or HTTP polling. You will implement the EA, execute BUY/SELL/CLOSE actions, and validate latency and reliability under real‑time conditions.

The article introduces CMicrostructureFeatures, an MQL5 class for bar‑level microstructure features: Roll spread/impact, Corwin‑Schultz spread and sigma, Kyle's Lambda, Amihud's ILLIQ, and Hasbrouck's Lambda. Calculations rely solely on OHLCV using rolling windows. It clarifies the implications of MT5 tick volume for lambda estimators and keeps spread estimators volume‑independent. A validation script asserts sizing and basic bounds on outputs.

This article introduces the DA-CG-LSTM algorithm, which offers new approaches to time series analysis and forecasting. It explains how innovative attention mechanisms and model flexibility can improve forecast accuracy.

An EA-ready weekend gap-fill tool for MetaTrader 5 that detects gaps, confirms complete fills, and posts deterministic buy/sell values to indicator buffers. It reconstructs historical events, monitors live markets without repainting, and visualizes gap structure directly on the chart. Configurable alerts and clear object graphics support both manual review and automated execution.

The article presents an MQL5 Expert Advisor that adapts the Ford–Fulkerson max-flow method into a liquidity-capacity filter. Market structures—Swing Highs/Lows, Fair Value Gaps, Order Blocks, and Liquidity Pools—form a directed graph with edge capacities from volume, price reaction, distance, and structure quality. Maximum flow qualifies ICT setups, filters weak paths, and drives dynamic position sizing for a consistent, two-stage decision process.

In this article we consider a custom MQL5 Wizard class that processes Money Management. Our custom class is labelled ‘CMoneyWaveletLSTM’, and is developed by combining the Wavelet Thresholding algorithm with an LSTM network. As has been the case throughout these series, the developed model is testable with MQL5 Wizard-Assembled Expert Advisors that can be tuned with different trailing stops and entry Signals classes. We maintain our entry Signal, as in past articles as the built-in 'Envelopes' class and the RSI class.

Direct calls to the MQL5 History API inside analytics components create hidden terminal dependencies that make isolated testing structurally impossible. This article constructs an ITradeRepository abstraction layer with CLiveTradeRepository and CMockTradeRepository implementations, enabling the same analytics engine and equity curve panel to operate identically against live account data or a deterministic in-memory dataset. Repository injection eliminates direct API coupling, supports offline validation, and confines data source changes to a single implementation class.

The article presents the Competitive Learning Algorithm (CLA), a new metaheuristic optimization method based on simulating the educational process. The algorithm organizes the population of solutions into classes with students and teachers, where agents learn through three mechanisms: following the best in the class, using personal experience, and sharing knowledge between classes.