History of MQL5.community development

The article examines the Hamiltonian Monte Carlo (HMC) algorithm — the gold standard for sampling from complex multivariate distributions. A full-featured implementation of HMC in MQL5 is presented, including adaptive mass matrix tuning, MAP estimation using the L-BFGS optimization method, and comprehensive diagnostics.

In the previous article, we considered how to implement a position indicator that allows you to close an open position directly from the chart by interacting with an object available on the chart. After completing and testing the first mechanism, we began making changes to ensure that take-profit and stop-loss levels could be removed for an open position. However, since the necessary changes required detailed explanations, in that same article I showed only the changes that needed to be made to the expert advisor; I still needed to show the changes that needed to be made to the position indicator.

We invite you to explore the innovative SCNN framework, which takes time series analysis to a new level by clearly separating data into long-term, seasonal, short-term, and residual components. This approach significantly improves forecasting accuracy by allowing the model to adapt to complex and changing market dynamics.

This article will explain a very simple and easy-to-understand mechanism whose purpose is to sort any array. In it, we will see that the result obtained does not always meet expectations, so the implementation itself will need to be adapted to achieve the desired results.

Fixed exits ignore state changes while a trade is open. We implement a discrete-time competing-risks model entirely in MQL5, estimate cause-specific hazards for take-profit and stop-loss via Newton–Raphson on a person-period dataset with time-varying features, and turn cumulative incidence into a bar-by-bar hold-or-close rule, then test it against fixed take-profit/stop-loss with identical entry logic.

This article presents a complete RL trading pipeline for XAUUSD: a supervised signal baseline with triple-barrier labels, PPO training, purged walk-forward validation with embargo, multi-seed checks, and contract-guarded deployment with normalization. It includes runnable code for data validation, features, environment, training, and broker‑based reconciliation. The live demo over 763 closed trades showed no statistically significant edge, and the methods highlight where information and costs, not architecture, set performance limits.

The Dandelion Optimizer (DO) turns the simple flight of a seed carried by the wind into a mathematical search strategy. The three phases — vortex rising, drift toward the center of the population, and landing along a Lévy-flight trajectory — form an elegant metaphor that yields interesting results in practice.

In this article, we'll start making some improvements to the position indicator so that we can interact with it and modify price lines or close a position directly through the position indicator. Before we move on to the implementation, there are a few things worth clarifying, especially for those who aren't familiar with this. The indicator cannot be used in any way to change anything on the trading server. This is because MetaTrader 5 has a security system in place that allows only Expert Advisors to modify orders and positions. No application other than an Expert Advisor can manipulate orders or positions.

The article proposes a synthesis of new technologies to overcome the limitations of classical indicators in market data analytics. It shows how language models and quantum encoding can reveal hidden market patterns that traditional methods overlook. The experiment confirms the value of new technologies and proposes an updated analysis methodology aligned with the current state of computational innovation.

In this article, we'll look at two ways to inspect the contents of the sandbox and even interact with it, using MetaTrader 5 as the base platform. Understanding the material in this article is essential to understanding what will be covered in subsequent articles.

Raw feature correlations contain estimation noise and a shared market-mode component that distort clustering. We fit the Marcenko–Pastur noise ceiling (with an effective sample size correction), apply constant-residual denoising and market detonation, and run the Optimal Number of Clusters routine. The result is a cleaned correlation matrix and stable cluster labels that avoid substitution effects and feed clustered MDI/MDA in the next article.

This article builds a Hull Moving Average Momentum indicator in MQL5 by combining raw price momentum with Hull MA smoothing. We compute momentum as the close-to-close difference over a user-defined length, form 2×Fast WMA − Slow WMA, then apply a final WMA with a square‑root period. The implementation covers inputs, buffers, warm-up/recalculation, and visualization with a color-coded line and zero-line filling, helping interpret positive/negative momentum without treating zero crossings as signals.

We implement a History Navigator for MetaTrader 5 that jumps the chart to an exact historical period by date and time. The dialog validates inputs, converts them to datetime, and searches bar times with a binary-search routine before centering the selected candle. The navigation logic is separated from chart control, improving testability and maintenance, and a one-click return restores the live market view.

This article opens a step-by-step 2D graphics engine for MetaTrader. It standardizes ARGB colors and implements a reusable surface: a uint pixel buffer uploaded as a dynamic resource and shown via one OBJ_BITMAP_LABEL. You will draw rectangles and a vertical gradient, check real transparency, and learn an efficient update path with a single Flush call.

We are pleased to present the final part of our series on GinAR — a neural network framework for time series forecasting. In this article, we analyze the results of testing the model on new data and assess its robustness under real-market conditions.

The Dendritic Cell Algorithm (DCA) is a metaheuristic inspired by the mechanisms of the innate immune system. Dendritic cells patrol the search space, accumulate signals about the quality of positions, and reach a collective decision: whether to exploit what they have found or to continue exploration. Let's take a look at how a biological model for detecting pathogens is transformed into an optimization algorithm.

This article implements a self-contained Isolation Forest library for MetaTrader 5 with no labels, no distribution assumptions and no external dependencies. It details a reproducible 64‑bit generator, tree/forest construction, scoring and feature design, then verifies results against Python and market data with two null models. The package includes an indicator that plots the decision variable and a gate example. Readers get a validated library, clear limits of applicability and a practical way to calibrate thresholds.

We invite you to explore a new implementation of the key components of the GinAR framework — an adaptive algorithm for working with graph-structured time series. This article provides a step-by-step breakdown of the architecture and the algorithms for the forward pass and error backpropagation.

This article develops a visual position planning tool in MQL5 for evaluating trade setups before execution. The tool utilizes interactive Entry, Stop-Loss, and Take-Profit lines to calculate the stop distance, risk amount, estimated position size, potential reward, and risk-to-reward ratio directly on the chart. It supports market, limit, and stop order scenarios while keeping the focus strictly on planning and analysis rather than trade execution.

A state-driven Position Lifecycle Manager brings structure to post-entry trade handling in MetaTrader 5. It discovers open positions, tracks them via managed objects, applies ATR-based protection, executes break-even transitions, and removes completed trades, with a clear NEW → PROTECTED → BREAKEVEN → CLOSED flow. The article shows integration with the standard MACD EA to enable reuse across strategies.

The article presents an adaptation of the Deterministic Dendritic Cell Algorithm (dDCA) for continuous optimization problems. The algorithm, inspired by the immune system's Danger Theory, uses a signal accumulation mechanism to automatically balance exploration and exploitation within the search space.