MQL5 Programming Articles

The article examines the Archimedes Optimization Algorithm — a metaheuristic in which an agent is represented as a physical object with density, volume, and acceleration, and the search itself is reinterpreted as the tendency of bodies submerged in a fluid to reach equilibrium. The balance between exploration and exploitation is not dictated by an external schedule here, but rather stems from the physics of decaying turbulence. We will implement the algorithm in MQL5, test it on a standard test bench, and analyze where this approach works.

This article implements an MQL5 Expert Advisor that scores each impulse leg behind a swing extreme with a 0–100 Candle Imbalance Score. The score combines four bounded components—body dominance, directional consistency, leg efficiency, and Fair Value Gap coverage—to filter weak swings before any pullback setup is armed. You will see enforced swing alternation, a state machine for first-clean-retest-only entries, and a three-mode trailing stop, enabling consistent setup selection and exit management.

We implement the CMonteCarlo module that turns the fitted MMAR parameters into a volatility forecast via Monte Carlo. It runs N independent simulations over a chosen horizon and reports mean, median, standard deviation, and a percentile-based 95% confidence interval, with access to per-run values if needed. Adaptive cascade depth selects the minimal k such that b^k covers the horizon, keeping the run fast and consistent.

In this article, we will look at three of the six events that MetaTrader 5 can generate when some change occurs to an object on the chart. These events are very useful from the standpoint of user interaction. This is because, without understanding these events, we would have to put in much more effort to maintain a specific chart configuration when trying to manage objects for particular purposes.

The article discusses the Colliding Bodies Optimization (CBO) algorithm, which is based on the physics of one-dimensional collisions between bodies. The basic version of the algorithm does not include any configurable parameters, which makes it simple. Therefore, the enhanced ECBO version — supplemented with Colliding Memory and a crossover mechanism — was used as the basis for the implementation, allowing the algorithm to achieve respectable results and earn a place in the ranking table.

The article discusses the parameters of the second stage of the automatic optimization pipeline for a multi-currency Expert Advisor. We analyze the criteria for filtering first-stage passes and the rules for forming groups of trading strategies. The article demonstrates how settings affect optimization results, discusses aspects of process reliability, and examines the balance between selection strictness and having enough candidates for the algorithm.

Here we will start bringing together different components or applications that were previously completely isolated from each other. Chart Trade, the mouse indicator, and the Expert Advisor had already been linked to one another, but there was still no way to directly display on the chart the positions open on the trading server, which are often managed using a cross-order system. From this point on, this becomes possible, opening the way for various ideas and future implementations. Although we are only beginning to put these components into operation, we already have a direction for further development.

An MQL5 script reconstructs closed trades from deal history using a two-pass SL/TP lookup and exports them to an Excel-compatible XLSX file without third-party libraries. Four cooperating classes handle trade data, history reconstruction, SpreadsheetML XML generation, and ZIP assembly via .NET's ZipFile class through a direct ShellExecuteW call with marker-file polling. The output opens in Excel and Google Sheets with correct numeric types, formatted date columns, and a bold header row.

The RAFT framework offers a fundamentally different approach to forecasting market dynamics — not as a one-time snapshot, but as an iterative refinement of the state in real time. It takes both local and global changes into account, maintaining a high degree of accuracy even with complex price structures.

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.

The framework evaluates boundary sensitivity by rebuilding shifted target-timeframe bars from completed M1 sources and running one Strategy Tester pass for each boundary offset. It includes a ShiftedBarBuilder, a demonstration EA, and a deterministic self-test. An XAUUSD M15 case study reports net profit, Profit Factor, maximum relative equity drawdown, and total trades across all 15 offsets to quantify dependence on the bar grid.

We decode ONNX files in pure MQL5 by implementing a Protocol Buffers reader from scratch. We generate a sample network in Python, verify it in Netron, and then parse the same binary to recover graph nodes, connections, weight tensors, and input/output shapes. The result is a MetaTrader 5 program that inspects a trained model's structure before inference, without any external libraries.

This article explains Monte Carlo simulation and analysis for trading and guides you through a Python tool that ingests MetaTrader 5 HTML reports. It generates many randomized equity paths, then summarizes them with max drawdown, bust/profit rates, and percentile envelopes around the mean curve. The workflow helps you assess uncertainty, separate normal behavior from outliers, and size positions accordingly.

A Sharpe ratio read off the best of many optimization runs is not the number it looks like. This article ships a reusable native CDeflatedSharpe class that turns a raw Sharpe into an honest confidence statement. The Probabilistic Sharpe Ratio corrects it for sample length and for skew and kurtosis; the Deflated Sharpe Ratio adds the correction almost nobody applies, for the number of variants you tried before keeping the best. Everything is from scratch, the sample moments, the normal CDF and its inverse included, so there is no Python, no DLL and no library. On a real sweep of 56 moving-average variants on XAUUSD the winner looked significant at 98.5 percent by PSR, then fell to 90.6 percent once the 56 trials were admitted, below the usual bar. That gap is the selection bias, made measurable.