MQL4 and MQL5 Programming Articles

The article proposes a hybrid approach to algorithmic trading based on quantum encoding of market states, Double DQN with a prioritized experience replay buffer, and an LLM acting as a contextual EA. The SEAL methodology enables asynchronous continued training of the agent without halting trading. A lightweight Q-learning filter (USE/SKIP/REDUCE) controls signal execution at the meta-level. Practical details are provided on integrating the system with the MetaTrader 5 trading platform, along with a scheme for adapting it to market regime shifts.

An LLM agent with SQL memory in MetaTrader 5 — an architecture that eliminates the main limitation of traditional LLM Expert Advisors (EAs): the loss of context between queries. Each agent decision is recorded in SQLite along with the market conditions at the time of the signal — RSI, MA alignment, volatility — and after the position is closed, the outcome is written to the database. When the next request comes in, the agent reviews its own history — similar past RSI conditions and the last three agent decisions — and only then analyzes the current market. The result is a system that remembers its errors between restarts and adapts its behavior based on accumulated statistics, rather than starting from scratch with each new bar.

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.

In this article, we will look at how the last three types of events generated by an object work. Understanding this will be very interesting, because in the end we will do something that may seem crazy to many people, but it is entirely possible and produces a very surprising result.

The article examines the practical application of finite differences in trading: types of differences, their relationship to price dynamics, and the binomial transform for noise filtering. The rules for encoding patterns based on difference levels and the application of these patterns to forecasting are described. This section presents naive, adaptive, and probabilistic approaches that help smooth time series, identify recurring patterns, and estimate future movements.

The article shows how to build an MQL5 EA that writes every deal to an SQLite database the moment it appears, using the built-in Database API as the SQLite bridge. It implements an event data model, a prepared INSERT workflow reused across calls, session-safe recovery after restarts, and deal detection via OnTrade(). You can open the resulting file with any SQLite client to run queries for analysis and reporting.

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.