History of MQL5.community development

In this article, we will explore the best ways to manage code when working with object-oriented programming. Although we are just beginning to learn about object-oriented programming, what we will cover here will help you understand its various aspects. This will also help dispel any doubts that may arise later.

Financing drives multi‑day index‑CFD results: in one full‑history test, swap consumed 44% of gross profit and all profit on one symbol. We convert swaps to annualized rates, contrast four brokers and two financing models with a read‑only script, and quantify a Strategy Tester issue where a single current swap is used for all history, inflating implied rates by up to seven times. The piece provides a repeatable cost‑audit method.

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.

In this article, we will implement a number of improvements to ensure that the position indicator accurately reflects the actual state on the trading server in terms of open positions and their current state. I should point out that the applications shown here are in no way intended to replace any of the elements available in MetaTrader 5. They should also not be used without due caution and a balanced approach, since their purpose is to provide educational code—that is, code intended solely for learning how the system works. The reason I call this code “educational” is that, in some cases, using messages is not the best way to implement certain functions.

This part focuses on practical data analysis in MQL5 with dataanalysis.mqh. We prepare a labeled dataset from bars, apply normalization, explore redundancy with PCA, and train a decision forest to classify future bar regimes. The article shows how to obtain out-of-bag estimates and permutation importance, helping you validate the model and understand which inputs matter most.

Establishes the persistence foundation for a prop-firm compliance EA in MetaTrader 5. It introduces rule inputs and status enums, separates live account state from stored settings, validates percentages and thresholds, and implements SQLite open/close, schema creation, and prepared save/load operations. Using the account login and server as a composite key, the EA restores existing settings and updates them when inputs change.

We invite you to learn about an algorithm for decomposing a time series into meaningful layers and using them to build a parsimonious model. We systematically present the architecture, the practical implementation in MQL5/OpenCL, and real-world tests using historical market data.

The article discusses the Cricket Algorithm, a metaheuristic optimization method that combines elements of the Bat Algorithm and the Firefly Algorithm with the physical laws governing the propagation of sound in the atmosphere. The algorithm simulates the behavior of crickets that navigate by the chirping of their conspecifics, using Dolbear's law and acoustic formulas to guide the search for best solutions.

In this article, we will examine the Finite Volume Elements (FVE) indicator, which helps identify genuine capital flows in the market. We will implement FVE for MetaTrader 5 and review recommendations for using it in trading.

In this article, we continue our hands-on exploration of SSCNN — a next-generation architectural solution capable of processing fragmented time series. Instead of blind scaling — smart modularity, attention to detail, and targeted normalization. Step by step, we are creating computational blocks in the MQL5 environment and laying the foundation for reliable predictive analysis.

This article presents a modular position sizing engine for MetaTrader 5 that operates on normalized R-multiples. A layered pipeline combines enriched trade statistics, a generalized Kelly edge estimate, volatility-aware adjustment, Monte Carlo calibration under ruin and drawdown limits, a continuous risk policy, an exposure guard, and a broker-aware lot calculator. The output is a broker-valid lot size with an optional CSV audit trail, providing a transparent prototype for implementing modern risk controls in native MQL5.

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.

The EA now defines risk by percentage, fixed cash, or fixed lot and can measure percentage against balance or equity. It supports market, limit, and stop orders, sizes from the planned entry, and enforces spread‑aware stop minima. Additional safeguards include downward volume rounding, explicit handling when the minimum lot exceeds target risk, and pending‑order distance/expiry checks, organized under a Plan–Validate–Execute structure.

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.

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.