History of MQL5.community development

This article introduces a lightweight OS-like helper for MQL5 that streamlines file and path operations using a Python-inspired interface. We implement getcwd, listdir, scandir with DirEntry, remove, rmdir, rename, mkdir, stat, and an os.path subset (exists, isfile, isdir, join, split, pardir). You will learn how to work consistently within the terminal Files/common sandbox and simplify everyday filesystem tasks.

FX options are quoted in delta space, not by strike. This article implements an FX-native smile tool for MetaTrader 5: it converts ATM, risk reversal and butterfly quotes into strike-space pillars, prices with the Garman–Kohlhagen model, handles spot/forward and premium-adjusted delta conventions per pair, and draws the smile with a reconstructed strike ladder and Greeks.

The article provides a systematic overview of the theory of random variables, which serves as the basis for analyzing and modeling uncertainty in financial markets. The article covers the definitions and properties of univariate random variables, cumulative distribution functions (CDFs) and probability density functions (PDFs), as well as the differences between discrete, continuous, and mixed models. The theoretical material is based on intuitive analogies with mass and density. The appendix to this article contains practical examples of using the standard MQL5 library to calculate probabilities, quantiles, and moments of distributions. It also demonstrates the graphical capabilities of the MetaTrader 5 platform for visual data analysis by plotting PDF and CDF curves and QQ plots.

This article presents a swing-based volume profile indicator in MQL5 that analyzes platform tick volume within completed high-to-low and low-to-high legs. It confirms swing points, partitions each leg's range into ATR(200)‑adaptive price bins, distributes tick volume, and marks the Point of Control. The indicator draws a ZigZag and renders the profile on chart rectangles, helping you study volume concentration within each swing relative to price structure.

We build an MQL5 program that detects and trades Double Top and Double Bottom patterns from confirmed swing pivots with rule-based logic. The detector matches peaks within a tolerance, derives the neckline, and applies leg-balance and spacing filters to reject weak shapes. It supports entry on a neckline break or an optional pullback, with a stop beyond the extreme and targets by measured move or reward-to-risk.

The article presents an MQL5 tool that tests whether scaling out improved results rather than only appearing disciplined. It reconstructs positions from closing-deal history and reprices the full volume at the first, last, and best exit rates actually achieved, producing a Value-Add Ratio, a Scale-Out Win Rate, and an Efficiency measure. A single-trade dependence check and a configurable A+ to F grade turn these into clear, decision-ready feedback.

Feature importance often understates correlated predictors by spreading one signal across many engineered copies, while unrelated noise can appear higher. We measure this effect against a known ground truth and compare four remedies: permutation importance with purged cross-validation, single-feature models, and clustered impurity versus clustered accuracy. The results include per-method rankings and a per-cluster dilution ratio that help identify true signals and avoid deleting valuable features.

In this article, we will examine the Dragonfly Algorithm (DA), inspired by the collective behavior of dragonflies in nature — their ability to coordinate flight in a swarm, avoid collisions, follow prey, and evade predators. Let's look at how five simple behavioral rules and an adaptive mechanism for transitioning from exploration to exploitation are implemented in MQL5, and test the algorithm on our test bench.

In this article, we will take a detailed look at the practical implementation of the key components of the SAGDFN framework. We will show how sparse attention and the selection of significant neighbors are organized for time series forecasting. The approaches presented strike a balance between forecast accuracy and computational efficiency.

The article examines three key obstacles to integrating LLMs with MetaTrader 5: the lack of direct access, strict rate limits, and API key security given the architectural limitations of MQL5. A configuration is proposed that uses a local Python server as a bridge between the Expert Advisor and OpenRouter. The article covers WebSocket and fallback to TCP, storing the key on the server, batch processing of multiple symbols, and constructing a technical prompt. Readers get a ready-made architecture that reduces latency and costs.

This article shows how a PDF works as plain text by hand‑written two files: a 592‑byte page and an 885‑byte trade ticket. It explains the file structure (header, body, xref, trailer), the required page objects and resources, and the operators that draw text, then provides an MQL5 script to generate them. First part of a pure‑MQL5 PDF library series.

I know that many of you may have thought I would publish a few more articles to explain other aspects of this system. The missing elements are easy to implement. Even so, developing them will let you see how prepared you really are.

In this article, we will look at how to modify the code to implement a solution entirely unlike what many consider possible in MQL5. Important note: To fully understand this material, you must have a solid grasp of the concepts covered in the previous articles.

The article examines an example of a multipurpose trading robot template that is suitable both for creating your own strategies and as a codebase for freelance work. A key feature of the solution is bar-based trading; the code already includes built-in modes for averaging, martingale, and holding positions for extended periods. This material will be most useful to beginners who want to develop their own simple strategies or learn about common trading techniques.

In this article, I will present our system for simulating market operations. Although everything is practically finished, there are still a few things to implement and a few changes to make. However, I have to admit that, after everything we've already developed, I'm tired of still being stuck on implementing this system.

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.

Code and build Python-based trading robots just like MQL5 Expert Advisors (EAs). In this article, we develop a Python-based replica of the MetaTrader 5 Python package, providing methods that closely resemble those of MetaTrader 5 during simulation. This allows us to backtest Python EAs in a simplified environment, using an approach similar to developing and testing Expert Advisors in MQL5.

We build an adaptive Fibonacci volatility band indicator in MQL5 that centers on a smoothed price (SMMA) and scales band width with a smoothed ATR. The article covers inputs, buffer mapping, ATR handling, and SMMA formulas, then projects configurable Fibonacci ratios with filled zones. Readers get a ready workflow for visualizing volatility expansion/contraction and outlining dynamic support and resistance.

We replace the embedded bitmap icons in our MQL5 canvas interface with a resolution-independent vector icon system. A small set of anti-aliased primitives (strokes, discs, rings, rounded rectangles, and polygons) draws every logo and sidebar glyph procedurally, then plugs into the header, sidebar, and theme toggle. You get smaller builds, theme-aware recoloring, and icons that stay sharp at any size.

In this article, we will take a first step toward showing how to implement operator overloading for the index operator and the assignment operator, while striving to offer a practical and interesting approach for everyone. What we will see here is only part of what I still intend to show, and it is directly related to the overloading of these operators.

Until now, the application being developed as part of this series of articles has focused exclusively on simulating the graphical part. However, to obtain a more complete system in which we can test the Expert Advisor within the replay/simulation service, we also need to simulate the trading server. You'll notice that this simulation will include only the most essential elements. Nevertheless, you, dear reader, will be able to fill in the missing parts. Since these additional components don't affect what I want to show, we already have more than enough to implement what we have in mind.

In this article, we explore the architecture of SAGDFN — a modern framework capable of transforming the approach to processing spatiotemporal data. It preserves key information even in complex graphs while reducing computational costs.

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 provides a critical analysis of an LLM strategy in which forecasting the direction is separated from trading decisions, and demonstrates why this leads to a disconnect between metrics and PnL. We will describe procedures for dataset balancing, feature engineering, prompt and response preparation, fine-tuning configuration in Ollama, and reliable parsing. Backtesting and forward testing reveal systematic degradation. The practical conclusion is that the problem must be formulated as a direct optimization of trading outcomes.