History of MQL5.community development

This article details a practical framework for converting MetaTrader 5 trendlines from static drawings into managed runtime entities. It covers object discovery, event-driven synchronization of user edits, and confirmation logic based on ATR multipliers and closed candles. A central manager coordinates multiple lines and updates their visual state. Readers can implement consistent, extensible rules for detecting proximity, validating bounces, and confirming breakouts.

Aggregate metrics alone do not reveal how a trade sequence manages risk. This MQL5 tool analyzes closed positions to flag four structural patterns: post-loss volume escalation, overlapping same-direction entries, asymmetric payoffs, and a classical risk-of-ruin figure. The results are merged into a configurable A-F grade with concise recommendations to guide further review.

The article details a master–agent MQL5 framework that mitigates cross-symbol risk concentration. A single Portfolio Controller publishes risk limits and halt flags to Instrument Agents through shared channels and a readiness flag, while agents size orders only within the published budget. It contrasts global variables, named pipes, and files, and clarifies timer intervals and latency so data allocation may be up to one cycle stale without breaking coordination.

Price outliers distort indicators based on the mean and standard deviation. This article delivers a robust MQL5 library (RobustStats.mqh) implementing the median, 1.4826-scaled MAD, and Theil–Sen slope, plus three drop‑in indicators that replace Bollinger Bands, the linear regression channel, and the z‑score oscillator. A comparison overlay and a breakdown‑point measurement on EURUSD show how the robust instruments hold their shape when a single spike moves the classical ones.

The article describes the development of an MVP prototype for an autonomous trading bot for MetaTrader 5 that uses large language models (LLMs) via the OpenRouter API to analyze the market and make trading decisions. A Python script retrieves historical OHLCV data, sends it to an LLM for technical analysis based on support/resistance levels and Price Action patterns, and then automatically places orders with specified stop loss and take profit levels.

We invite you to explore a new approach that combines classical methods and modern neural networks for time series analysis. The article provides a detailed explanation of the architecture and operating principles of the K²VAE model.

The article describes the process of fine-tuning a language model for trading based on real historical data from MetaTrader 5. The base model, which has only theoretical knowledge of technical analysis, is trained on a thousand examples of the real behavior of currency pairs (EURUSD, GBPUSD, USDCHF, USDCAD) over 180 days. After being trained using Ollama, the model begins to understand the specific characteristics of each instrument.

This article presents a unified news model and a set of reusable MQL5 classes for working with the MetaTrader 5 Economic Calendar. You will retrieve, filter, and cache events by time, currency, country, and importance using a single interface across three providers: built-in calendar, CSV, and SQLite. The framework supports export/import, next/previous event lookup, and reliable strategy‑tester backtesting without changing trading logic.

We evaluate blind source separation for market noise control using FastICA applied to SMA-filtered, time-lagged OHLC features. The study compares classical and surrogate targets, measures accuracy across lags, tunes KNN models, and inspects residual structure with clustering. Models are exported to ONNX and integrated into an MQL5 Expert Advisor for testing. The result is a reproducible pipeline from data extraction to deployment.

The article discusses the Differential Search Algorithm (DSA), which simulates the migration of a superorganism in search of optimal living conditions. The algorithm uses a Gamma distribution to generate a pseudo-stable random walk and offers four strategies for selecting the direction of movement, along with three coordinate mutation mechanisms. How will this method perform?

We invite you to explore the original implementation of the K²VAE framework — a flexible model capable of linearly approximating complex dynamics in latent space. This article demonstrates how to implement key components in MQL5, including parameterized matrices and how to manage them outside standard neural network layers. This material will be useful for anyone looking for a practical approach to building interpretable time-series models.

The article describes an approach to trade labeling using oscillators for machine learning models. This eliminates look-ahead bias. It has been shown that this type of labeling does not lead to model overfitting, and the strategies continue to perform well over the long term.

The article's system introduces CBasketManager: positions are grouped by a comment‑based basket ID, analyzed as a single snapshot, and controlled with a unified equity stop. CBasketScanner computes aggregate P&L and volume‑weighted pip performance; CBasketStopRegistry triggers coordinated closure on threshold breach; CBasketExecutor adapts to the broker's filling mode. A lightweight dashboard shows live legs, volumes, stops, and distances for faster basket decisions.

We complete the native MQL5 port of Kronos: the decoder, the predictor's decode_s1 and decode_s2 stages with their cross-attention traps, and the autoregressive loop that produces a multi-bar forecast. Then we profile and make it roughly 4.5x faster with an exact KV-cache and pre-transposed weights, verifying every stage against PyTorch.

This article builds a robust SuperTrend indicator in MQL5 using ATR-based bands, a ratchet mechanism, and strict series indexing to avoid silent recursion errors and repainting on closed bars. We walk through buffer binding, ATR handle management, seeding, and arrow confirmation logic. A companion EA demonstrates practical integration

We are adding to our web application the ability to retrieve and display information about the terminal instances’ trading accounts, including balance, profit, connection status, and other important details. We will also implement a flexible configuration system that lets you manage application settings via an external JSON file, and improve the user interface of the main page.

Trend-scanning supports both forward and backward windows, and the labeling default is unsafe for features: it looks ahead and boosts next-bar agreement well above chance on random walks. We provide a dedicated wrapper, get trend scanning features, that forces computational causal and returns only window, slope, t value, and rsquared. A second analysis quantifies errors introduced by the default log transform on signed series.

We invite you to dive into the exciting world of LightGTS — a lightweight yet powerful framework for time-series forecasting, where adaptive convolution and RoPE encoding are combined with innovative attention mechanisms. In our article, you will find a detailed description of all components — from creating patches to the complex mixture of experts in the decoder — ready for integration into MQL5 projects. Discover how LightGTS takes automated trading to a whole new level!

The article presents the implementation and analysis of the Bonobo Optimizer algorithm, which is based on the unique behavioral characteristics of bonobos — their dynamic fission-fusion social structure and three mating strategies. What interesting features does this method have?

The article shows how to evaluate machine-learning alphas before a full backtest by expressing them as formulaic alphas. We compute Information Coefficient (IC), Rank IC, Information Ratio (ICIR), and t-statistics to quantify forecasting strength and stability. A MetaTrader 5 backtest illustrates differences versus execution-dependent tests, and a Python parser facilitates reproducible calculations and bulk screening.

We implement an interactive Crosshair Volume Profile indicator in MQL5 for MetaTrader 5. Using a right-click-and-drag crosshair, you select a chart region; the indicator then builds adaptive price bins, accumulates tick or real volume, detects the POC, and renders the profile on the chart. You will practice OnChartEvent()-driven interaction, cursor-to-time/price mapping, and price-source models (Close, Median, Typical, Weighted, OHLC4) for on-demand volume analysis.

This article develops a dynamic ATR-based trend channel indicator in MQL5 that responds to current market volatility. It derives True Range, applies a two-step ATR smoothing, and constructs adaptive upper and lower boundaries to track trend shifts. The tool also renders a trailing trend line, trend-colored candles, and reversal arrows, offering a usable code base for volatility-aware analysis and further indicator design.

This article delivers active drawdown monitoring, automated mitigation rules, Excel XML data export, and AI-assisted review for the Portfolio Analyzer dashboard. It visualizes strategy-level drawdowns over time, enforces limits by closing positions and optionally disabling AutoTrading, and generates structured spreadsheets from trade records. A hybrid MQL5-Python approach runs the external review script directly from the terminal, supporting practical risk control and transparent reporting.

Flat files work well at the start of an MQL5 research pipeline, but they hinder cross-run queries and provenance once the archive grows. We build a Python-based SQLite registry that ingests CSV exports with SHA-1 deduplication, records EA version and run timestamps, applies forward-only schema migrations, and indexes common filters. You get a structured query layer for fast lookups, robustness checks, and version comparisons across all campaigns.