History of MQL5.community development

We implement ordinal pattern transition networks in MQL5: a Lehmer-code encoder, a directed network over ordinal price patterns, and three complexity metrics. Two indicators expose a trend-versus-range regime from time-irreversibility and an efficiency gauge from permutation entropy, with a transparent parameter sweep showing how to tune settings on FX data.

This article presents a distribution-adaptive spread monitor for MQL5 that replaces fixed thresholds with a rolling histogram of each symbol's recent spread. It explains percentile estimation from bins, a four-state GREEN/YELLOW/RED/WARMING classification, and a CCanvas dashboard rendered from real histogram data. You will get a ready workflow for per-symbol order gating and controlled alerting via arm/disarm hysteresis plus cooldown, with a verification script and clear calibration and resolution limits.

We demonstrate a practical customization path for a built-in MetaTrader 5 EA using BullishBearish MeetingLines Stoch. The workflow covers baseline testing in the Strategy Tester, parameter optimization, and code-level changes. Two modifications are implemented: exposing Stochastic thresholds as inputs and adding an optional Moving Average filter to limit counter‑trend signals. The article includes the full modified code for replication.

A native, dependency-free MQL5 implementation of the Mann-Whitney U test for comparing trade returns across two market regimes. It details rank calculation, tie correction, and a normal-approximation p-value, and pairs the test with a CCanvas box-and-whisker chart and a trade-history extraction script. A verification script is included, and the limits of the normal approximation and independence assumptions are clearly stated for informed use.

In this article we make the case for pairing the Discrete Fourier Transform with a Spiking Neural Network in a Trading Robot. The Fourier Transform helps represent data as oscillations instead of its raw values. To govern how we interpret these cycles, we engage a Spiking Neural Network that unlike regular networks, uses time dependent electrical charges to accumulate potential and only "spike" when a target threshold is met. Combining these two engines allows us better control on the timing of discrete market movements, that in theory should give us entry signals with rigorous mathematical confirmation.

The custom modification of the Dingo algorithm presented in the article has raised the bar for finding the best optimization algorithm. Are even better results possible?

The article quantifies correlation and portfolio risk in MetaTrader 5: from time-aligned returns to a covariance matrix, true portfolio variance against the independent-sum assumption, and position-level risk attribution. A MetaTrader 5 service runs in the background, shows the metrics on a small chart panel, and pushes alerts when risk thresholds are crossed. Source code is provided for an example script, a reusable risk engine class, and the service.

A structured MQL5 implementation of a multi‑symbol trading panel with clear separation of concerns: symbol handling, trading logic, and GUI. Integrated into an Expert Advisor, it validates symbols, exposes centralized controls for opening and positions managing across symbols, and applies SL/TP changes. Real‑time account and portfolio metrics help streamline routine operations from a single chart.

In Part 2 we measure the market's dominant cycle using Ehlers' Hilbert-transform homodyne discriminator and wrap it as an indicator. We then build the MESA Adaptive Moving Average (MAMA) and its follower FAMA from that phase information. Finally, we combine MAMA/FAMA with the Even Better Sinewave to form a regime-switching Expert Advisor and test it on EURUSD in the Strategy Tester, giving you a complete, reproducible MQL5 implementation.

Backtests often understate spread, commission, and slippage. This MQL5 analyzer loads closing deals and simulates rising execution costs to measure robustness. It computes the breakeven cost per deal, the cushion over an assumed cost, the net profit and profit factor at that cost, and how many winners turn into losers, then summarizes the result with an A+ to F grade and targeted guidance.

The article presents a step-by-step development of a multi-threaded trading robot with machine learning in Python and MetaTrader 5. The system architecture is considered – from data collection and creation of technical indicators to training XGBoost models with portfolio risk management. The implementation of data augmentation, feature clustering via Gaussian Mixture Models, and flow coordination for parallel trading of multiple currency pairs is described in detail.

This article presents CSymbolMetaCache, an MQL5 layer that preloads contract specifications and trading-session schedules for monitored symbols at EA startup and then serves typed getters from memory. It explains which properties are safe to cache versus dynamic ones, including the semi-dynamic tick value on cross-currency pairs, and implements an in-memory IsMarketOpen() evaluator. A benchmark quantifies latency reduction across a set of twenty symbols.

Your Expert Advisor runs clean on your demo, then throws errors on a client's broker and quietly stops trading - and the code never changed. What changed is the broker's rulebook. This first article of the Broker Reality Check series builds a diagnostic EA that reads every relevant symbol trading condition - filling policy, stops and freeze levels, volume step, trade mode, swap and the triple-swap day - and flags the ones that silently break EAs, in plain language. It shows a green/amber/red panel, prints a report and dumps every Market Watch symbol to CSV, so you see why an OrderSend fails (10030, invalid stops, invalid volume) before it costs you a trade.