History of MQL5.community development

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.

This article introduces ap.mqh, the ALGLIB port for MQL5, and demonstrates its use in multi‑asset workflows that require robust linear algebra. It covers why built-in indicators fall short, then implements polynomial regression, a rolling correlation matrix indicator, and an adaptive hedge ratio estimator using ridge regression with Cholesky. Practical code shows how to compute spread z‑scores and execute coordinated pairs trades entirely within MetaTrader 5.

In this article we build a dealer gamma-exposure map in MQL5. From an option chain, the tool computes per-strike GEX, finds the call and put walls, and solves for the zero-gamma flip that separates a mean-reverting regime from a trending one, then draws it all on the chart. CSV and native-symbol data paths included.

An MQL5 script reconstructs closed trades from raw deal history and replays them on the chart bar by bar, drawing entry, exit, stop, target, and an annotation with per‑trade statistics. Four classes separate concerns: a trade data record, history reconstruction with a two‑pass SL/TP lookup and partial‑close aggregation, chart rendering, and a controller with polling‑based keyboard navigation. This enables consistent, fast visual review of each trade in its original candlestick context.

The article presents a new metaheuristic method based on the hunting strategies of Australian dingoes: group attack, chase, and scavenging. Let's see how the Dingo Optimization Algorithm (DOA) performs algorithmically.

This article implements recursive least squares in native MQL5 with a constant O(1) update per bar, avoiding the per‑bar O(n) rebuild of a rolling OLS. It derives and codes the Sherman–Morrison rank‑1 update, explains the forgetting factor through its effective window, and provides a reusable class. Two coordinated indicators plot a 1‑step‑ahead price forecast on the chart and the signed slope in a correctly scaled subwindow for practical trend tracking.

This article profiles a rolling z-score indicator with bands using MetaEditor's built-in sampling profiler. We read the Total CPU and Self CPU columns and follow the heat‑mapped source to the true hotspots, replace window rescans with sliding accumulators, remove a redundant array copy, and honor prev_calculated. The result is the same output with measured samples reduced from roughly 7,050 to 59.

Net profit and win rate tell you how much a strategy made, not how the result is distributed. This article builds a native MQL5 script that reads your closed trades and measures profit concentration: the top-N trade share, the Gini coefficient of the winners, an outlier-dependence stress test that removes the best few winners, and the largest day against a prop-firm consistency limit. It combines these into one A+ to F score with recommendations, running inside MetaTrader 5.

This article describes a prototype reusable market structure framework for MQL5, built with a clean modular architecture and an internal event queue. It shows how to detect swing points, classify break-of-structure and change-of-character events, maintain a deterministic market state, and persist data to CSV. The focus is entirely on software engineering, component separation, and extensibility, not on trading signals. The prototype is a foundation for further development, not a production-ready library.

A reproducible, read-only Python audit for MetaTrader 5 that verifies history quality before any backtest. It exports M5 data from multiple terminals, detects gaps and synthetic bars by timestamp spacing, and reports coverage per year. The same deterministic strategy then runs on three broker feeds over a common window to quantify result drift and decompose it into spread, data/price, and trade effects.

This article builds a sequential CUSUM breakpoint detector for MetaTrader 5, starting from the statistical construction and ending with a working indicator. It explains standardized log-returns, dual accumulators, the role of k and h, and the ARL₀ baseline from Siegmund. The code walkthrough covers buffer persistence, recalculation handling, idempotent chart objects, and a three-pass engine, so you can compile, attach, and use the detector to flag structural regime shifts earlier than fixed-window smoothers.

Part 8 adds bar-by-bar micro-trend scoring for NQ M1. GetMicroTrendStrength() builds a continuous [-1, +1] composite from EMA alignment, ATR‑normalized price position, slope consistency, and volume, with a contradiction penalty to suppress alignment/price conflicts. Session-adaptive thresholds scale by Part 7 confidence to modulate signal frequency across regimes. Outputs include a seven-state label, a binary signal, and a persistence check, calibrated on 514 New York sessions (May 2024–May 2026).

In this article, we will examine how two slightly different approaches can significantly affect the overall implementation strategy, both in performance and in disk I/O design, while helping to prevent compatibility issues between applications.

We test AFML's claim that the sequential bootstrap decorrelates bagged trees on overlapping triple‑barrier labels by isolating two levers: draw count and draw rule. One decision identical tree is bagged under four row‑sampling regimes and evaluated on EURUSD 2022–2023 for draw uniqueness, between‑tree correlation, AUC, and calibration. Decorrelation comes almost entirely from throttling max_samples to average uniqueness; the sequential draw adds little. Out-of-bag inflation is largest under full-count sequential sampling.

We implement an interactive, anchored multi-timeframe volume profile in MQL5 for MetaTrader 5. The indicator draws the current-timeframe profile on the main chart and a higher timeframe profile in a subwindow, both aligned by a draggable anchor and the visible range. You will learn keyboard-driven bin editing (E/S double-click), robust timeframe validation, viewport-aware updates, and object restoration to build a reliable, synchronized volume workflow.