History of MQL5.community development

We build a rolling price channel by fitting the 0.1, 0.5 and 0.9 conditional quantile lines via IRLS with pinball loss, packaged as a reusable class and two MetaTrader 5 indicators. We verify in-sample coverage, examine quantile crossing, and compare the channel width with ATR, Bollinger and regression widths on matched horizons. Tests in the Strategy Tester show the edges are descriptive, while the normalized width works as a volatility/regime feature.

In this article, we refine a moving average crossover strategy with a momentum candle filter and an immediate retracement bar confirmation. When both conditions are met, a pending stop order is placed using a pivot-based stop loss and a 2R take profit. The complete MQL5 Expert Advisor code, finite-state-machine logic, and chart annotations are detailed.

Bollinger Band mean reversion degrades in trending regimes when ADX is high and bandwidth expands. We separate direction from trade selection with a two‑stage meta‑labeling pipeline: a gradient‑boosted secondary classifier trained with PurgedKFold on band‑specific features (BBP, BBB, bandwidth regime) outputs action probabilities that drive probability‑based bet sizing. The MQL5 implementation loads the ONNX model and applies position sizing within a two‑EA architecture to filter low‑quality band touches.

This article presents a circuit breaker for MQL5 that monitors combined daily P&L (realized plus floating) on every tick and compares it to a configured loss limit. On breach, it closes positions, cancels pending orders, and activates a HALTED state that blocks further order submission in the EA until server‑time midnight. The package provides a chart dashboard, a demo Expert Advisor, a verification script, and notes on extending the halt signal across EAs.

Learn how to build an MQL5 Expert Advisor that detects and trades Larry Williams’ Oops Gap Reversal pattern using objective gap rules and later-bar confirmation. The EA tracks setup expiration, prepares stop-loss and take-profit levels, supports manual or risk-based position sizing, executes market orders, and is evaluated through historical testing.

An MQL5 analyzer reconstructs completed trades, records acceptance labels, and measures the accepted-minus-rejected mean net-profit difference. It benchmarks that statistic against individual permutations, equal-block permutations, and circular shifts while preserving the accepted count. Block-size sensitivity, CSV exports, and coordinated base/filtered passes separate statistical selection evidence from operational effects on profit, drawdown, and efficiency metrics.

This article presents CSwingEngine, a reusable MQL5 class that detects H4 swing highs and lows, labels them HH, LH, HL, or LL, and classifies market structure as trend or range. Swings are always computed on H4, regardless of the attached chart, and each point draws correctly on lower timeframes via native datetime anchoring. The engine exposes a clean interface to query the current trend and retrieve the swing array for context-aware pattern logic.

This article presents a compact MQL5 library for market-complexity analysis: LZ76 complexity and Normalized Compression Distance built on a SAX symbolizer, exposed through a simple facade and an efficiency indicator. It explains the discretization choices, normalization, and distance formulation, and validates the code with unit checks and an independent cross-check. You get a ready-to-use library and indicator, plus a disciplined way to interpret readings with a shuffle null and a direction check.

A naive MQL5 port of trend-scanning features recomputes each candidate window per bar at O(H·L) cost. This article introduces CTrendScanningFeatures.mqh, which maintains three running sums per horizon and updates them in O(1) per bar, verified against a Python reference. The indicator exposes four causal buffers - window, slope, t_value, rsquared - at the confirmation bar and corrects a sign inversion present in the original backward labeling mode.

This article implements an online logistic‑regression trade filter in native MQL5 and integrates it into an EMA‑crossover EA with a closed‑trade feedback loop. It details the shared class, features, SGD update, persistence, and a read‑only probability view. Synthetic experiments cover multi‑seed separation, calibration, feature ablation, regime‑shift baselines, and hyperparameter sweeps. You get reproducible scripts and a walk‑forward protocol to validate the filter on your own instrument.