History of MQL5.community development

We invite you to learn about the innovative technique of adaptive patching — a method for flexibly segmenting time series while taking their internal periodicity into account. We will also look at an efficient encoding technique that preserves important semantic characteristics when working with data at different scales. These methods open up new possibilities for the accurate processing of complex, multiscale data characteristic of financial markets and significantly improve the stability and reliability of forecasts.

We implement CTrailingEngine, an interface-driven MQL5 engine that evaluates each registered position on every tick and applies one of five trailing methods: fixed-pip, ATR multiplier, Parabolic SAR, percentage-of-profit, or swing high/low. All methods share the ITrailMethod contract, so new trails plug in without engine edits. Strict improvement and a one-point guard block backward moves and no-change SLTP modifications.

EGARCH models log-variance, avoiding the non-negativity constraints that can distort GARCH estimates and enabling a clear treatment of leverage asymmetry. The article provides a complete MQL5 implementation with logarithmic backcasting, simulation-based multi-step forecasting, and diagnostics including the Engle–Ng Sign Bias, Leverage Correlation, and Volatility Runs tests. Practical outputs include EGARCH Volatility, an Innovation Z-Score, and an Asymmetric Volatility Regime Oscillator to support regime analysis and strategy design.

Experimental evaluation on standard benchmark functions reveals the advantages and limitations of directly adapting combinatorial algorithms. The article provides a detailed description of the ECEA algorithm's mechanisms and test results.

A direct MQL5 port of the fractal detector writes each pattern at its center bar, so a buffer read by an expert advisor holds a value that only existed n bars later. We implement CFractalFeatures.mqh with ProcessBar for bar-by-bar use and Compute for full-series recalculation, covering detection, strength scored against a fixed or volatility-scaled floor, an event-based support/resistance ring, and trend-filtered breakout signals. Output is eighteen buffers published at the confirmation bar, verified against the Python reference to within 1e-13.