History of MQL5.community development

A new optimization method, the Bison Algorithm (BIA), uses two strategies, inspired by the behavior of bison, for solving continuous problems with a single objective function. The key features of BIA are two fundamental principles borrowed from the behavior of bison: the ability to move dynamically and a defensive strategy.

In this article we make the case for merging Divergence Mapping with a Temporal Fusion Proxy in a Trading Robot. Rather than depending on lagging price confirmations, the Divergence Mapping's thesis is that acting like a structural sensor can help identify hidden momentum shifts from price action and indicator anomalies. To establish how these anomalies are interpreted over time we use a Temporal Fusion Transformer proxy. This network incorporates historical context to weigh developing trends such that merging it with Divergence Mapping should set us up to spot shifts in accumulation and distribution before price breakouts.

The article introduces the Mapper pipeline in MQL5 by implementing the two fundamental components: CTDAMapperFilter (lens) and CTDAMapperCover (overlapping intervals). It explains three lens options—eccentricity, density, and coordinate—plus cover parameters (resolution and gain), and demonstrates how a price point cloud is reduced to one value per point and interval memberships. Readers obtain ready inputs for subsequent clustering and graph construction.

A refined MQL5 script exports trade history to a well‑formed JSON file in MQL5/Files/, reconstructing trades from deals by position ID and recovering stop loss and take profit via a two‑pass lookup that falls back closed to the originating order. It includes a dedicated JSON serializer and computes R‑multiple, pip profit, and duration. The result loads cleanly in Python, R, or Excel without custom parsing.

The article describes an interesting approach to algorithmic trading based on symbolic mathematical equations instead of traditional machine learning "black boxes". The author demonstrates how to transform opaque neural networks into readable mathematical equations using the SymPy library and polynomial regression, allowing for a full understanding of the logic behind trading decisions. The approach combines the computational power of ML with the transparency of classical methods, giving traders the ability to analyze, adjust, and adapt models in real time.

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.