MQL4 and MQL5 Programming Articles

In this article, we will look at how the last three types of events generated by an object work. Understanding this will be very interesting, because in the end we will do something that may seem crazy to many people, but it is entirely possible and produces a very surprising result.

This article adds six order-flow functions and a new OrderFlowAnalysis struct to MicroStructureFoundation.mqh: VPINOHLC, signed flow imbalance, trade intensity versus a 20-session baseline, a late-minus-early smart-money index, flow momentum, and a wrapper that outputs a confidence weight. Flow confidence is gated by noise and jump intensity from Parts 5 and 4. Calibrated on 602 NQ M1 NY sessions, it provides ready-to-use intraday flow signals with documented thresholds.

This article presents a custom MetaTrader 5 indicator that computes a rolling annualized Sharpe ratio and plots configurable z-score significance bands based on Lo's asymptotic standard error. It uses a circular return buffer with incremental variance to keep O(1) updates. We explain the n^(-1/2) uncertainty scaling, the inflation of intervals at high Sharpe values, and how to set per-instrument annualization for correct deployment.

Fixed-weight moving averages introduce regime-insensitive lag. This work presents an adaptive scalar Kalman filter indicator in native MQL5 that estimates process noise Q from rolling return variance and measurement noise R from rolling price variance, with floor clamps for stability, and recomputes the Kalman Gain on every bar. The chart-overlay output is benchmarked against a 20-period EMA using MAE, RMSE, lag, and smoothness metrics to quantify tracking and noise suppression.

In this article, we will look at three of the six events that MetaTrader 5 can generate when some change occurs to an object on the chart. These events are very useful from the standpoint of user interaction. This is because, without understanding these events, we would have to put in much more effort to maintain a specific chart configuration when trying to manage objects for particular purposes.

In today's article, we will look at how to implement a very attractive and interesting interaction system, especially for those who are just beginning to practice programming in MQL5. There is nothing fundamentally new here. Thanks to my approach to the topic, it will be much easier to understand everything, because we will see in practice how to develop a program using a structured approach with a practical and engaging goal.

The article presents an innovative quantum neural network architecture for algorithmic trading that combines the principles of quantum mechanics with modern machine learning methods. The system includes quantum effects (resonance, interference, decoherence), multi-level memory of different time scales, Markov chains with the ALGLIB library, and adaptive parameter control. The full implementation is done in MQL5 using the built-in matrix/vector types, which removes implementation barriers in MetaTrader 5.

MetaTrader 5 is well suited for AI trading because it combines market data, MQL5 development, Python research, ONNX models, Strategy Tester, VPS, and the MQL5.community ecosystem into a single workflow. This article demonstrates a practical path from AI prompts to structured signals, working with code via the AI Assistant in MetaEditor, a quality model, a custom-created Expert Advisor, testing, and a controllable launch of a trading system.

Many people tend to underestimate SQL, or even not use it at all, because they do not fully understand how it actually works. When running queries against an SQL database, we are not always looking for a universal answer; in some cases, we need a very specific and practical answer. If a database is created with a proper structure and data model, almost any type of information can be integrated into it.

The article introduces CMicrostructureFeatures, an MQL5 class for bar‑level microstructure features: Roll spread/impact, Corwin‑Schultz spread and sigma, Kyle's Lambda, Amihud's ILLIQ, and Hasbrouck's Lambda. Calculations rely solely on OHLCV using rolling windows. It clarifies the implications of MT5 tick volume for lambda estimators and keeps spread estimators volume‑independent. A validation script asserts sizing and basic bounds on outputs.

Direct calls to the MQL5 History API inside analytics components create hidden terminal dependencies that make isolated testing structurally impossible. This article constructs an ITradeRepository abstraction layer with CLiveTradeRepository and CMockTradeRepository implementations, enabling the same analytics engine and equity curve panel to operate identically against live account data or a deterministic in-memory dataset. Repository injection eliminates direct API coupling, supports offline validation, and confines data source changes to a single implementation class.

The article presents the Competitive Learning Algorithm (CLA), a new metaheuristic optimization method based on simulating the educational process. The algorithm organizes the population of solutions into classes with students and teachers, where agents learn through three mechanisms: following the best in the class, using personal experience, and sharing knowledge between classes.

The article presents a reproducible MetaTrader 5 to Python pipeline for large-scale indicator research. An MQL5 export schema captures fixed columns, including custom lag and whipsaw counters. A baseline module performs parameter-matched comparisons across symbols and timeframes, while a walk-forward module locks the InSample optimum and evaluates it on unseen data. Readers gain unbiased robustness measurements and automation that removes manual selection bias.

This article introduces the Self-Adaptive Moving Average (SAMA), an adaptive filter leveraging the Normalized Least Mean Squares (NLMS) algorithm. It explores why fixed-period averages fail, how NLMS adapts bar by bar, and the engineering protections required for production. This conceptual and mathematical foundation prepares you for the MQL5 code implementation in Part 2.