MQL5 Programming Articles

The article bridges automated placement with manual analysis for the Fibonacci family in MQL5. It scans charts, identifies user Fibonacci objects, and normalizes their level arrays, interaction flags, and visuals per object type while preserving coordinates. With manual-priority enforcement, Expert Advisors can evaluate both human and code-generated tools reliably, without duplicates or runtime indexing issues.

This article builds a white-box classifier in MQL5 using the Tsetlin Machine. It learns human-readable AND-rules instead of weights, trains with integer state updates, and requires no external dependencies. You will assemble the automaton, clause, and multi-class voter, verify on XOR and other boolean tasks, booleanize indicators, label by forward ATR-scaled return, save the model to CSV, and view active rules on a live chart.

We extend the supply and demand framework with a strategy layer that converts zone interactions into decisions. Qualified zones pass sequential checks for interaction proximity, approach behavior, higher‑timeframe alignment, and price action before execution is handed to a dedicated trade manager. This architecture improves control, maintainability, and future extensibility without changing the underlying zone engine.

The article provides a fascinating look at how SwiGLU embedding reveals hidden market patterns, and how a sparse Mixture of Experts within a Decoder-Only Transformer makes forecasts more accurate at reasonable computational cost. We take an in-depth look at the integration of Time‑MoE into MQL5 and OpenCL, and provide a step-by-step guide to configuring and training the model.

This article performs a numerical verification of MQL5 eigendecomposition for a covariance matrix using the spectral theorem A = V Λ Vᵀ. It reconstructs the matrix with Diag(), Transpose(), and MatMul(), computes the residual and its Frobenius norm, and shows that deviations remain at floating‑point precision, with results printed to the Experts journal.

We continue enhancing our modular indicator search panel by adding symbol selection capabilities. The implementation allows users to search for built-in indicators, choose a destination symbol, and attach the selected indicator without opening multiple charts or running separate Expert Advisor instances.

This article benchmarks CUSUM_Breakpoint.mq5 against the Siegmund ARL₀ prediction on live‑like data. The empirical false‑alarm rate is about five times higher than theory for all tested symbols and timeframes, and confirmations show sensitivity to variance changes over mean changes. Practitioners should calibrate h and k on the target instrument's history and apply the signal to manage volatility regimes, not to infer directional shifts.