MQL5 Programming Articles

Implement the Malaysian Engulfing concept in MQL5 with two coordinated indicators. One applies strict, body‑based engulfing rules for precise pattern detection; the other uses a state-driven model to monitor what follows—pullbacks and timed retests—directly on the chart. The result is a repeatable, rule-based workflow that replaces visual guesswork with programmable logic.

In this article, we will consider conformal predictions and the MAPIE library that implements them. This approach is one of the most modern ones in machine learning and allows us to focus on risk management for existing diverse machine learning models. Conformal predictions, by themselves, are not a way to find patterns in data. They only determine the degree of confidence of existing models in predicting specific examples and allow filtering for reliable predictions.

We present a rule‑based alphabet for candlestick price action that maps measurable shape and direction to letter codes (A/a, H/h, E/e, G/g, D). The article shows an MQL5 implementation: classifying candles, building two‑bar sequences via permutations, and scanning charts with an indicator and alerts. Readers gain a practical template for objective pattern detection and systematic testing.

A custom forward simulation engine detects fast/slow EMA crossovers and immediately projects synthetic candles ahead of the signal bar. It generates bodies and wicks using controlled logic, draws them with chart objects, and refreshes on every new signal or anchor change. You get a clear forward-looking view to test timing, visualize scenarios, and manage invalidation on the chart.

We will review the basics of Gaussian processes (GP) as a probabilistic machine learning model and demonstrate its application to regression problems using synthetic data.

The article presents two systematic pitfalls in MQL5 multi‑timeframe work: indicator handle leaks that exhausted resources and repainting from reading the forming bar (index 0). It introduces MTFEngine.mqh, a unified include that creates and tracks handles in one place and defaults all reads to closed bars (index 1). A D1–H4–H1 example shows how this approach keeps signals technically correct and consistent with charts.

Biogeography-Based Optimization (BBO) is an elegant global optimization method inspired by natural processes of species migration between islands within archipelagos. The algorithm is based on a simple yet powerful idea: high-quality solutions actively share their characteristics, while low-quality ones actively adopt new features, creating a natural flow of information from the best solutions to the worst. A unique adaptive mutation operator provides an excellent balance between exploration and exploitation. BBO demonstrates high efficiency on a variety of tasks.

Learn how to build a manual backtesting EA for MetaTrader 5's visual tester by adding chart buttons with CButton, executing orders through CTrade, and filtering positions with a magic number. The article implements Buy/Sell and Close All controls, configurable lot size and initial SL, and a trailing stop via CPositionInfo. You will also see how to load indicators with tester.tpl to validate ideas faster before automation and narrow optimization ranges.

This article extends a Flask backend to reliably receive, validate, and store closed trade data from MetaTrader 5 using SQLite and Flask‑SQLAlchemy. It implements required‑field checks, timestamp conversion, transaction‑safe persistence, and working retrieval endpoints for all trades and single records, plus a basic summary. The result is a complete data pipeline with local testing that records trades and exposes them through a structured API for further analysis.