MQL4 and MQL5 Programming Articles

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.

In this article, we will explore what structured code is. Many people confuse structured code with organized code, but there is a difference between these two concepts. This is exactly what will be discussed in this article. Despite the apparent complexity you may feel when first encountering this type of code writing, I have tried to approach the topic as simply as possible. However, this article is just the first step toward something greater.

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.