History of MQL5.community development

The article describes and codes MR Swing in MQL5, a mean‑reversion swing approach that combines a 200‑day hysteresis channel with Value Charts, DVO, and SVAPO. We document entry/exit rules for bull and bear regimes and show five‑year backtests on six high‑liquidity Nasdaq stocks. The complete EA code and backtest configurations are provided for reproducibility.

We replace native MetaTrader chart objects with a canvas-based drawing engine that renders tools pixel-by-pixel on a full-chart bitmap layer. The article implements persistent object storage with per-tool style memory, precise hit testing, selection, whole-object dragging, and handle manipulation. It also adds new line tools, a reorganized category system with a one-click delete action, and a rubber-band preview for multi-click placement.

We bridge Python-native artifacts to MQL5 for tick-accurate CPCV backtesting. The export script converts the ONNX model, calibrator, feature spec, and path masks to flat files, while the expert advisor rebuilds features, performs ONNX inference with calibration, and trades on real ticks. The Strategy Tester runs each combinatorial path, and Python aggregates per-path equities into a path Sharpe distribution to assess robustness after spread, slippage, and commission.

The article describes an MQL5 EA that automates Wyckoff accumulation and distribution via a finite state machine. It confirms spring to SOS and upthrust to SOW before placing LPS or LPSY entries, using relative tick volume as the confirmation metric. Readers get the state model, detection criteria, code organization, and MetaTrader 5 testing procedure.

The article explains how to develop a tool for analyzing recurring price patterns in financial markets — by day of the month (1-31), day of the week (Monday-Sunday), or hour of the day (0-23). The indicator analyzes historical data, calculates the average return for each period, and displays the results as a histogram with a forecast. It includes customizable parameters: seasonality type, number of bars analyzed, display as percentages or absolute values, chart colors.

What if an optimization algorithm could remember its past journeys and use that memory to find better solutions? BSA does just that – balancing exploration with revisiting the tried and true. In this article, we reveal the secrets of the algorithm. A simple idea, minimum parameters and a stable result.

Before you give up and decide to abandon learning SQL, allow me to remind you, dear readers, that here we are still using only the most basic elements. We have not yet looked at some of SQL's capabilities. Once you understand them, you will see that SQL is far more practical than it seems. Although, most likely, we will eventually change the direction of what we are building, because the creation process is dynamic. We will show a little more about creating different things in SQL, because this is truly important and useful for you. Simply thinking that you are more capable than an entire community of programmers and developers will only lead to wasted time and opportunities. Do not worry, because what comes next will be even more interesting.

In this article, we will begin looking at how to work with objects directly on the chart. This is done using code specially developed for demonstration purposes. Working with objects is very interesting and can be a lot of fun. Since this will be our first contact with the topic, we will start with something very simple.

Time gap analysis helps traders identify potential market reversal points. The article discusses what a time gap is, how to interpret it, and how it can be used to detect large volume influxes into the market.

In this article, we take a closer look at the DEA algorithm, a metaheuristic optimization method inspired by dolphins' unique ability to find prey using echolocation. From mathematical foundations to practical implementation in MQL5, from analysis to comparison with classical algorithms, we will examine in detail why this relatively new method deserves a place in the arsenal of researchers facing optimization problems.

Many of you may have far more experience working with databases than I do, and therefore may have a different opinion. Since it was necessary to explain why databases are designed the way they are, and why SQL has the form it does—especially why primary and foreign keys emerged—some things had to remain somewhat abstract.

In this article, we will look at how to handle user requests to change the chart plotting mode. This is necessary so that an indicator designed for the current chart plotting mode does not look strange or differ from what a MetaTrader 5 user expects.