History of MQL5.community development

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.

We implement an EquiVolume indicator in MQL5 that converts standard candlesticks into volume-weighted boxes. The workflow includes selecting volume type, detecting the maximum volume within a lookback range, normalizing all values against it, and mapping them into proportional box widths. The result is a chart-based structure that visualizes trading activity intensity alongside price movement in MetaTrader 5.

This article presents a multi-symbol execution filter that scores real-time market quality before any trade is allowed. It measures spread behavior, tick velocity, quote gaps, micro-volatility, and a slippage estimate, then classifies the state to block degraded conditions. Once noise settles, a liquidity sweep continuation model evaluates structure shifts so entries occur only when execution is mechanically stable.

We build a compact profiler that records calls, min/max/average times, and slow-call counts to CSV, and a simple test runner that writes deterministic pass/fail reports. The article explains where to place measurements in an EA, how to sample ticks, and how to keep pure calculations testable. Running the script first and the profiling EA second provides repeatable evidence for regression analysis.

We introduce CPyramidBridge, a thin MQL5 layer that maps bet-sizing results to CPyramidEngine. The bridge applies probability to initial lot sizing, enforces a capacity-aware entry gate, promotes add-ons from dynamic divergence, adapts the trailing stop to reserve estimates, and syncs signals on close, allowing an Expert Advisor to convert model confidence and concurrency into a structured, decreasing-risk pyramid.

In this article we present yet another custom MQL5 Signal Class that we are labelling ‘CSignalBTreeBayesian’. We are marrying the algorithm of a balanced tree with a neural network that is built on Bayesian principles to formulate yet another custom signal testable independently or with other signals thanks to the MQL5 Wizard.

This article presents a compact MQL5 utility layer for routine trade operations. It includes position existence checkers, position counters, bulk close helpers, and functions to retrieve the most recent or oldest position by symbol, magic, or type. A simple SMA crossover Expert Advisor demonstrates integration. The result is cleaner EAs, fewer inconsistencies across projects, and faster maintenance.

This article develops an object-oriented framework for MQL5 indicators by evolving a primitive example into reusable modules. It formalizes partial buffer recalculation in OnCalculate, moves logic into header-based classes (CAppliedPrice, CSma), and introduces CSubIndiBase, CIndicatorBase, and a registry to centralize requirements. You get portable components, isolated inputs, and clean buffers with minimal boilerplate, making new indicators faster to assemble and easier to maintain.

Developing permutation-based tools in MQL5 provides a systematic way to analyze candlestick pattern combinations for trading strategies. This article introduces a permutation calculator and generator designed to compute and enumerate all possible ordered candlestick sequences from bullish and bearish sets, with or without repetition. By generating exhaustive pattern combinations, traders can perform data-driven analysis to identify high-probability market patterns and improve decision-making in automated trading systems.

In this article, we shift from Python research to native MQL5 engineering. We build the first module of the MMAR library: a shared constants header, an SVD-based OLS regression class, a Generalized Hurst Exponent estimator, and the partition analysis engine that computes the partition function, extracts tau(q), estimates H via zero-crossing interpolation, and scores multifractality through three diagnostic tests. Tested on 500,000 bars of EURUSD M10, the engine correctly classifies the data as multifractal in under four seconds. Part 4 of an eight-part series. Part 5 fits the tau(q) curve to four candidate distributions via the Legendre transform.

An SLSQP optimizer is implemented in MQL5 to resolve parameter discrepancies between a volatility library and Python's ARCH module. The article details constraint handling, gradient options, configuration, and convergence controls and shows how to integrate the solver into existing code. Practical examples and comparisons demonstrate matched log‑likelihoods and parameters on shared datasets.