MQL4 and MQL5 Programming Articles

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.

RSI accumulates losses in trending conditions by firing at every threshold crossing regardless of market regime. A Random Forest secondary classifier trained on 12 contextual features — RSI momentum slope, EMA50 trend velocity, ATR-normalised trend stretch, and nine others — filters raw signals and scales position size by classifier confidence on EURUSD H1. Results compare plain RSI, meta-filtered RSI, and bet-sized RSI across a 16-month out-of-sample period with per-trade metrics and drawdown diagnostics.

Downloading international monetary fund data in Python: Mining IMF data for use in macroeconomic currency strategies. How can macroeconomics help an ordinary and an algorithmic trader?

The article considers the classification model of Gaussian processes. We will start by studying its theoretical principles moving on to the practical development of the GP library in MQL5.

Downloading international monetary fund data in Python: Mining IMF data for use in macroeconomic currency strategies. How can macroeconomics help an ordinary and an algorithmic trader?

Building on the partition function analysis from Part 1, this article deepens the theoretical foundation before completing the analytical pipeline. We first give a full treatment of the Hurst exponent: what it measures, what it implies about market memory, and why it matters for the MMAR. This is followed by an intuitive exploration of multifractal spectra and what f(α) reveals about volatility heterogeneity. We then move to implementation: extracting the scaling function τ(q), estimating H via R/S analysis, and fitting the multifractal spectrum across four candidate distributions. By the end, we have the complete parameter set needed to construct the MMAR process in Part 3. Part 2 of an eight-part series.

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.

This work presents an end-to-end pipeline: collect MetaTrader 5 data, engineer entropy/volatility/trend features, train a PyTorch classifier, and expose predictions through a Flask API. An MQL5 EA posts rolling prices each tick, receives probability and regime, and applies adaptive position sizing and stop distances. The result is a clear recipe for integrating ML inference with MetaTrader 5.

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 a new approach to creating trading systems based on quantum principles and artificial intelligence. The author describes the development of a unique neural network that goes beyond classical machine learning by combining quantum mechanics with modern AI architectures.

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.

This article adds eight volatility functions to MicroStructure_Foundation.mqh, including realized volatility, duration-adjusted volatility, fractional volatility, a FIGARCH-inspired proxy, a volatility clustering index, a GJR-GARCH asymmetry measure (using the Dube library), bipower-variation jump detection, and a wrapper function. The MFDFA implementation is revised to return the conventional Legendre-transform Δα with an R² confidence field, replacing the τ-spread proxy used in the original submission. Thresholds are derived from 514 NY sessions of NQ E-mini Nasdaq 100 futures (May 2024–May 2026); no new include file is created.

For this article we switch to a custom MQL5 Wizard class that examines entry Signals. Our custom class is ‘CSignalDSUDBN’ this time around, and is coded by combining the Disjoint Set Union algorithm with a Deep Belief network. As has been the case throughout these series, our model is testable with MQL5 Wizard-Assembled Expert Advisors that can be tuned with different trailing stops and money management classes.

The article presents an MQL5 Expert Advisor that adapts the Ford–Fulkerson max-flow method into a liquidity-capacity filter. Market structures—Swing Highs/Lows, Fair Value Gaps, Order Blocks, and Liquidity Pools—form a directed graph with edge capacities from volume, price reaction, distance, and structure quality. Maximum flow qualifies ICT setups, filters weak paths, and drives dynamic position sizing for a consistent, two-stage decision process.

With the multifractal parameters from Part 2 in hand, this article builds the full MMAR process. We construct the multiplicative cascade for trading time, generate Fractional Brownian Motion via Davies-Harte FFT, and combine both into X(t) = B_H[theta(t)]. A 100-path Monte Carlo simulation produces the volatility forecast, which we then pit against GARCH on the same EURUSD M5 data. Does Mandelbrot's fractal architecture outforecast Engle's conditional variance framework? Part 3 of a eight-part series leading to a native MQL5 library and Expert Advisor.

The article presents an MQL5 Expert Advisor that adapts the Ford–Fulkerson max-flow method into a liquidity-capacity filter. Market structures—Swing Highs/Lows, Fair Value Gaps, Order Blocks, and Liquidity Pools—form a directed graph with edge capacities from volume, price reaction, distance, and structure quality. Maximum flow qualifies ICT setups, filters weak paths, and drives dynamic position sizing for a consistent, two-stage decision process.

With the multifractal parameters from Part 2 in hand, this article builds the full MMAR process. We construct the multiplicative cascade for trading time, generate Fractional Brownian Motion via Davies-Harte FFT, and combine both into X(t) = B_H[theta(t)]. A 100-path Monte Carlo simulation produces the volatility forecast, which we then pit against GARCH on the same EURUSD M5 data. Does Mandelbrot's fractal architecture outforecast Engle's conditional variance framework? Part 3 of a eight-part series leading to a native MQL5 library and Expert Advisor.

We extend the canvas drawing layer from the previous part with seven new categories of multi-anchor analytical drawing tools, covering three channel variants, three pitchfork variants, three Gann tools, and the six Fibonacci tools. We work through how each tool encodes its geometry on the canvas, how derived handles let users reshape compound shapes coherently, and how shared helpers handle ray clipping, scanline filling, and anti-aliased arc rendering. By the end, we will have a full set of analytical drawing tools that live on the same interactive canvas alongside the basic line tools from the previous part.

A GPH‑based estimator for d, the key ARFIMA parameter, is added to MicroStructure_Foundation.mqh. GPHEstimator() computes d via log‑periodogram regression, while PopulateARFIMAAnalysis() stores d with an R² confidence score and validates the theoretical relationship H = d + 0.5. An empirical study on 72 US100 M1 sessions confirms pooled d = −0.006, consistent with the random walk boundary established in Part 2.

You have probably already heard about pointers when it comes to programming. But did you know that we can use this kind of data here in MQL5? Of course, this must be done in a way that keeps us in control and avoids strange program behavior during execution. Still, because this is a feature with a very specific purpose and aimed at particular kinds of tasks, it is rare to hear anyone discuss what a pointer is and how to use it in MQL5.

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.

We resume the topic of optimization by the scientific community. CoSO should not be viewed as a ready-made solution, but as a promising research platform. With proper development, CoSO can find its niche in tasks where adaptability and resilience to change are important, and computation time is not critical.

MQL5's file system operates within a strict sandbox. Understanding its access flags and path resolution rules is the foundation of any reliable export pipeline. This article builds a CCSVExporter class that handles file creation, safe appending, and error recovery. It also covers CSV parsing, field tokenization, concurrent access conflicts, and write-buffering strategies for high-frequency optimization runs.

AlternativeBarsViewer is a subwindow indicator that renders all ten alternative bar types as color‑coded candles using the same CBarConstructor hierarchy as BarBuilderEA, ensuring identical bars. It supports three data sources (real ticks, synthetic OHLC ticks, or the EA's CSV) and two render modes (TIME and INDEX) toggleable at runtime. Degenerate bars are highlighted and summarized on a compact panel, enabling live calibration without leaving the terminal.

Build a restart-aware virtual protection layer on top of the SQLite persistence from Part 1. The EA reconstructs hidden stop-loss and take-profit after restart, verifies current price against recovered exits, and closes or continues positions accordingly. The result is a consistent recovery path that detects managed positions and sustains safe runtime management.

This article presents CTradeJournal, a self-contained MQL5 class for live tracking of open positions at tick frequency. It maintains MAE, MFE, and initial risk in money, calculates the R-multiple when a position closes, and writes a complete CSV record. The text explains the design choices, provides the implementation, and shows simple EA integration so you can analyze entries, stop placement, and outcome distribution.