MQL4 and MQL5 Programming Articles

Today, we explore another component of ALGLIB, leveraging its mathematical capabilities to develop a Polynomial Regression Channel indicator. By the end of this discussion, you will gain practical insights into indicator development using the MQL5 Standard Library, along with a fully functional, mathematically driven indicator source code.

This article explores the powerful role of matrix factorization in algorithmic trading, specifically within MQL5 applications. From regression models to multi-target classifiers, we walk through practical examples that demonstrate how easily these techniques can be integrated using built-in MQL5 functions. Whether you're predicting price direction or modeling indicator behavior, this guide lays a strong foundation for building intelligent trading systems using matrix methods.

What we create today will be difficult to understand. Therefore, in this article I will only talk about the initial stage. Please read this article carefully, it is an important prerequisite before we proceed to the next step. The purpose of this material is purely didactic as we will only study and master the presented concepts, without practical application.

High probability Setups are well known in our trading community, but regrettably they are not well-defined. In this article, we will aim to find an empirical and algorithmic way of defining exactly what is a high probability setup, identifying and exploiting them. By using Gradient Boosting Trees, we demonstrated how the reader can improve the performance of an arbitrary trading strategy and better communicate the exact job to be done to our computer in a more meaningful and explicit manner.

In the previous article, we made some fixes and added tests to our replication system to ensure the best possible stability. We also started creating and using a configuration file for this system.

We are close to completing this challenge. However, before we begin, I want you to try to understand these two articles—this one and the previous one. That way, you will truly understand the next article, in which I will cover exclusively the part related to MQL5 programming. But I will also try to make it understandable. If you do not understand these last two articles, it will be difficult for you to understand the next one, because the material accumulates. The more things there are to do, the more you need to create and understand in order to achieve the goal.

As already explained in the theoretical part, when working with neural networks we need to use linear regressions and derivatives. Why? The reason is that linear regression is one of the simplest formulas in existence. Essentially, linear regression is just an affine function. However, when we talk about neural networks, we are not interested in the effects of direct linear regression. We are interested in the equation that generates this line. We are not that interested in the line created. Do you know the main equation that we need to understand? If not, I recommend reading this article to understanding it.

This article explores the powerful role of matrix factorization in algorithmic trading, specifically within MQL5 applications. From regression models to multi-target classifiers, we walk through practical examples that demonstrate how easily these techniques can be integrated using built-in MQL5 functions. Whether you're predicting price direction or modeling indicator behavior, this guide lays a strong foundation for building intelligent trading systems using matrix methods.

This article builds a Hull Moving Average Momentum indicator in MQL5 by combining raw price momentum with Hull MA smoothing. We compute momentum as the close-to-close difference over a user-defined length, form 2×Fast WMA − Slow WMA, then apply a final WMA with a square‑root period. The implementation covers inputs, buffers, warm-up/recalculation, and visualization with a color-coded line and zero-line filling, helping interpret positive/negative momentum without treating zero crossings as signals.

The DeMarker Oscillator and the Envelope indicator are momentum and support/resistance tools that can be paired when developing an Expert Advisor. We therefore examine on a pattern by pattern basis what could be of use and what potentially avoid. We are using, as always, a wizard assembled Expert Advisor together with the Patterns-Usage functions that are built into the Expert Signal Class.

The article discusses the non-parametric HSIC (Hilbert-Schmidt Independence Criterion) statistical test designed to identify linear and non-linear dependencies in data. Implementations of two algorithms for calculating HSIC in the MQL5 language are proposed: the exact permutation test and the gamma approximation. The method efficiency is demonstrated on synthetic data modeling a non-linear relationship between features and the target variable.

An empirical study of Larry Williams’ Trade Day of the Week concept, showing how time-based market bias can be measured, tested, and applied using MQL5. This article presents a practical framework for analyzing win rates and performance across trading days to improve short-term trading systems.

We implement Ehlers-style DSP filters in a single reusable MQL5 library and use it to build two indicators. The Roofing Filter applies a 2‑pole high‑pass followed by a Super Smoother to isolate the tradeable 10–48‑bar band. The Even Better Sinewave normalizes the wave to about ±1, oscillating in cycle regimes and railing in trends, so you can read cycles and detect regime shifts in charts and EAs.

We continue our experiment that aims to examine the behavior of population optimization algorithms in the context of their ability to efficiently escape local minima when population diversity is low and reach global maxima. Research results are provided.

There is a powerful and pervasive force quietly corrupting the collective efforts of our community to build reliable trading strategies that employ AI in any shape or form. This article establishes that part of the problems we face, are rooted in blind adherence to "best practices". By furnishing the reader with simple real-world market-based evidence, we will reason to the reader why we must refrain from such conduct, and rather adopt domain-bound best practices if our community should stand any chance of recovering the latent potential of AI.

Automated trading systems often focus heavily on signal generation while neglecting the mechanisms required to protect capital during periods of stress. This article presents an Equity Governance Framework in MQL5 that monitors drawdown conditions, evaluates equity pressure, and dynamically controls trading activity through a state-driven risk management model. By combining drawdown analysis, cooldown logic, trade authorization, and execution restrictions, the framework demonstrates how trading discipline can be engineered directly into code using a modular and extensible architecture.

A hybrid exit engine for MQL5 replaces static TPs with CRT-derived structural levels. The CRT_ProfitConserve class secures a partial at the first level and then trails the remaining position by structural anchors rather than fixed pips. The article walks through the class API, essential methods, and example usage in EAs, providing a clear path to embed CRT-based exits into existing strategies.

Replay buffers in Reinforcement Learning are particularly important with off-policy algorithms like DQN or SAC. This then puts the spotlight on the sampling process of this memory-buffer. While default options with SAC, for instance, use random selection from this buffer, Prioritized Experience Replay buffers fine tune this by sampling from the buffer based on a TD-score. We review the importance of Reinforcement Learning, and, as always, examine just this hypothesis (not the cross-validation) in a wizard assembled Expert Advisor.

This article is somewhat different from my earlier publications. In this article, we will talk about an alternative representation of time series. Piecewise linear representation of time series is a method of approximating a time series using linear functions over small intervals.

The article replaces clock-based sampling with López de Prado's alternative bar types and provides two aligned implementations: a unified Python module for batch tick histories and an object‑oriented MQL5 library for live EAs. It covers Parquet/Dask infrastructure, data cleaning, and a single API. Practical issues are solved explicitly: zero‑tick time‑bar filtering, imbalance threshold initialization, EWM state persistence, and parity between Python and MQL5 outputs.

This MQL5 engine applies configurable profit ladders in R‑multiples to manage partial closes reliably. It prevents stranded remainders by rounding to lot step, computes close percentages from the original entry volume, and moves the stop to breakeven when configured. A supported filling mode is chosen automatically, and the download includes seven include files, a demo EA, and a verification script.

In this article, we will explore and implement, methods for assessing model quality that utilize a single dataset as both training and validation sets.

Live performance often drifts from backtests because of execution friction. We introduce an MQL5 diagnostic EA that records entry and exit slippage, asymmetry, observed spread, requotes, and per-leg latency, using a precise probe mode and an approximate passive mode, and writes every sample to CSV. Use the results to distinguish strategy issues from execution effects across your terminal, network, broker, and liquidity.

In this article we will look at a very interesting and quite challenging programming concept, although it should be treated with great caution, as its misuse or misunderstanding can turn relatively simple programs into something unnecessarily complex. But when used correctly and adapted perfectly to equally suitable situations, recursion becomes an excellent ally in solving problems that would otherwise be much more laborious and time-consuming. The materials presented here are intended for educational purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

This article takes a fresh perspective on a hidden, geometric source of error that quietly shapes every prediction your models make. By rethinking how we measure and apply machine learning forecasts in trading, we reveal how this overlooked perspective can unlock sharper decisions, stronger returns, and a more intelligent way to work with models we thought we already understood.

The ProSpread seasonality index indicator with a Moving Average is a technical analysis tool that identifies seasonal patterns in price movements, analyzes price behavior during specific trading hours and is able to work with either a single instrument or a spread between two assets. It also visualizes the statistical probability of directional movements.

This article presents a complete RL trading pipeline for XAUUSD: a supervised signal baseline with triple-barrier labels, PPO training, purged walk-forward validation with embargo, multi-seed checks, and contract-guarded deployment with normalization. It includes runnable code for data validation, features, environment, training, and broker‑based reconciliation. The live demo over 763 closed trades showed no statistically significant edge, and the methods highlight where information and costs, not architecture, set performance limits.

In this article we make the case for pairing the Discrete Fourier Transform with a Spiking Neural Network in a Trading Robot. The Fourier Transform helps represent data as oscillations instead of its raw values. To govern how we interpret these cycles, we engage a Spiking Neural Network that unlike regular networks, uses time dependent electrical charges to accumulate potential and only "spike" when a target threshold is met. Combining these two engines allows us better control on the timing of discrete market movements, that in theory should give us entry signals with rigorous mathematical confirmation.

In this article, we will perform Enhanced Data Visualization by going beyond basic charts by incorporating features like interactivity, layered data, and dynamic elements, enabling traders to explore trends, patterns, and correlations more effectively.