Articles on data analysis and statistics in MQL5

In this article, we enhance the 3D binomial distribution graphing tool in MQL5 by adding a segmented 3D curve for improved depth perception of the probability mass function, integrating pan mode for view target shifting, and implementing an interactive view cube with hover zones and animations for quick orientation changes. We incorporate clickable sub-zones on the view cube for faces, edges, and corners to animate camera transitions to standard views, while maintaining switchable 2D/3D modes, real-time updates, and customizable parameters for immersive probabilistic analysis in trading.

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.

Maximum drawdown is one number that hides what really matters: how often an equity curve declines, how long it stays below a previous peak, and how quickly it recovers. This article builds a native MQL5 tool that reconstructs the underwater curve, breaks it into individual drawdown episodes (depth, duration, recovery time), computes the Ulcer Index, Pain Index, and Recovery Factor, and combines them into a single resilience grade with practical recommendations. No external libraries, no Python, no AI.

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.

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.

MetaTrader 5 provides rich performance data but limited structural analysis. This article shows how to export results to CSV from MQL5 and build five Python visualizations that expose cross-asset parameter consistency, the lag‑versus‑noise trade-off, walk‑forward decay, drawdown depth and duration, and intraday hour‑by‑day clusters. A unified automation module runs the full pipeline on any new export to deliver repeatable diagnostics.

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.

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.

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.

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

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.

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.

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.

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.

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.

How can a trader manage capital? How can a trader and investor keep track of expenses, income, assets, and liabilities? I am not just going to introduce you to accounting software; I am going to show you a tool that might become your reliable financial navigator in the stormy sea of trading.

We will build a biologically correct system of neurons for time series forecasting. The introduction of a plasma-like environment into the neural network architecture creates a kind of "collective intelligence," where each neuron influences the system's operation not only through direct connections, but also through long-range electromagnetic interactions. Let's see how the neural brain modeling system will perform in the market.

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 will consider the Artificial Bee Hive Algorithm (ABHA) developed in 2009. The algorithm is aimed at solving continuous optimization problems. We will look at how ABHA draws inspiration from the behavior of a bee colony, where each bee has a unique role that helps them find resources more efficiently.

Continuing tries to decipher price movements... What about linguistic analysis of the "market dictionary" that we get by converting the binary price code to BIP39? In this article, we will delve into an innovative approach to exchange data analysis and consider how modern natural language processing techniques can be applied to the market language.

Finally, our Chart Trade indicator starts interacting with the EA, allowing information to be transferred interactively. Therefore, in this article, we will improve the indicator, making it functional enough to be used together with any EA. This will allow us to access the Chart Trade indicator and work with it as if it were actually connected with an EA. But we will do it in a much more interesting way than before.

In this article, we will begin to address the issue of tick excess that can impact application performance when using real data. This excess often interferes with the correct timing required to construct a one-minute bar in the appropriate window.

The article is devoted to the AMO algorithm, which models the seasonal migration of animals in search of optimal conditions for life and reproduction. The main features of AMO include the use of topological neighborhood and a probabilistic update mechanism, which makes it easy to implement and flexible for various optimization tasks.

Most people who want or dream of learning to program don't actually have a clue what they're doing. Their activity consists of trying to create things in a certain way. However, programming is not about tailoring suitable solutions. Doing it this way can create more problems than solutions. Here we will be doing something more advanced and therefore different.

The article presents an optimization algorithm based on the patterns of constructing spiral trajectories in nature, such as mollusk shells - the spiral dynamics optimization (SDO) algorithm. I have thoroughly revised and modified the algorithm proposed by the authors. The article will consider the necessity of these changes.

Successful Restaurateur Algorithm (SRA) is an innovative optimization method inspired by restaurant business management principles. Unlike traditional approaches, SRA does not discard weak solutions, but improves them by combining with elements of successful ones. The algorithm shows competitive results and offers a fresh perspective on balancing exploration and exploitation in optimization problems.

In this article, we present the implementation of several ensemble learning methods in MQL5 and examine their effectiveness across different scenarios.

Today, we are harnessing the capabilities of MQL5 to utilize external resources—such as images in the BMP format—to create a uniquely styled home interface for the Trading Administrator Panel. The strategy demonstrated here is particularly useful when packaging multiple resources, including images, sounds, and more, for streamlined distribution. Join us in this discussion as we explore how these features are implemented to deliver a modern and visually appealing interface for our New_Admin_Panel EA.

We add supersampling‑driven anti‑aliasing and high‑resolution rendering to the MQL5 canvas dashboard, then downsample to the target size. The article implements rounded rectangle fills and borders, rounded triangle arrows, and a custom scrollbar with theming for the stats and text panels. These tools help you build smoother, more legible UI components in MetaTrader 5.

We will solve the chart ID problem and at the same time we will begin to provide the user with the ability to use a personal template for the analysis and simulation of the desired asset. The materials presented here are for didactic purposes only and should in no way be considered as an application for any purpose other than studying and mastering the concepts presented.