Articles on data analysis and statistics in MQL5

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.

In this article, we implement an adaptive Linear Regression Channel system in MQL5 that automatically calculates the regression line and standard deviation channel over a user-defined period, only activates when the slope exceeds a minimum threshold to confirm a clear trend, and dynamically recreates or extends the channel when the price breaks out by a configurable percentage of channel width.

Sockets. Do you know what they are for or how to use them in MetaTrader 5? If the answer is no, let's start by studying them. In today's article, we'll cover the basics. Since there are several ways to do the same thing, and we are always interested in the result, I want to show that there is indeed a simple way to transfer data from MetaTrader 5 to other programs, such as Excel. However, the main idea is not to transfer data from MetaTrader 5 to Excel, but the opposite, that is, to transfer data from Excel or any other program to MetaTrader 5.

This article presents the Multi‑Timeframe Visual Analyzer, an MQL5 Expert Advisor that reconstructs and overlays higher‑timeframe candles directly onto your active chart. It explains the implementation, key inputs, and practical outcomes, supported by an animated demo and chart examples showing instant toggling, multi‑timeframe confirmation, and configurable alerts. Read on to see how this tool can make chart analysis faster, clearer, and more efficient.

This article is intended for algorithmic traders, quantitative analysts, and MQL5 developers interested in enhancing their understanding of candlestick pattern recognition through practical implementation. It provides an in‑depth exploration of the CandlePatternSearch.mq5 Expert Advisor—a complete framework for detecting, visualizing, and monitoring classical candlestick formations in MetaTrader 5. Beyond a line‑by‑line review of the code, the article discusses architectural design, pattern detection logic, GUI integration, and alert mechanisms, illustrating how traditional price‑action analysis can be automated efficiently.

In this article, we look at the process of developing a table model in MQL5 using the MVC (Model-View-Controller) architectural pattern to separate data logic, presentation, and control, enabling structured, flexible, and scalable code. We consider implementation of classes for building a table model, including the use of linked lists for storing data.

Many traders struggle to identify genuine reversals. This article presents an EA that combines RVGI, CCI (±100), and an SMA trend filter to produce a single clear reversal signal. The EA includes an on-chart panel, configurable alerts, and the full source file for immediate download and testing.

Many people, especially non=programmers, find it very difficult to transfer information between MetaTrader 5 and other programs. One such program is Excel. Many use Excel as a way to manage and maintain their risk control. It is an excellent program and easy to learn, even for those who are not VBA programmers. Here we will look at how to establish a connection between MetaTrader 5 and Excel (a very simple method).

In this part, we focus on how to merge real-time market feedback—such as live trade outcomes, volatility changes, and liquidity shifts—with adaptive model learning to maintain a responsive and self-improving trading system.

I present to you my new population optimization algorithm - Blood Inheritance Optimization (BIO), inspired by the human blood group inheritance system. In this algorithm, each solution has its own "blood type" that determines the way it evolves. Just as in nature where a child's blood type is inherited according to specific rules, in BIO new solutions acquire their characteristics through a system of inheritance and mutations.

Just because ticks are constantly flowing in doesn’t mean every moment is an opportunity to trade. Today, we take an in-depth study into the art of timing—focusing on developing a time isolation algorithm to help traders identify and trade within their most favorable market windows. Cultivating this discipline allows retail traders to synchronize more closely with institutional timing, where precision and patience often define success. Join this discussion as we explore the science of timing and selective trading through the analytical capabilities of MQL5.

We will use a quantum computer from IBM to discover all price movement options. Sounds like science fiction? Welcome to the world of quantum computing for trading!

This article helps new community members search for and discover their own candlestick patterns. Describing these patterns can be daunting, as it requires manually searching and creatively identifying improvements. Here, we introduce the engulfing candlestick pattern and show how it can be enhanced for more profitable trading applications.

The article presents a new metaheuristic optimization Circle Search Algorithm (CSA) based on the geometric properties of a circle. The algorithm uses the principle of moving points along tangents to find the optimal solution, combining the phases of global exploration and local exploitation.

Discover how to fix a critical flaw in financial machine learning that causes overfit models and poor live performance—label concurrency. When using the triple-barrier method, your training labels overlap in time, violating the core IID assumption of most ML algorithms. This article provides a hands-on solution through sample weighting. You will learn how to quantify temporal overlap between trading signals, calculate sample weights that reflect each observation's unique information, and implement these weights in scikit-learn to build more robust classifiers. Learning these essential techniques will make your trading models more robust, reliable and profitable.

Global market sessions shape the rhythm of the trading day, and understanding their overlap is vital to timing entries and exits. In this article, we’ll build an interactive trading sessions  EA that brings those global hours to life directly on your chart. The EA automatically plots color‑coded rectangles for the Asia, Tokyo, London, and New York sessions, updating in real time as each market opens or closes. It features on‑chart toggle buttons, a dynamic information panel, and a scrolling ticker headline that streams live status and breakout messages. Tested on different brokers, this EA combines precision with style—helping traders see volatility transitions, identify cross‑session breakouts, and stay visually connected to the global market’s pulse.

In this article, we propose a scoring system for mean-reversion strategies based on statistical arbitrage of cointegrated stocks. The article suggests criteria that go from liquidity and transaction costs to the number of cointegration ranks and time to mean-reversion, while taking into account the strategic criteria of data frequency (timeframe) and the lookback period for cointegration tests, which are evaluated before the score ranking properly. The files required for the reproduction of the backtest are provided, and their results are commented on as well.

Imagine transforming the traditional EA or indicator input properties into a real-time, on-chart control interface. This discussion builds upon our foundational work in the Market Periods Synchronizer indicator, marking a significant evolution in how we visualize and manage higher-timeframe (HTF) market structures. Here, we turn that concept into a fully interactive utility—a dashboard that brings dynamic control and enhanced multi-period price action visualization directly onto the chart. Join us as we explore how this innovation reshapes the way traders interact with their tools.

In this discussion, we contrast the classical approach to time series cross-validation with modern alternatives that challenge its core assumptions. We expose key blind spots in the traditional method—especially its failure to account for evolving market conditions. To address these gaps, we introduce Effective Memory Cross-Validation (EMCV), a domain-aware approach that questions the long-held belief that more historical data always improves performance.

In this article, we will start creating the C_Orders class to be able to send orders to the trading server. We'll do this little by little, as our goal is to explain in detail how this will happen through the messaging system.

Fibonacci tools are among the most popular instruments used by technical analysts. In this article, we’ll build an Interactive Fibonacci EA that draws retracement and extension levels that react dynamically to price movement, delivering real‑time alerts, stylish lines, and a scrolling news‑style headline. Another key advantage of this EA is flexibility; you can manually type the high (A) and low (B) swing values directly on the chart, giving you exact control over the market range you want to analyze.

The article introduces the dialectical algorithm (DA), a new global optimization method inspired by the philosophical concept of dialectics. The algorithm exploits a unique division of the population into speculative and practical thinkers. Testing shows impressive performance of up to 98% on low-dimensional problems and overall efficiency of 57.95%. The article explains these metrics and presents a detailed description of the algorithm and the results of experiments on different types of functions.

For many traders, it's a familiar pain point: watching a trade come within a whisker of your profit target, only to reverse and hit your stop-loss. Or worse, seeing a trailing stop close you out at breakeven before the market surges toward your original target. This article focuses on using multiple entries at different Reward-to-Risk Ratios to systematically secure gains and reduce overall risk exposure.

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.

In this article, we explore a powerful MQL5 tool that let's you test any price level you desire with just one click. Simply enter your chosen level and press analyze, the EA instantly scans historical data, highlights every touch and breakout on the chart, and displays statistics in a clean, organized dashboard. You'll see exactly how often price respected or broke through your level, and whether it behaved more like support or resistance. Continue reading to explore the detailed procedure.