Articles on data analysis and statistics in MQL5
Articles on mathematical models and laws of probability are interesting for many traders. Mathematics is the basis of technical indicators, and statistics is required to analyze trading results and develop strategies.
Read about the fuzzy logic, digital filters, market profile, Kohonen maps, neural gas and many other tools that can be used for trading.
Add a new article
You are missing trading opportunities:
- Free trading apps
- Over 8,000 signals for copying
- Economic news for exploring financial markets
Registration
Log in
You agree to website policy and terms of use
If you do not have an account, please register
Price Action Analysis Toolkit Development (Part 9): External Flow
This article explores a new dimension of analysis using external libraries specifically designed for advanced analytics. These libraries, like pandas, provide powerful tools for processing and interpreting complex data, enabling traders to gain more profound insights into market dynamics. By integrating such technologies, we can bridge the gap between raw data and actionable strategies. Join us as we lay the foundation for this innovative approach and unlock the potential of combining technology with trading expertise.
Blood inheritance optimization (BIO)
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.
Developing a Replay System (Part 75): New Chart Trade (II)
In this article, we will talk about the C_ChartFloatingRAD class. This is what makes Chart Trade work. However, the explanation does not end there. We will complete it in the next article, as the content of this article is quite extensive and requires deep understanding. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Developing a Replay System (Part 37): Paving the Path (I)
In this article, we will finally begin to do what we wanted to do much earlier. However, due to the lack of "solid ground", I did not feel confident to present this part publicly. Now I have the basis to do this. I suggest that you focus as much as possible on understanding the content of this article. I mean not simply reading it. I want to emphasize that if you do not understand this article, you can completely give up hope of understanding the content of the following ones.
Developing a Replay System (Part 51): Things Get Complicated (III)
In this article, we will look into one of the most difficult issues in the field of MQL5 programming: how to correctly obtain a chart ID, and why objects are sometimes not plotted on the chart. The materials presented here are for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Neural networks made easy (Part 41): Hierarchical models
The article describes hierarchical training models that offer an effective approach to solving complex machine learning problems. Hierarchical models consist of several levels, each of which is responsible for different aspects of the task.
Low-Frequency Quantitative Strategies in Metatrader 5: (Part 1) Setting Up An OLAP-Friendly Data Store
The article outlines a practical data pipeline for quantitative analysis based on Parquet storage, Hive-style partitions, and DuckDB. It details migrating selected SQLite tables to Parquet, structuring market data by source, symbol, timeframe, and date, and querying it with SQL window functions. A Golden Cross example illustrates cross‑symbol evaluation of forward returns. Accompanying Python scripts handle data download, conversion, and execution.
Data Science and ML (Part 35): NumPy in MQL5 – The Art of Making Complex Algorithms with Less Code
NumPy library is powering almost all the machine learning algorithms to the core in Python programming language, In this article we are going to implement a similar module which has a collection of all the complex code to aid us in building sophisticated models and algorithms of any kind.
Hidden Markov Models in Machine Learning-Based Trading Systems
Hidden Markov Models (HMMs) are a powerful class of probabilistic models designed to analyze sequential data, where observed events depend on some sequence of unobserved (hidden) states that form a Markov process. The main assumptions of HMM include the Markov property for hidden states, meaning that the probability of transition to the next state depends only on the current state, and the independence of observations given knowledge of the current hidden state.
Developing a Replay System — Market simulation (Part 19): Necessary adjustments
Here we will prepare the ground so that if we need to add new functions to the code, this will happen smoothly and easily. The current code cannot yet cover or handle some of the things that will be necessary to make meaningful progress. We need everything to be structured in order to enable the implementation of certain things with the minimal effort. If we do everything correctly, we can get a truly universal system that can very easily adapt to any situation that needs to be handled.
Developing a Replay System (Part 54): The Birth of the First Module
In this article, we will look at how to put together the first of a number of truly functional modules for use in the replay/simulator system that will also be of general purpose to serve other purposes. We are talking about the mouse module.
Multi-Threaded Trading Robot with Machine Learning: From Concept to Implementation
The article presents a step-by-step development of a multi-threaded trading robot with machine learning in Python and MetaTrader 5. The system architecture is considered – from data collection and creation of technical indicators to training XGBoost models with portfolio risk management. The implementation of data augmentation, feature clustering via Gaussian Mixture Models, and flow coordination for parallel trading of multiple currency pairs is described in detail.
Swap Arbitrage in Forex: Building a Synthetic Portfolio and Generating a Consistent Swap Flow
Do you want to know how to benefit from the difference in interest rates? This article considers how to use swap arbitrage in Forex to earn stable profit every night, creating a portfolio that is resistant to market fluctuations.
Category Theory in MQL5 (Part 23): A different look at the Double Exponential Moving Average
In this article we continue with our theme in the last of tackling everyday trading indicators viewed in a ‘new’ light. We are handling horizontal composition of natural transformations for this piece and the best indicator for this, that expands on what we just covered, is the double exponential moving average (DEMA).
Population optimization algorithms: Simulated Annealing (SA) algorithm. Part I
The Simulated Annealing algorithm is a metaheuristic inspired by the metal annealing process. In the article, we will conduct a thorough analysis of the algorithm and debunk a number of common beliefs and myths surrounding this widely known optimization method. The second part of the article will consider the custom Simulated Isotropic Annealing (SIA) algorithm.
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (I)
This discussion delves into the challenges encountered when working with large codebases. We will explore the best practices for code organization in MQL5 and implement a practical approach to enhance the readability and scalability of our Trading Administrator Panel source code. Additionally, we aim to develop reusable code components that can potentially benefit other developers in their algorithm development. Read on and join the conversation.
Chaos Game Optimization (CGO)
The article presents a new metaheuristic algorithm, Chaos Game Optimization (CGO), which demonstrates a unique ability to maintain high efficiency when dealing with high-dimensional problems. Unlike most optimization algorithms, CGO not only does not lose, but sometimes even increases performance when scaling a problem, which is its key feature.
Redefining MQL5 and MetaTrader 5 Indicators
An innovative approach to collecting indicator information in MQL5 enables more flexible and streamlined data analysis by allowing developers to pass custom inputs to indicators for immediate calculations. This approach is particularly useful for algorithmic trading, as it provides enhanced control over the information processed by indicators, moving beyond traditional constraints.
How to implement AutoARIMA forecasting in MQL5
This article presents an MQL5 implementation of AutoARIMA that builds ARIMA models without manual tuning. It estimates d via a variance-based heuristic, fits ARMA(p,q) by gradient optimization with Adam, and selects p and q using AICc. The code returns a one-step-ahead price forecast by differencing, model estimation, and integration back to price level, ready to call on a Close series.
Eagle Strategy (ES)
Eagle Strategy is an algorithm that mimics the eagle's two-phase hunting strategy: global search via Levy flights using Mantegna method, alternating with intense local exploitation using the firefly algorithm, a mathematically sound approach to balancing exploration and exploitation, and a bioinspired concept that combines two natural phenomena into a single computational method.
Introduction to MQL5 (Part 31): Mastering API and WebRequest Function in MQL5 (V)
Learn how to use WebRequest and external API calls to retrieve recent candle data, convert each value into a usable type, and save the information neatly in a table format. This step lays the groundwork for building an indicator that visualizes the data in candle format.
Artificial Bee Hive Algorithm (ABHA): Tests and results
In this article, we will continue exploring the Artificial Bee Hive Algorithm (ABHA) by diving into the code and considering the remaining methods. As you might remember, each bee in the model is represented as an individual agent whose behavior depends on internal and external information, as well as motivational state. We will test the algorithm on various functions and summarize the results by presenting them in the rating table.
Creating Custom Indicators in MQL5 (Part 10): Enhancing the Footprint Chart with Per-Bar Volume Sentiment Information Box
The article enhances an MQL5 footprint indicator with a compact box above each candle that summarizes net delta, total volume, and buy/sell percentages. We implement supersampled anti‑aliased rendering, rounded corners via arc and quadrilateral rasterization, and per‑pixel alpha compositing. Supporting utilities include ARGB conversion, scanline fills, and box‑filter downsampling. The box delivers fast sentiment reads that stay legible across zoom levels.
Comet Tail Algorithm (CTA)
In this article, we will look at the Comet Tail Optimization Algorithm (CTA), which draws inspiration from unique space objects - comets and their impressive tails that form when approaching the Sun. The algorithm is based on the concept of the motion of comets and their tails, and is designed to find optimal solutions in optimization problems.
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (II): Modularization
In this discussion, we take a step further in breaking down our MQL5 program into smaller, more manageable modules. These modular components will then be integrated into the main program, enhancing its organization and maintainability. This approach simplifies the structure of our main program and makes the individual components reusable in other Expert Advisors (EAs) and indicator developments. By adopting this modular design, we create a solid foundation for future enhancements, benefiting both our project and the broader developer community.
Coral Reefs Optimization (CRO)
The article presents a comprehensive analysis of the Coral Reef Optimization (CRO) algorithm, a metaheuristic method inspired by the biological processes of coral reef formation and development. The algorithm models key aspects of coral evolution: broadcast spawning, brooding, larval settlement, asexual reproduction, and competition for limited reef space. Particular attention is paid to the improved version of the algorithm.
Population optimization algorithms: Evolution Strategies, (μ,λ)-ES and (μ+λ)-ES
The article considers a group of optimization algorithms known as Evolution Strategies (ES). They are among the very first population algorithms to use evolutionary principles for finding optimal solutions. We will implement changes to the conventional ES variants and revise the test function and test stand methodology for the algorithms.
Feature Engineering With Python And MQL5 (Part II): Angle Of Price
There are many posts in the MQL5 Forum asking for help calculating the slope of price changes. This article will demonstrate one possible way of calculating the angle formed by the changes in price in any market you wish to trade. Additionally, we will answer if engineering this new feature is worth the extra effort and time invested. We will explore if the slope of the price can improve any of our AI model's accuracy when forecasting the USDZAR pair on the M1.
Statistical Arbitrage Through Cointegrated Stocks (Part 7): Scoring System 2
This article describes two additional scoring criteria used for selection of baskets of stocks to be traded in mean-reversion strategies, more specifically, in cointegration based statistical arbitrage. It complements a previous article where liquidity and strength of the cointegration vectors were presented, along with the strategic criteria of timeframe and lookback period, by including the stability of the cointegration vectors and the time to mean reversion (half-time). The article includes the commented results of a backtest with the new filters applied and the files required for its reproduction are also provided.
Developing a Replay System (Part 71): Getting the Time Right (IV)
In this article, we will look at how to implement what was shown in the previous article related to our replay/simulation service. As in many other things in life, problems are bound to arise. And this case was no exception. In this article, we continue to improve things. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (III): Communication Module
Join us for an in-depth discussion on the latest advancements in MQL5 interface design as we unveil the redesigned Communications Panel and continue our series on building the New Admin Panel using modularization principles. We'll develop the CommunicationsDialog class step by step, thoroughly explaining how to inherit it from the Dialog class. Additionally, we'll leverage arrays and ListView class in our development. Gain actionable insights to elevate your MQL5 development skills—read through the article and join the discussion in the comments section!
Developing a Replay System — Market simulation (Part 12): Birth of the SIMULATOR (II)
Developing a simulator can be much more interesting than it seems. Today we'll take a few more steps in this direction because things are getting more interesting.
Royal Flush Optimization (RFO)
The original Royal Flush Optimization algorithm offers a new approach to solving optimization problems, replacing the classic binary coding of genetic algorithms with a sector-based approach inspired by poker principles. RFO demonstrates how simplifying basic principles can lead to an efficient and practical optimization method. The article presents a detailed analysis of the algorithm and test results.
Big Bang - Big Crunch (BBBC) algorithm
The article presents the Big Bang - Big Crunch method, which has two key phases: cyclic generation of random points and their compression to the optimal solution. This approach combines exploration and refinement, allowing us to gradually find better solutions and open up new optimization opportunities.
Data Science and ML (Part 39): News + Artificial Intelligence, Would You Bet on it?
News drives the financial markets, especially major releases like Non-Farm Payrolls (NFPs). We've all witnessed how a single headline can trigger sharp price movements. In this article, we dive into the powerful intersection of news data and Artificial Intelligence.
Population optimization algorithms: Intelligent Water Drops (IWD) algorithm
The article considers an interesting algorithm derived from inanimate nature - intelligent water drops (IWD) simulating the process of river bed formation. The ideas of this algorithm made it possible to significantly improve the previous leader of the rating - SDS. As usual, the new leader (modified SDSm) can be found in the attachment.
Market Simulation (Part 23): Position View (I)
The content we will cover from this point on is much more complex in terms of theory and concepts. I will try to make the material as simple as possible. The programming part itself is quite simple and straightforward. But if you do not understand the theory behind it, you will be left with no practical basis at all for refining or adapting the replay/simulation system to tasks different from the ones I am going to show. I do not want you merely to compile and use the code I present. I want you to learn, understand and, if possible, be able to create something even better.
Population optimization algorithms: Changing shape, shifting probability distributions and testing on Smart Cephalopod (SC)
The article examines the impact of changing the shape of probability distributions on the performance of optimization algorithms. We will conduct experiments using the Smart Cephalopod (SC) test algorithm to evaluate the efficiency of various probability distributions in the context of optimization problems.
Data Science and ML (Part 34): Time series decomposition, Breaking the stock market down to the core
In a world overflowing with noisy and unpredictable data, identifying meaningful patterns can be challenging. In this article, we'll explore seasonal decomposition, a powerful analytical technique that helps separate data into its key components: trend, seasonal patterns, and noise. By breaking data down this way, we can uncover hidden insights and work with cleaner, more interpretable information.
MetaTrader 5 Machine Learning Blueprint (Part 13): Implementing Bet Sizing in MQL5
We build a production MQL5 bet‑sizing toolkit: utilities, snippets, and user‑level functions that mirror the Python originals. The methods cover probability‑to‑size mapping with overlap correction, dynamic forecast‑price sizing (calibrated sigmoid/power with limit price), occupancy‑based budgeting, and mixture‑model reserve sizing (EF3M). The result is a signed [−1, ..., 1] position plus diagnostics you can plug directly into order logic.