
Gain an Edge Over Any Market (Part III): Visa Spending Index
In the world of big data, there are millions of alternative datasets that hold the potential to enhance our trading strategies. In this series of articles, we will help you identify the most informative public datasets.

Creating a Trading Administrator Panel in MQL5 (Part I): Building a Messaging Interface
This article discusses the creation of a Messaging Interface for MetaTrader 5, aimed at System Administrators, to facilitate communication with other traders directly within the platform. Recent integrations of social platforms with MQL5 allow for quick signal broadcasting across different channels. Imagine being able to validate sent signals with just a click—either "YES" or "NO." Read on to learn more.

Reimagining Classic Strategies (Part VI): Multiple Time-Frame Analysis
In this series of articles, we revisit classic strategies to see if we can improve them using AI. In today's article, we will examine the popular strategy of multiple time-frame analysis to judge if the strategy would be enhanced with AI.

Population optimization algorithms: Bird Swarm Algorithm (BSA)
The article explores the bird swarm-based algorithm (BSA) inspired by the collective flocking interactions of birds in nature. The different search strategies of individuals in BSA, including switching between flight, vigilance and foraging behavior, make this algorithm multifaceted. It uses the principles of bird flocking, communication, adaptability, leading and following to efficiently find optimal solutions.

Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR
In this series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.

Pattern Recognition Using Dynamic Time Warping in MQL5
In this article, we discuss the concept of dynamic time warping as a means of identifying predictive patterns in financial time series. We will look into how it works as well as present its implementation in pure MQL5.

Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes
In this series of articles, we analyze classical trading strategies using modern algorithms to determine whether we can improve the strategy using AI. In today's article, we revisit a classical approach for trading the SP500 using the relationship it has with US Treasury Notes.

Population optimization algorithms: Boids Algorithm
The article considers Boids algorithm based on unique examples of animal flocking behavior. In turn, the Boids algorithm serves as the basis for the creation of the whole class of algorithms united under the name "Swarm Intelligence".

Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA
This article presents a sophisticated Expert Advisor for forex trading, combining machine learning with technical analysis. It focuses on trading Apple stock, featuring adaptive optimization, risk management, and multiple strategies. Backtesting shows promising results with high profitability but also significant drawdowns, indicating potential for further refinement.

DoEasy. Service functions (Part 2): Inside Bar pattern
In this article, we will continue to look at price patterns in the DoEasy library. We will also create the Inside Bar pattern class of the Price Action formations.

Developing a Replay System (Part 43): Chart Trade Project (II)
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.

Build Self Optimizing Expert Advisors With MQL5 And Python (Part II): Tuning Deep Neural Networks
Machine learning models come with various adjustable parameters. In this series of articles, we will explore how to customize your AI models to fit your specific market using the SciPy library.

Risk manager for manual trading
In this article we will discuss in detail how to write a risk manager class for manual trading from scratch. This class can also be used as a base class for inheritance by algorithmic traders who use automated programs.

Reimagining Classic Strategies (Part III): Forecasting Higher Highs And Lower Lows
In this series article, we will empirically analyze classic trading strategies to see if we can improve them using AI. In today's discussion, we tried to predict higher highs and lower lows using the Linear Discriminant Analysis model.

Causal analysis of time series using transfer entropy
In this article, we discuss how statistical causality can be applied to identify predictive variables. We will explore the link between causality and transfer entropy, as well as present MQL5 code for detecting directional transfers of information between two variables.

Developing a Replay System (Part 42): Chart Trade Project (I)
Let's create something more interesting. I don't want to spoil the surprise, so follow the article for a better understanding. From the very beginning of this series on developing the replay/simulator system, I was saying that the idea is to use the MetaTrader 5 platform in the same way both in the system we are developing and in the real market. It is important that this is done properly. No one wants to train and learn to fight using one tool while having to use another one during the fight.

Population optimization algorithms: Whale Optimization Algorithm (WOA)
Whale Optimization Algorithm (WOA) is a metaheuristic algorithm inspired by the behavior and hunting strategies of humpback whales. The main idea of WOA is to mimic the so-called "bubble-net" feeding method, in which whales create bubbles around prey and then attack it in a spiral motion.

Build Self Optimizing Expert Advisors With MQL5 And Python
In this article, we will discuss how we can build Expert Advisors capable of autonomously selecting and changing trading strategies based on prevailing market conditions. We will learn about Markov Chains and how they can be helpful to us as algorithmic traders.

MQL5 Trading Toolkit (Part 2): Expanding and Implementing the Positions Management EX5 Library
Learn how to import and use EX5 libraries in your MQL5 code or projects. In this continuation article, we will expand the EX5 library by adding more position management functions to the existing library and creating two Expert Advisors. The first example will use the Variable Index Dynamic Average Technical Indicator to develop a trailing stop trading strategy expert advisor, while the second example will utilize a trade panel to monitor, open, close, and modify positions. These two examples will demonstrate how to use and implement the upgraded EX5 position management library.

Reimagining Classic Strategies (Part II): Bollinger Bands Breakouts
This article explores a trading strategy that integrates Linear Discriminant Analysis (LDA) with Bollinger Bands, leveraging categorical zone predictions for strategic market entry signals.

Hybridization of population algorithms. Sequential and parallel structures
Here we will dive into the world of hybridization of optimization algorithms by looking at three key types: strategy mixing, sequential and parallel hybridization. We will conduct a series of experiments combining and testing relevant optimization algorithms.

Combine Fundamental And Technical Analysis Strategies in MQL5 For Beginners
In this article, we will discuss how to integrate trend following and fundamental principles seamlessly into one Expert Advisors to build a strategy that is more robust. This article will demonstrate how easy it is for anyone to get up and running building customized trading algorithms using MQL5.

GIT: What is it?
In this article, I will introduce a very important tool for developers. If you are not familiar with GIT, read this article to get an idea of what it is and how to use it with MQL5.

DoEasy. Service functions (Part 1): Price patterns
In this article, we will start developing methods for searching for price patterns using timeseries data. A pattern has a certain set of parameters, common to any type of patterns. All data of this kind will be concentrated in the object class of the base abstract pattern. In the current article, we will create an abstract pattern class and a Pin Bar pattern class.

SP500 Trading Strategy in MQL5 For Beginners
Discover how to leverage MQL5 to forecast the S&P 500 with precision, blending in classical technical analysis for added stability and combining algorithms with time-tested principles for robust market insights.

Price Driven CGI Model: Theoretical Foundation
Let's discuss the data manipulation algorithm, as we dive deeper into conceptualizing the idea of using price data to drive CGI objects. Think about transferring the effects of events, human emotions and actions on financial asset prices to a real-life model. This study delves into leveraging price data to influence the scale of a CGI object, controlling growth and emotions. These visible effects can establish a fresh analytical foundation for traders. Further insights are shared in the article.

Eigenvectors and eigenvalues: Exploratory data analysis in MetaTrader 5
In this article we explore different ways in which the eigenvectors and eigenvalues can be applied in exploratory data analysis to reveal unique relationships in data.

How to Integrate Smart Money Concepts (BOS) Coupled with the RSI Indicator into an EA
Smart Money Concept (Break Of Structure) coupled with the RSI Indicator to make informed automated trading decisions based on the market structure.

Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python
In this article, we will introduce Sentiment Analysis and ONNX Models with Python to be used in an EA. One script runs a trained ONNX model from TensorFlow for deep learning predictions, while another fetches news headlines and quantifies sentiment using AI.

Developing a Replay System (Part 41): Starting the second phase (II)
If everything seemed right to you up to this point, it means you're not really thinking about the long term, when you start developing applications. Over time you will no longer need to program new applications, you will just have to make them work together. So let's see how to finish assembling the mouse indicator.

Developing an MQL5 RL agent with RestAPI integration (Part 4): Organizing functions in classes in MQL5
This article discusses the transition from procedural coding to object-oriented programming (OOP) in MQL5 with an emphasis on integration with the REST API. Today we will discuss how to organize HTTP request functions (GET and POST) into classes. We will take a closer look at code refactoring and show how to replace isolated functions with class methods. The article contains practical examples and tests.

Developing a Replay System (Part 40): Starting the second phase (I)
Today we'll talk about the new phase of the replay/simulator system. At this stage, the conversation will become truly interesting and quite rich in content. I strongly recommend that you read the article carefully and use the links provided in it. This will help you understand the content better.

Automated Parameter Optimization for Trading Strategies Using Python and MQL5
There are several types of algorithms for self-optimization of trading strategies and parameters. These algorithms are used to automatically improve trading strategies based on historical and current market data. In this article we will look at one of them with python and MQL5 examples.

Creating Time Series Predictions using LSTM Neural Networks: Normalizing Price and Tokenizing Time
This article outlines a simple strategy for normalizing the market data using the daily range and training a neural network to enhance market predictions. The developed models may be used in conjunction with an existing technical analysis frameworks or on a standalone basis to assist in predicting the overall market direction. The framework outlined in this article may be further refined by any technical analyst to develop models suitable for both manual and automated trading strategies.

Developing a Replay System (Part 39): Paving the Path (III)
Before we proceed to the second stage of development, we need to revise some ideas. Do you know how to make MQL5 do what you need? Have you ever tried to go beyond what is contained in the documentation? If not, then get ready. Because we will be doing something that most people don't normally do.

Population optimization algorithms: Resistance to getting stuck in local extrema (Part I)
This article presents a unique 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. Working in this direction will provide further insight into which specific algorithms can successfully continue their search using coordinates set by the user as a starting point, and what factors influence their success.

Multibot in MetaTrader (Part II): Improved dynamic template
Developing the theme of the previous article, I decided to create a more flexible and functional template that has greater capabilities and can be effectively used both in freelancing and as a base for developing multi-currency and multi-period EAs with the ability to integrate with external solutions.

Angle-based operations for traders
This article will cover angle-based operations. We will look at methods for constructing angles and using them in trading.

DoEasy. Controls (Part 33): Vertical ScrollBar
In this article, we will continue the development of graphical elements of the DoEasy library and add vertical scrolling of form object controls, as well as some useful functions and methods that will be required in the future.

MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library
Learn how to create a developer's toolkit for managing various position operations with MQL5. In this article, I will demonstrate how to create a library of functions (ex5) that will perform simple to advanced position management operations, including automatic handling and reporting of the different errors that arise when dealing with position management tasks with MQL5.