Articles on data analysis and statistics in MQL5

In this article, I will consider the Monkey Algorithm (MA) optimization algorithm. The ability of these animals to overcome difficult obstacles and get to the most inaccessible tree tops formed the basis of the idea of the MA algorithm.

The article presents a detailed description of the shuffled frog-leaping (SFL) algorithm and its capabilities in solving optimization problems. The SFL algorithm is inspired by the behavior of frogs in their natural environment and offers a new approach to function optimization. The SFL algorithm is an efficient and flexible tool capable of processing a variety of data types and achieving optimal solutions.

The article examines the possibilities of generating and providing reporting data on the use of the seasonality factor when trading spreads on Forex.

In this part we continue discussing Artificial Intelligence models. Namely, we study unsupervised learning algorithms. We have already discussed one of the clustering algorithms. In this article, I am sharing a variant of solving problems related to dimensionality reduction.

Many traders have experienced this situation, often stick to their entry criteria but struggle with trade management. Even with the right setups, emotional decision-making—such as panic exits before trades reach their take-profit or stop-loss levels—can lead to a declining equity curve. How can traders overcome this issue and improve their results? This article will address these questions by examining random win-rates and demonstrating, through Monte Carlo simulation, how traders can refine their strategies by taking profits at reasonable levels before the original target is reached.

In this article, we will consider one of the methods of spectral analysis and signal processing - the discrete Hartley transform. It allows filtering signals, analyzing their spectrum and much more. The capabilities of DHT are no less than those of the discrete Fourier transform. However, unlike DFT, DHT uses only real numbers, which makes it more convenient for implementation in practice, and the results of its application are more visual.

The article describes the principles, methods and possibilities of using the Electromagnetic Algorithm in various optimization problems. The EM algorithm is an efficient optimization tool capable of working with large amounts of data and multidimensional functions.

In this article, we develop a Bat Pattern system in MQL5 that identifies bullish and bearish Bat harmonic patterns using pivot points and Fibonacci ratios, triggering trades with precise entry, stop loss, and take-profit levels, enhanced with visual feedback through chart objects

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.

The ATR oscillator is a very popular indicator for acting as a volatility proxy, especially in the forex markets where volume data is scarce. We examine this, on a pattern basis as we have with prior indicators, and share strategies & test reports thanks to the MQL5 wizard library classes and assembly.

Hidden Markov Models (HMMs) are powerful statistical tools that identify underlying market states by analyzing observable price movements. In trading, HMMs enhance volatility prediction and inform trend-following strategies by modeling and anticipating shifts in market regimes. In this article, we will present the complete procedure for developing a trend-following strategy that utilizes HMMs to predict volatility as a filter.

In this article, we'll cover an important topic that few people understand: Custom Events. Dangers. Advantages and disadvantages of these elements. This topic is key for those who want to become a professional programmer in MQL5 or any other language. Here we will focus on MQL5 and MetaTrader 5.

This article continues the series on category theory implementation in MQL5. Here we introduce monoids as domain (set) that sets category theory apart from other data classification methods by including rules and an identity element.

In this third part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Hedge EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.

Detecting patterns in financial markets is challenging because it involves seeing what's on the chart, something that's difficult to undertake in MQL5 due to image limitations. In this article, we are going to discuss a decent model made in Python that helps us detect patterns present on the chart with minimal effort.

In this article we demonstrate how Hidden Markov Models trained using Python can be integrated into MetaTrader 5 applications. Hidden Markov Models are a powerful statistical tool used for modeling time series data, where the system being modeled is characterized by unobservable (hidden) states. A fundamental premise of HMMs is that the probability of being in a given state at a particular time depends on the process's state at the previous time slot.

We have managed to develop a way to implement the market replay system in a realistic and accessible way. Now let's continue our project and add data to improve the replay behavior.

The article presents a simple and accessible way to use a neural network in a trading EA that does not require deep knowledge of machine learning. The method eliminates the target function normalization, as well as overcomes "weight explosion" and "network stall" issues offering intuitive training and visual control of the results.

Do you know what really drives a currency pair’s direction? It’s the strength of each individual currency. In this article, we’ll measure a currency’s strength by looping through every pair it appears in. That insight lets us predict how those pairs may move based on their relative strengths. Read on to learn more.

This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!

The flow of the market is determined by the forces between bulls and bears. There are specific levels that the market respects due to the forces acting on them. Fibonacci and VWAP levels are especially powerful in influencing market behavior. Join me in this article as we explore a strategy based on VWAP and Fibonacci levels for signal generation.

This part focuses on building a flexible, adaptive trading model trained on historical XAUUSD data, preparing it for ONNX export and potential integration into live trading systems.

In the previous article, we discussed a simple RNN which despite its inability to understand long-term dependencies in the data, was able to make a profitable strategy. In this article, we are discussing both the Long-Short Term Memory(LSTM) and the Gated Recurrent Unit(GRU). These two were introduced to overcome the shortcomings of a simple RNN and to outsmart it.

We continue to study unsupervised learning algorithms. Some readers might have questions regarding the relevance of recent publications to the topic of neural networks. In this new article, we get back to studying neural networks.

In this article, We explore the dynamic integration of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in stock market prediction. By leveraging CNNs' ability to extract patterns and RNNs' proficiency in handling sequential data. Let us see how this powerful combination can enhance the accuracy and efficiency of trading algorithms.

Determining market direction can be straightforward, but knowing when to enter can be challenging. As part of the series titled "Price Action Analysis Toolkit Development", I am excited to introduce another tool that provides entry points, take profit levels, and stop loss placements. To achieve this, we have utilized the MQL5 programming language. Let’s delve into each step in this article.

We are moving beyond simply viewing analyzed metrics on charts to a broader perspective that includes Telegram integration. This enhancement allows important results to be delivered directly to your mobile device via the Telegram app. Join us as we explore this journey together in this article.

In the previous article, we introduced the multi-agent adaptive framework MASAAT, which uses an ensemble of agents to perform cross-analysis of multimodal time series at different data scales. Today we will continue implementing the approaches of this framework in MQL5 and bring this work to a logical conclusion.

We will analyze the question of what quantitative analysis is and how it is used by major players. We will create one of the quantitative analysis algorithms in the MQL5 language.

Correlation Pathfinder offers a fresh approach to understanding currency pair dynamics as part of the Price Action Analysis Toolkit Development Series. This tool automates data collection and analysis, providing insight into how pairs like EUR/USD and GBP/USD interact. Enhance your trading strategy with practical, real-time information that helps you manage risk and spot opportunities more effectively.