Articles on trading system automation in MQL5

The article proposes a synthesis of new technologies to overcome the limitations of classical indicators in market data analytics. It shows how language models and quantum encoding can reveal hidden market patterns that traditional methods overlook. The experiment confirms the value of new technologies and proposes an updated analysis methodology aligned with the current state of computational innovation.

The article examines the quality of a seasonal trading approach on a daily timeframe, both for individual symbols and for spreads. Particular attention is paid to identifying recurring monthly cycles and the possibilities of their application in trading within the current year.

What if an optimization algorithm could remember its past journeys and use that memory to find better solutions? BSA does just that – balancing exploration with revisiting the tried and true. In this article, we reveal the secrets of the algorithm. A simple idea, minimum parameters and a stable result.

The EA learns each symbol's volatility profile before trading by processing 1000 bars and summarizing candle ranges, bodies and wicks, noise ratio, trend runs, pullback size, and true‑range dispersion. A classifier assigns regime and structure labels per pair. The stop‑loss optimizer maps those labels to a symbol‑specific ATR multiplier, and the risk module sizes lots to maintain constant percentage risk.

This article introduces a frequency-analysis framework for encoded candlestick patterns in MQL5. By transforming candlesticks into alphabetic symbols, historical price action can be analyzed as a statistical sequence rather than a visual chart. Using GBPUSD and Gold across multiple timeframes, the study examines the occurrence frequency of individual candlestick types, identifies dominant market structures, and reveals the symmetry between bullish and bearish price movements. The results establish a quantitative foundation for pattern discovery and prepare the way for analyzing multi-candlestick sequences and their predictive potential in algorithmic trading systems.

From ChatGPT to Gemini and many model AI tools for text, image, and video generation. Transformers have rocked the AI-world. But, are they applicable in the financial (trading) space? Let's find out.

In this article, we expand the MQL5 graphing tool to support seventeen statistical distributions with interactive cycling via a header switch icon. We add type-specific data loading, discrete and continuous histogram computation, and theoretical density functions for each model, with dynamic titles, axis labels, and parameter panels that adapt automatically. The result lets you overlay distribution models on the same sample and compare fit across families without reloading the tool.

In our models, we often use various attention algorithms. And, probably, most often we use Transformers. Their main disadvantage is the resource requirement. In this article, we will consider a new algorithm that can help reduce computing costs without losing quality.

Restrictive Boltzmann Machines are at the basic level, a two-layer neural network that is proficient at unsupervised classification through dimensionality reduction. We take its basic principles and examine if we were to re-design and train it unorthodoxly, we could get a useful signal filter.

This part extends the series with a modular, event-driven MQL5 pipeline: swing detection feeds an object placer for trendlines, SR, Fibonacci, channels, and pitchforks; evaluators monitor interactions and generate signals; adaptive logic executes trades with valid stops per instrument. The topology manager synchronizes placement, scanning, and processing. The code is structured into reusable components for easy reuse and scaling.

The article is devoted to the metaheuristic Atmosphere Clouds Model Optimization (ACMO) algorithm, which simulates the behavior of clouds to solve optimization problems. The algorithm uses the principles of cloud generation, movement and propagation, adapting to the "weather conditions" in the solution space. The article reveals how the algorithm's meteorological simulation finds optimal solutions in a complex possibility space and describes in detail the stages of ACMO operation, including "sky" preparation, cloud birth, cloud movement, and rain concentration.

The article considers methods of encoding initial data in hyperbolic latent space through anisotropic diffusion processes. This helps to more accurately preserve the topological characteristics of the current market situation and improves the quality of its analysis.

Integrating Python's logging module with MQL5 empowers traders with a systematic logging approach, simplifying the process of monitoring, debugging, and documenting trading activities. This article explains the adaptation process, offering traders a powerful tool for maintaining clarity and organization in trading software development.

In this article we build a dealer gamma-exposure map in MQL5. From an option chain, the tool computes per-strike GEX, finds the call and put walls, and solves for the zero-gamma flip that separates a mean-reverting regime from a trending one, then draws it all on the chart. CSV and native-symbol data paths included.

The article explains how to develop a tool for analyzing recurring price patterns in financial markets — by day of the month (1-31), day of the week (Monday-Sunday), or hour of the day (0-23). The indicator analyzes historical data, calculates the average return for each period, and displays the results as a histogram with a forecast. It includes customizable parameters: seasonality type, number of bars analyzed, display as percentages or absolute values, chart colors.