Articles on machine learning in trading

We are continuing our work on implementing the approaches proposed by the authors of the Extralonger framework. This time, we will focus on building a Global-Local Spatial Attention module using MQL5, examining both its structure and its practical integration into the overall computational process.

The article discusses the Competitive Swarm Optimizer — a swarm optimization algorithm based on an extremely simple idea: agents are randomly paired, and the loser learns from the winner and is drawn toward the center of the swarm. In addition to analyzing CSO, the article describes the modernization of the test bench: visualization of the algorithms’ operation has been moved into 3D space, making it possible to clearly observe the movement of the population on the surface of the test function.

Standard HMMs assume geometric, memoryless state durations, which poorly match real market phases. This piece implements a duration‑explicit Hidden Semi‑Markov Model natively in MQL5, with per‑state sojourn distributions and a residual‑time forward filter. Parameters are fit offline via EM and loaded through a compact JSON manifest. The EA for XAUUSD M5 uses expected remaining duration to gate entries and exits, helping hold trends while avoiding late entries near regime exhaustion.

The Extralonger framework demonstrates an approach to integrating spatial and temporal factors into a single model, which makes it possible to account for both local patterns and long-term cycles simultaneously. This architecture makes time series forecasting more resilient to market noise and enables data analysis across different time horizons. The article takes a detailed look at how these ideas are put into practice using OpenCL and MQL5.

Sample correlation matrices can look precise yet be mostly noise. This article implements a dependency-free RMT cleaner in MQL5: Jacobi eigendecomposition, Marchenko–Pastur eigenvalue screening, and average-noise reconstruction that preserves the matrix trace and unit diagonal. It explains integration into a basket EA so the denoised matrix improves stability of hedge ratios and weights between rebalances, while keeping the code portable and auditable.

In this article, we conclude our work on building the SAGDFN framework using MQL5, summarizing the development process and presenting the results of its practical testing. Let's combine the modules we've already implemented into a single system, highlight the strengths of this approach, point out its weaknesses, and discuss possible ways to improve it.

In this article, we will examine the Dragonfly Algorithm (DA), inspired by the collective behavior of dragonflies in nature — their ability to coordinate flight in a swarm, avoid collisions, follow prey, and evade predators. Let's look at how five simple behavioral rules and an adaptive mechanism for transitioning from exploration to exploitation are implemented in MQL5, and test the algorithm on our test bench.

In this article, we will take a detailed look at the practical implementation of the key components of the SAGDFN framework. We will show how sparse attention and the selection of significant neighbors are organized for time series forecasting. The approaches presented strike a balance between forecast accuracy and computational efficiency.

The article examines three key obstacles to integrating LLMs with MetaTrader 5: the lack of direct access, strict rate limits, and API key security given the architectural limitations of MQL5. A configuration is proposed that uses a local Python server as a bridge between the Expert Advisor and OpenRouter. The article covers WebSocket and fallback to TCP, storing the key on the server, batch processing of multiple symbols, and constructing a technical prompt. Readers get a ready-made architecture that reduces latency and costs.

In this article, we explore the architecture of SAGDFN — a modern framework capable of transforming the approach to processing spatiotemporal data. It preserves key information even in complex graphs while reducing computational costs.

Article revisits Trading Robot that merged Discrete Fourier Transform with Leaky Integrate-and-Fire Spiking Neural Network. Evaluation is made over the seven operating modes with different symbols, timeframes, and test windows. We use two-thirds of the test window to optimize while the one-third does a forward walk run. This study tries to detail parameter interactions, input scaling, gating effects, and outlines practical checks that include rolling windows, frozen inputs, and others. The goal remains identifying settings worth further testing or paper trading.

The article describes a practical implementation of the HimNet framework based on MQL5, ready for integration into automated trading. We demonstrate how heterogeneity-adapted meta-parameters transform the model into a universal tool capable of handling fluctuating volatility.

In this article, we take a detailed look at the algorithms used to implement the key components of the HimNet framework. We demonstrate how, with a minimal number of trainable components, a high degree of consistency and controllability can be achieved throughout the entire system. The presented implementation is compact and transparent, which makes it easier to adapt to real-world market tasks.

We invite you to explore the HimNet framework, which combines the flexibility of spatio-temporal adaptation with high computational efficiency, enabling accurate and stable forecasts for financial time series. The article explains in detail how its key components interact with one another, transforming complex algorithms into a manageable architecture.

The series develops machine learning in 100% native MQL5 with no external dependencies. Part 1 delivers logistic regression from first principles: a CLogReg class with standardization, a stable sigmoid, SGD training, and model persistence, plus a script that builds ATR-normalized features, labels the next bar, and tests out-of-sample against a baseline. Readers get a compact include file and a clear template for leakage-free evaluation.

We merge GARCH(1,1) variance projections with ATR plus Bollinger-Bands patterns to form an algorithm that could optionally be used with volatility-scaled LSTM within LSTM Wizard-ready signal class. We cover feature scaling, mode scoring, thresholds, and safety checks. Readers can replicate backtest/forward test results to verify if the recurrent layer gives incremental discrimination over our deterministic baseline.

This article presents a native MQL5 implementation of the ZeroMQ Message Transfer Protocol (ZMTP) built on raw MQL5 sockets. It explains the REQ/REP pattern via the CZmqReqSocket class, including framing, handshake, and strict send/receive alternation. A practical pipeline shows an MQL5 script streaming returns to a Python/R server running MS‑GARCH and receiving regime probabilities, enabling integration without DLLs.

A hybrid architecture based on Llama 3.2 and SEAL is being tested on eight currency pairs (M15), with forward-period data isolation and information leakage control. The methodology combines adversarial self-play, curriculum learning, and class balancing to ensure stable training. The experiments confirm the gap between forecast accuracy and actual returns, providing readers with practical guidelines for testing strategies and accurately assessing their generalizability.

The article discusses the Butterfly Optimization Algorithm, which is based on modeling foraging using the sense of smell. We will analyze the original formulas, identify and correct errors in motion equations, add a mechanism for maintaining population diversity, and present the test results.

The TQNet framework opens up new possibilities for modeling and forecasting financial time series by combining modularity, flexibility, and high performance. The article explores the possibility of implementing complex mechanisms for handling global correlations, including advanced parameter initialization methods.

We turn eigenvalues from a covariance matrix into a normalized spectral‑entropy score that measures how evenly variance is spread across factors. SpectralEntropyCalculator.mq5 compares two portfolios in one run, using native vector summation, ArraySort()-based ordering, element‑wise division, and the Shannon entropy formula. The report makes dominant factors visible and enables quick, repeatable checks of diversification quality.

We consider the methods with which Isotonic Regression calibrates raw RSI, Stochastic and price-action signal scores into probabilities that are sorted, while a separate Probability based Neural Network evaluates similar historical market states. This article uses both approaches in a ready-made MQL5 custom signal class that is compatible with MQL5 Wizard and provides up to 7 selectable entry modes. Reproducible tests compare isotonic-only signals with the combined Isotonic-PNN model to assess whether the network adds useful information beyond the simpler baseline.

We invite you to learn about an algorithm for decomposing a time series into meaningful layers and using them to build a parsimonious model. We systematically present the architecture, the practical implementation in MQL5/OpenCL, and real-world tests using historical market data.

The article discusses the Cricket Algorithm, a metaheuristic optimization method that combines elements of the Bat Algorithm and the Firefly Algorithm with the physical laws governing the propagation of sound in the atmosphere. The algorithm simulates the behavior of crickets that navigate by the chirping of their conspecifics, using Dolbear's law and acoustic formulas to guide the search for best solutions.

In this article, we continue our hands-on exploration of SSCNN — a next-generation architectural solution capable of processing fragmented time series. Instead of blind scaling — smart modularity, attention to detail, and targeted normalization. Step by step, we are creating computational blocks in the MQL5 environment and laying the foundation for reliable predictive analysis.

SEAL (Self-Evolving Adaptive Learning) is a system for the continuous adaptation of large language models (LLMs) for algorithmic trading, designed to address the problem of rapid model degradation in changing markets. Instead of periodic retraining, which takes hours and erases old patterns, SEAL learns from every closed trade, maintains priority memory for important examples, and automatically initiates incremental fine-tuning when accuracy drops or a market regime change occurs.

The article discusses the ECO optimization algorithm, which is based on ecological concepts: populations are grouped into habitats based on territorial proximity, exchange genetic material within habitats, and migrate between them. Despite its wide range of operators and elegant biological metaphor, the algorithm produced a certain result discussed below.