Articles on machine learning in trading

The article discusses the Extremal Optimization (EO) algorithm, an optimization method inspired by the Bak-Sneppen self-organized criticality model, where evolution occurs through the elimination of the worst-case components of the system. The modified population version of the algorithm demonstrates a shift away from theoretical principles in favor of practical efficiency, leading to the creation of powerful computational tools.

Machine learning is often viewed through statistical or linear algebraic lenses, but this article emphasizes a geometric perspective of model predictions. It demonstrates that models do not truly approximate the target but rather map it onto a new coordinate system, creating an inherent misalignment that results in irreducible error. The article proposes that multi-step predictions, comparing the model’s forecasts across different horizons, offer a more effective approach than direct comparisons with the target. By applying this method to a trading model, the article demonstrates significant improvements in profitability and accuracy without changing the underlying model.

This article which is part of a series that follows Category Theory implementation of Graphs in MQL5, delves in Orders. We examine how concepts of Order-Theory can support monoid sets in informing trade decisions by considering two major ordering types.

In this article, we will touch upon the intriguing topic of fractal analysis and market forecasting using machine learning. These are just the first steps towards exploring the diverse fractal structures that form on financial price charts. We will use the correlation to find patterns and the CatBoost algorithm to classify these patterns.

The new proprietary optimization algorithm NOA2 (Neuroboids Optimization Algorithm 2) combines the principles of swarm intelligence with neural control. NOA2 combines the mechanics of a neuroboid swarm with an adaptive neural system that allows agents to self-correct their behavior while searching for the optimum. The algorithm is under active development and demonstrates potential for solving complex optimization problems.

What is the relationship between weather and Forex? Classical economic theory has long ignored the influence of such factors as weather on market behavior. But everything has changed. Let's try to find connections between the weather conditions and the position of agricultural currencies on the market.

One of the directions for increasing the efficiency of the model training and convergence process is the improvement of optimization methods. Adam-mini is an adaptive optimization method designed to improve on the basic Adam algorithm.

This article introduces Jardine's Gate, a six-gate orthogonal signal filter for MetaTrader 5 that validates LSTM predictions across entropy, expert interference, confidence, regime-adjusted probability, trend direction, and consecutive-loss kill switch dimensions. Out of 43,200 raw signals per month, only 127 pass all six gates. Readers get the complete QuantumEdgeFilter MQL5 class, threshold calibration logic, and gate performance analytics.

Does better return conditioning buy strategy performance? We hold bar count fixed across time, tick, tick-imbalance, and tick-runs on 60.5 million EURUSD ticks, then meta-label RSI, Bollinger, and ADX/DI entries and score with purged cross-validation. No family delivers a consistent edge; efficacy varies narrowly and the best case fails a permutation test. Readers learn how to control overlap, leakage, and multiple testing in bar studies.

We build a rolling price channel by fitting the 0.1, 0.5 and 0.9 conditional quantile lines via IRLS with pinball loss, packaged as a reusable class and two MetaTrader 5 indicators. We verify in-sample coverage, examine quantile crossing, and compare the channel width with ATR, Bollinger and regression widths on matched horizons. Tests in the Strategy Tester show the edges are descriptive, while the normalized width works as a volatility/regime feature.

In this article, we will explore the innovative Chimera framework: a two-dimensional state-space model that uses neural networks to analyze multivariate time series. This method offers high accuracy with low computational cost, outperforming traditional approaches and Transformer architectures.

In this article, we will consider another optimization algorithm inspired by inanimate nature - Charged System Search (CSS) algorithm. The purpose of this article is to present a new optimization algorithm based on the principles of physics and mechanics.