History of MQL5.community development

The article discusses the application of Walsh functions in trading. We will explore the basic principles of using these functions to analyze financial markets, forecast prices, and make trading decisions. We will also discuss the advantages and disadvantages of these functions, as well as the prospects for their application in trading and technical analysis.

We examine how a Hidden Markov Model (HMM) estimates latent market regimes while basing on observable price and indicator sequences. This is done by estimating the probability of state transitions. A Gated Recurrent Unit (GRU) network models time dependencies and keeps important information over several observations. In an Expert Advisor, HMM-based regime probabilities, can be merged with GRU-based sequence learning to better classify increments in accumulation, distribution, and momentum prior to their showing up in regular price confirmations.

We introduce a Partial Information Decomposition library for MQL5 that decomposes two sources about a target into four atoms: unique to each, shared, and synergy. The implementation uses quantile binning, tabulated logarithms, and a maximum-entropy fit (for I_ccs), and it pairs results with a block-permutation null because atoms sit above zero on finite samples. Use it to screen indicator pairs and judge significance, including family-wise correction.

The article provides a detailed explanation of the SCNN architecture and one way to implement it using MQL5. We will show how time series decomposition can be combined with neural network methods and attention mechanisms.

The article presents the full integration of the 3D-bar module into a quantum-enhanced trading system for forecasting the movement of currency pairs. The system combines stationary four-dimensional features, an 8-qubit quantum encoder, and CatBoost gradient boosting with 52+ features. The system is implemented in Python using MetaTrader 5, Qiskit, CatBoost, and optional integration with the Llama 3.2 LLM for interpreting forecasts.

We implement a five-stage MQL5 pipeline that quantifies market structure, liquidity interaction, and price behavior on four timeframes, then resolves them into a 0–100 Market Intent Score. Decision states (WAIT/WATCH/ACTION) are driven by explicit weights plus hard gates. The analytical core feeds a concise dashboard and, when AutoTrade is on, an execution layer with entry zones, invalidation and liquidity‑based targets.

This part implements risk-based position sizing for the EA. Lot size is derived from account balance, a chosen risk percent, and ATR-based stop distance, then confined and rounded to the broker's volume rules and minimum stop levels. An optional drawdown-aware layer reduces risk during equity declines. Readers get a reproducible sizing function that keeps per-trade risk consistent and orders acceptable to the server.

In this article, you will learn how to create a visual signal on your trading platform so you can determine directly on the chart whether a position is long or short, without having to open the Terminal. In addition, the article also explains how to implement a feature that improves the display when moving Take Profit and Stop Loss lines by hiding the horizontal line that follows the mouse cursor while these lines are being moved, to avoid confusion. The article provides practical insight into setting up market simulation systems.

In this article, we will conclude the section on the implementation and explanation of the linked list. However, the implementation presented here omits one detail that can be implemented in a linked list. We will discuss this later, in another article.

The ECO (Ecological Cycle Optimizer) algorithm offers an interesting metaphor for applying the concept of the ecological cycle to the field of metaheuristic optimization. The idea of dividing a population into trophic levels — producers, herbivores, carnivores, omnivores, and decomposers — creates a hierarchical search structure, in which each group contributes to the overall optimization process.

We invite you to explore the next stage in implementing the SCNN framework, which combines flexibility and interpretability, allowing structural components of a time series to be identified precisely. The article provides a detailed explanation of the mechanisms of adaptive normalization and attention, which ensure the model's resilience to changing market conditions.

In this article, we will refine the graphics library classes by adding a vertical header to the table and use the table classes to create an indicator that displays the correlation between the symbols specified in the settings.

We present a rule-set-aware calibration chain that turns the remaining risk budget into a calibrated sigmoid scale for position sizing. It computes a ceiling from stop loss pct and safety factor, back-solves w at a reference divergence, and flattens size progressively as the budget shrinks. The paper also clarifies where leverage caps must be applied in production: at the lots conversion, since risk-based sizing alone does not enforce max leverage.

The article shows how to tune ICA hyperparameters with a supervised evaluation pipeline and apply spectral clustering to time-lagged indicators. Cross-validation identifies the optimal number of clusters, which are translated into expected return and risk measures. These signals drive dynamic position sizing and stop-loss control, with surrogate models converted to ONNX and integrated into an MQL5 Expert Advisor.