History of MQL5.community development

We implement Symbolic Fourier Approximation in MQL5 and compare it to SAX under a shared harness on identical price windows. SFA keeps low‑frequency Fourier coefficients and learns per‑position bins (MCB), with a proven, sound lower bound. The measurements show how the same bit budget behaves under different splits of word length and alphabet, and give a practical rule for choosing settings for your symbol.

The second part of the series examines the mathematical framework for multivariate random variables, which is necessary for analyzing the dependence and joint behavior of market assets. This section describes joint distribution functions, the concepts of marginal and conditional distributions, and the conditions for dependence and independence of variables. The theoretical material is based on extending the analogy between probability and mass to multidimensional space. Particular attention is given to measures of association: from classical linear covariance and correlation to modern tools such as copulas and Shannon mutual information.

The article introduces the APARCH volatility process to the MQL5 library via the CAparchProcess class, estimating the power exponent (delta) jointly with other parameters. It details the recursion, parameter bounds, stationarity constraints, and starting values and reports SLSQP solver updates that streamline optimization. Implementation correctness is partially validated by reproducing approximations of GARCH and GJR-GARCH conditional volatility under parameter restrictions. A companion APARCH indicator visualizes conditional volatility, standardized residuals, and delta to track volatility dynamics and parameter drift.

The article presents a compact dashboard that relates trade duration to net profit using MQL5 and CCanvas. It pulls closed deals, derives duration in minutes, and renders a log‑scaled scatter by symbol, with an overlaid least‑squares line and R². A bucketed duration view identifies which hold‑time range produced the highest average result, helping assess exit timing.

In this article, we will explore the ST-Expert framework, which ensures the robustness of forecasts under market uncertainty by taking local and global dependencies in time series into account. Its flexible architecture promotes model adaptability and improves the accuracy of predictions.

We outline a modular analyzer for MetaTrader 5 that separates detection, interpretation, and visualization. The engine identifies swing highs and lows, assigns structural labels, evaluates impulses and pullbacks, and stores results in a market state object. An on‑chart dashboard and interactive inspection tools make the latest structure and measurements immediately accessible.

The article develops a multi-symbol basket EA that standardizes prices, derives PCA weights with native MQL5 matrices, forms a synthetic spread, and trades z-score deviations from a rolling mean. It validates symbols and synchronized history, stabilizes component orientation, maps signed weights to leg directions, and applies broker-aware volumes, stops, and netting rules. Basket entries run with rollback protection and chart status, with a reproducible testing procedure.

Part 9 adds a second measurement layer to Part 8's micro‑trend signal: pullback quality. It maps Fibonacci retracement depth to a six‑level PULLBACK QUALITY label, adds an H1 range position from a 60‑bar rolling proxy, and uses lag‑1 momentum autocorrelation. These inputs form a single composite entry‑quality score in [0,1] for filtering setups and sizing trades within MicroStructure_Foundation.mqh.

The article implements intrinsic-time analysis in MQL5: an event-based directional-change operator that splits ticks into directional-change and overshoot sections. We reproduce the core scaling laws on 17.8 million live EUR/USD ticks and compare them to a random-walk baseline. Finally, we build a hedging-account Expert Advisor that trades the Alpha Engine with limit orders, detailing thresholds, inventory skew, and liquidity control for practical reuse.

The study implements five historical-variance estimators in MQL5 and evaluates their one-session-ahead persistence forecasts for EURUSD D1 sessions using an M1 realized-variance proxy. Deterministic tests cover formulas, chronological order, and target construction. A configurable indicator, comparison scripts, and CSV outputs provide reproducible losses, calibration diagnostics, a common‑target mask, and sensitivity to the estimation window.

This article applies the Kelly criterion to position sizing in native MQL5. It presents a reusable CKelly class that estimates win rate and payoff from closed deals, derives the Kelly fraction, and sizes lots from a stop distance. A Monte Carlo sweep of the Kelly multiplier shows growth peaking at full Kelly while drawdown and ruin increase, motivating fractional Kelly such as half Kelly that preserves most growth with materially lower drawdown.

Part 5 adds a fill rule to the rasterizer and a stroke helper to the path. The engine now supports both non-zero and even-odd fills via a single enum parameter, enabling rings, true borders that do not repaint interiors, and glyph counters. AddThickLine() builds strokes from fills in one path and one pass, preserving uniform opacity at joints and showing why strokes require the non-zero rule.

The article describes a competitive architecture for MetaTrader 5 in which ten LLM agents, each with different trading rules, manage their own capital and open independent positions using unique magic numbers. The system prompt and the agent's trading aggressiveness are adjusted based on PnL results and the trade streak. A reproducible framework with operating modes and monitored metrics is presented, suitable for testing and further optimization.

The Extralonger framework demonstrates a unique ability to integrate spatial and temporal factors into a single model, ensuring high forecast accuracy. Its architecture allows it to adapt to different planning horizons and financial instruments while maintaining the system's transparency and manageability.

A modular MQL5 toolkit computes and visualizes rolling Profit Factor over fixed trade-count windows. It presents the statistical motivation, an incremental algorithm that avoids recomputation, and a dedicated CCanvas rendering pipeline. The dashboard adds reference lines, shading for weak periods, and summary metrics, while a separate test suite validates the math, giving a practical way to monitor stability and detect deterioration in strategy behavior.

This article defines a Protocol Buffers contract for the MetaTrader 5-Python boundary and implements a length-prefixed Protobuf-over-TCP client in MQL5, since MQL5 cannot speak real gRPC natively. A small Python shim relays those frames to a genuine grpc.aio server, unary today, with streaming already live on the backend. You get schema-enforced, strongly-typed messages, explicit errors, retry/backoff, and a Strategy Tester cache for reproducible backtests where sockets don't run.

Volatility arrives in clusters: one large move makes the next large move more likely, and quiet spells stay quiet. This article builds a Hawkes self-exciting point process in pure MQL5 to measure that effect directly, ending in a single number, the branching ratio, that says how reflexive a market currently is. You get a small, tested library, an indicator that plots the fitted intensity live, and a demonstration Expert Advisor, along with the honest limits of all three.

The article describes the architecture of a multi-agent trading system based on the grok-4-fast language model, in which, instead of a single system prompt, four independent analysts with fundamentally different roles operate: a bull, a bear, a risk manager, and an arbiter. Three analysts run in parallel using a ThreadPoolExecutor and, within 3–5 seconds, formulate well-reasoned positions based on the same market data; after that, a deterministic judge renders a final verdict according to strict rules.

This article reframes performance from unconditional win rate to conditional probability given survival time. It introduces an MQL5 library, an on‑chart indicator, and a demo Expert Advisor that read deal history, fit Kaplan–Meier and Aalen–Johansen curves with competing risks, and report forward probabilities over a bar‑based horizon. Readers gain a reproducible way to quantify the chance that the current position reaches its target or stop, and to see the bias of the naive censoring approach.