MQL5 Programs Market

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.

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.