Articles on data analysis and statistics in MQL5

This article finalizes the MMAR project with a CMMAR facade class and a demo Expert Advisor for MetaTrader 5. The facade exposes a compact API—configure, Fit(), Forecast()—that wraps partition analysis, spectrum fitting and Monte Carlo simulation. You will learn how to load data, fit the model and obtain a volatility forecast, with diagnostics and status handling for robust use in EAs.

We extend the RQA library for MetaTrader 5 with JRQA, which detects when two series simultaneously revisit their own past states. The article covers the joint recurrence matrix, twelve JRQA metrics (including TREND and COMPLEXITY), dual-epsilon configuration, and a rolling-window engine with OpenCL acceleration and automatic CPU fallback. A practical indicator plots JRR, JDET, JLAM, JENTR, and JTREND for any symbol pair with timestamp alignment and normalization.

MetaTrader 5 provides no native tool for visualizing intraday return patterns across time dimensions simultaneously. This article implements a custom indicator that aggregates historical bar returns into a 5×24 matrix indexed by weekday and hour of day, then renders the result as a color-interpolated heatmap inside an indicator subwindow using CCanvas. Green cells represent positive average returns, red cells negative, with color intensity encoding return magnitude.

This article presents a distribution-adaptive spread monitor for MQL5 that replaces fixed thresholds with a rolling histogram of each symbol's recent spread. It explains percentile estimation from bins, a four-state GREEN/YELLOW/RED/WARMING classification, and a CCanvas dashboard rendered from real histogram data. You will get a ready workflow for per-symbol order gating and controlled alerting via arm/disarm hysteresis plus cooldown, with a verification script and clear calibration and resolution limits.

The article builds an MQL5 Expert Advisor that writes a self-refreshing HTML positions dashboard to MQL5/Files on every tick, so you can monitor open trades in any browser. It covers reading live position data, generating a complete page with inline CSS and a JavaScript reload timer, and writing the file atomically. The design escapes HTML in comments, shows an explicit empty state, and writes a clear offline page on EA shutdown.

The article presents the implementation and analysis of the Bonobo Optimizer algorithm, which is based on the unique behavioral characteristics of bonobos — their dynamic fission-fusion social structure and three mating strategies. What interesting features does this method have?

We implement ordinal pattern transition networks in MQL5: a Lehmer-code encoder, a directed network over ordinal price patterns, and three complexity metrics. Two indicators expose a trend-versus-range regime from time-irreversibility and an efficiency gauge from permutation entropy, with a transparent parameter sweep showing how to tune settings on FX data.

A rolling-window Approximate Entropy oscillator for MQL5, built without external dependencies. Covers the full mathematics of template matching, Chebyshev distance, and the Phi-function derivation before presenting a reusable CApEnCalculator class and a color-zoned subwindow indicator. Includes a synthetic-data verification script and an honest discussion of bias, parameter sensitivity, and computational cost.

The article provides a critical analysis of an LLM strategy in which forecasting the direction is separated from trading decisions, and demonstrates why this leads to a disconnect between metrics and PnL. We will describe procedures for dataset balancing, feature engineering, prompt and response preparation, fine-tuning configuration in Ollama, and reliable parsing. Backtesting and forward testing reveal systematic degradation. The practical conclusion is that the problem must be formulated as a direct optimization of trading outcomes.

Many people tend to underestimate SQL, or even not use it at all, because they do not fully understand how it actually works. When running queries against an SQL database, we are not always looking for a universal answer; in some cases, we need a very specific and practical answer. If a database is created with a proper structure and data model, almost any type of information can be integrated into it.

This article extends the MQL5 RQA library to Cross-Recurrence Quantification Analysis (CRQA) for comparing two time series. We implement dual‑series embedding, cross‑recurrence matrix construction, adapted metrics (CRR, CDET, CLAM, CENTR, and others), and rolling‑window analysis, with optional GPU acceleration via OpenCL. A ready-to-use indicator compares two symbols in real time, supporting timestamp alignment and normalization for practical inter-market analysis.

The article provides a systematic overview of the theory of random variables, which serves as the basis for analyzing and modeling uncertainty in financial markets. The article covers the definitions and properties of univariate random variables, cumulative distribution functions (CDFs) and probability density functions (PDFs), as well as the differences between discrete, continuous, and mixed models. The theoretical material is based on intuitive analogies with mass and density. The appendix to this article contains practical examples of using the standard MQL5 library to calculate probabilities, quantiles, and moments of distributions. It also demonstrates the graphical capabilities of the MetaTrader 5 platform for visual data analysis by plotting PDF and CDF curves and QQ plots.