Articles on data analysis and statistics in MQL5

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.

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.

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.

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.

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.

The article extends the MQL5 recurrence library to Recurrence Network Analysis (RNA) by treating recurrence matrices as adjacency matrices of undirected graphs. It implements core network metrics—clustering, transitivity, average path length, betweenness, assortativity, and density—and applies them in rolling windows for single-series RNA and Joint RNA (JRNA). A modular metrics engine and two indicators visualize the evolving network structure on MetaTrader 5 charts for practical time-series analysis.

For our next Exploration on notions that are testable with the MQL5 Wizard we examine if Skip Lists and the Hopfield Network can give us a profit-guarding trailing strategy. Trailing Stop Management, as already argued, can be overlooked in most trading systems at the expense of Entry Signals or even Money Management. Trailing stops can make all the difference in certain situations such as trending markets, and thus we test this out with GBPJPY.

In the previous article, I showed how to proceed in order to add a query mechanism. This was needed so that, inside MQL5 code, you could fully use SQL and retrieve results using an SQL SELECT query. But there is still one last function we need to implement. This is the DatabaseReadBind function. Since understanding it properly requires a slightly more detailed explanation, it was decided to cover it not in the previous article, but in today's article. So, since the topic will be fairly extensive, let us proceed directly to the next section.

Net profit and win rate do not tell you if a strategy's edge is statistically real. This MQL5 toolkit analyzes return series built from price data or deal history and reports t‑statistics, p‑values, and confidence intervals using one-sample and Welch t‑tests, the Mann–Whitney U test, and volatility‑regime analysis to support evidence‑based trading decisions.

A native, dependency-free MQL5 implementation of the Mann-Whitney U test for comparing trade returns across two market regimes. It details rank calculation, tie correction, and a normal-approximation p-value, and pairs the test with a CCanvas box-and-whisker chart and a trade-history extraction script. A verification script is included, and the limits of the normal approximation and independence assumptions are clearly stated for informed use.