MQL4 and MQL5 Programming Articles

A complete MQL5 implementation of the Ljung-Box test helps verify independence in trading data and fitted-model residuals. It computes sample autocorrelations, the Q statistic over selected horizons, degrees of freedom with user-controlled adjustments, and right-tail p-values via the regularized incomplete gamma function. Run it on returns, deal outcomes, or external residuals and review decisions directly in the Experts tab.

FX options are quoted in delta space, not by strike. This article implements an FX-native smile tool for MetaTrader 5: it converts ATM, risk reversal and butterfly quotes into strike-space pillars, prices with the Garman–Kohlhagen model, handles spot/forward and premium-adjusted delta conventions per pair, and draws the smile with a reconstructed strike ladder and Greeks.

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.

Feature importance often understates correlated predictors by spreading one signal across many engineered copies, while unrelated noise can appear higher. We measure this effect against a known ground truth and compare four remedies: permutation importance with purged cross-validation, single-feature models, and clustered impurity versus clustered accuracy. The results include per-method rankings and a per-cluster dilution ratio that help identify true signals and avoid deleting valuable features.

This article shows how a PDF works as plain text by hand‑written two files: a 592‑byte page and an 885‑byte trade ticket. It explains the file structure (header, body, xref, trailer), the required page objects and resources, and the operators that draw text, then provides an MQL5 script to generate them. First part of a pure‑MQL5 PDF library series.

In this article, we will look at how to modify the code to implement a solution entirely unlike what many consider possible in MQL5. Important note: To fully understand this material, you must have a solid grasp of the concepts covered in the previous articles.

The article examines an example of a multipurpose trading robot template that is suitable both for creating your own strategies and as a codebase for freelance work. A key feature of the solution is bar-based trading; the code already includes built-in modes for averaging, martingale, and holding positions for extended periods. This material will be most useful to beginners who want to develop their own simple strategies or learn about common trading techniques.

The article discusses the parameters of the second stage of the automatic optimization pipeline for a multi-currency Expert Advisor. We analyze the criteria for filtering first-stage passes and the rules for forming groups of trading strategies. The article demonstrates how settings affect optimization results, discusses aspects of process reliability, and examines the balance between selection strictness and having enough candidates for the algorithm.

We build an adaptive Fibonacci volatility band indicator in MQL5 that centers on a smoothed price (SMMA) and scales band width with a smoothed ATR. The article covers inputs, buffer mapping, ATR handling, and SMMA formulas, then projects configurable Fibonacci ratios with filled zones. Readers get a ready workflow for visualizing volatility expansion/contraction and outlining dynamic support and resistance.

Until now, the application being developed as part of this series of articles has focused exclusively on simulating the graphical part. However, to obtain a more complete system in which we can test the Expert Advisor within the replay/simulation service, we also need to simulate the trading server. You'll notice that this simulation will include only the most essential elements. Nevertheless, you, dear reader, will be able to fill in the missing parts. Since these additional components don't affect what I want to show, we already have more than enough to implement what we have in mind.

The article examines the practical application of finite differences in trading: types of differences, their relationship to price dynamics, and the binomial transform for noise filtering. The rules for encoding patterns based on difference levels and the application of these patterns to forecasting are described. This section presents naive, adaptive, and probabilistic approaches that help smooth time series, identify recurring patterns, and estimate future movements.

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.

This article presents a modular, object-oriented volatility trailing stop engine for MQL5 packaged as a reusable include class, it calculates dynamic stop-loss levels using a True Range average filter on closed bars, supports step-based trailing, includes a visual diagnostic indicator, and provides an Expert Advisor execution template.

In this article, we will examine how to implement overloading for both logical operators and comparison operators. This requires a certain amount of caution and a fair amount of attention. Even a minor oversight when implementing the overloading of these operators can render the entire code completely unusable. If any problems arise in the overloading, the entire database created from the results generated by the code will have to be either discarded completely or, at the very least, reviewed in full.

We're entering the home stretch. The development of the replay/simulation system is nearly complete. Of course, we still have a few things left to finish, but compared to everything we've already done, completing what's left won't be difficult. However, it is essential to fully absorb and understand everything covered in this article. So I hope you enjoy reading this and, above all, that you enjoy this final stage of the journey.

The article describes a practical approach to synchronizing bars between instruments in a portfolio in MQL5. Classes are provided for loading, storing, and aligning OHLCV data, with options to use an empty bar or carry over values from the previous bar, select a synchronization symbol, and process new bars asynchronously. Examples of use in multi-chart and basket indicators are shown. Readers receive a ready-to-use API for reliable portfolio calculations.

The article builds an MQL5 indicator that recovers the risk-neutral density from an option chain via the Breeden–Litzenberger identity. Quotes are inverted to implied volatilities, the smile is smoothed and priced back to arbitrage‑free calls, and the second derivative yields the density. The tool reports probabilities above any level, the expected move, skew and kurtosis, and overlays the realized-return distribution for comparison.