Articles on trading system automation in MQL5

We are developing and implementing the Flock by Leader algorithm in MQL5: sub-flocks are formed based on the ARF metric, and the leader is determined by the highest personal best rather than by the centroid's position. We present the update formulas for the swarm roles and the separation mechanism. The C_AO_FBL class is compatible with the test bench and has been tested on the Hilly, Forest, and Megacity functions with dimensionalities from 10 to 1000 coordinates, which simplifies reproduction and comparison.

A Sharpe ratio read off the best of many optimization runs is not the number it looks like. This article ships a reusable native CDeflatedSharpe class that turns a raw Sharpe into an honest confidence statement. The Probabilistic Sharpe Ratio corrects it for sample length and for skew and kurtosis; the Deflated Sharpe Ratio adds the correction almost nobody applies, for the number of variants you tried before keeping the best. Everything is from scratch, the sample moments, the normal CDF and its inverse included, so there is no Python, no DLL and no library. On a real sweep of 56 moving-average variants on XAUUSD the winner looked significant at 98.5 percent by PSR, then fell to 90.6 percent once the 56 trials were admitted, below the usual bar. That gap is the selection bias, made measurable.

This article implements an MQL5 Expert Advisor that scores each impulse leg behind a swing extreme with a 0–100 Candle Imbalance Score. The score combines four bounded components—body dominance, directional consistency, leg efficiency, and Fair Value Gap coverage—to filter weak swings before any pullback setup is armed. You will see enforced swing alternation, a state machine for first-clean-retest-only entries, and a three-mode trailing stop, enabling consistent setup selection and exit management.

The article provides a detailed examination of the integration of the ST-Expert framework's approaches into the Extralonger architecture, which enables the simultaneous analysis of temporal and spatial representations of data. The results of testing on real historical data are presented, demonstrating the model's effectiveness and its robustness to market anomalies. The article describes the framework's modular structure, which ensures reproducibility, flexibility for research, and the ability to optimize components in stages.

The article describes the transition from four-voice debates to the Council of Fifteen: ten analysts, four independent risk managers, and a Chair, with strict voting rules. It examines the roles of the participants, the three-phase architecture, and the parallel execution of a full cycle in 10–15 seconds. It shows the operational log, the risk-gate rules, and backward compatibility so you can quickly connect the system to an Expert Advisor.

The series develops state persistence for MQL5 Expert Advisors. Part 1 delivers a crash-safe key-value store: a CStateStore class that saves through a temporary file and a rename, carries a versioned header with a checksum, and stores integers, doubles, strings, booleans, and double arrays, plus a demo advisor that resumes a counter and a rolling window after a restart. Readers get a compact include file and a pattern that protects the live state file if the process dies mid-save.

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.

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.

This article extends the candlestick encoding framework by separating previously unclassified bullish and bearish candlesticks into distinct symbols. Using an MQL5 script, historical data were automatically encoded, sequential patterns were extracted, and frequency statistics were generated. The results show that these newly identified candles represent a significant portion of market activity, revealing structural information previously hidden by the generic underscore notation. The expanded taxonomy improves the completeness of the symbolic representation while preserving an objective, quantitative framework for large-scale market structure analysis.

We implement three MQL5 classes that replace fixed 1R/2R/3R targets with data-driven scale-out levels. CExcursionTracker records each closed trade's maximum favorable excursion in R, CExitLadderCalibrator derives runs from distribution percentiles with lookback and minimum-sample controls, and CLadderExecutor executes them on open positions. The ladder recalibrates as trades accumulate and uses a fallback until enough samples exist.

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.

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.

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.