Clemence Benjamin / Perfil

This part focuses on practical data analysis in MQL5 with dataanalysis.mqh. We prepare a labeled dataset from bars, apply normalization, explore redundancy with PCA, and train a decision forest to classify future bar regimes. The article shows how to obtain out-of-bag estimates and permutation importance, helping you validate the model and understand which inputs matter most.

In this article, we refine a moving average crossover strategy with a momentum candle filter and an immediate retracement bar confirmation. When both conditions are met, a pending stop order is placed using a pivot-based stop loss and a 2R take profit. The complete MQL5 Expert Advisor code, finite-state-machine logic, and chart annotations are detailed.

The article bridges automated placement with manual analysis for the Fibonacci family in MQL5. It scans charts, identifies user Fibonacci objects, and normalizes their level arrays, interaction flags, and visuals per object type while preserving coordinates. With manual-priority enforcement, Expert Advisors can evaluate both human and code-generated tools reliably, without duplicates or runtime indexing issues.

The article provides a practical research setup for weekend gap analysis: MQL5 extracts precise pip‑based gaps and tracks fills, while Python performs statistical testing and visualization. You will compute fill rates by gap buckets, model fill probability with logistic regression, and assess time-to-fill via Kaplan–Meier curves. All steps are configurable and reproducible for EURUSD, GBPUSD, USDJPY and beyond.

A hybrid exit engine for MQL5 replaces static TPs with CRT-derived structural levels. The CRT_ProfitConserve class secures a partial at the first level and then trails the remaining position by structural anchors rather than fixed pips. The article walks through the class API, essential methods, and example usage in EAs, providing a clear path to embed CRT-based exits into existing strategies.

This part extends the series with a modular, event-driven MQL5 pipeline: swing detection feeds an object placer for trendlines, SR, Fibonacci, channels, and pitchforks; evaluators monitor interactions and generate signals; adaptive logic executes trades with valid stops per instrument. The topology manager synchronizes placement, scanning, and processing. The code is structured into reusable components for easy reuse and scaling.