MQL5 Programs Market

This article presents a complete Expert Advisor built around Stan Weinstein's Stage Analysis method. The EA classifies the market into one of four stages using the 30-week moving average slope and position and volume behavior, then trades only Stage 2 breakouts long and Stage 4 breakdowns short. It explains each stage, how to detect it programmatically, and why the method's discipline—trading only in the correct stage—is what produces the edge.

We port AFML Chapter 17 structural break tests to MQL5 as a single include, CStructuralBreaks, delivering six bar-indexed features for EAs: CSW statistic and critical value, Chow-Type DFC, SADF with a rolling lookback (default 252), SM-Exp, and SM-Power. SADF uses O(L²) rolling windows for real-time viability. A companion StructuralBreaksViewer indicator plots all series with per‑series visibility and optional z‑score normalization. SB_EMPTY marks invalid values for safe integration.

The article presents an innovative hybrid system for forecasting exchange rates that combines a linear autoregressive model with a U-Transformer architecture for residual analysis. The system automatically switches between signal sources depending on their quality and includes complete trading logic with averaging/pyramiding strategies. The key advantage of this approach is that the neural network is trained on the residuals of the linear model, which simplifies the task and reduces the risk of overfitting. The implementation is done entirely in MQL5 and is ready for use in real trading with automatic adaptation to changing market conditions.

We build a Support and Resistance Volume Profile indicator that adapts to the current viewport in MetaTrader 5. You will learn viewport detection, dynamic SnR identification, zoom‑driven bin sizing, min‑max volume scaling, and fast on‑chart rendering controlled by OnChartEvent. This approach expresses the relative strength of SnR levels with volume, keeping the chart focused on actionable reaction zones.

We present a production‑ready implementation of AFML Chapter 17 structural break tests. The module includes Chu-Stinchcombe-White (one-/two-sided), Chow-type DFC, SADF across six models (linear, quadratic, sm poly 1, sm poly 2, sm exp, sm power), plus QADF (q, v) and CADF (q), returning bar-indexed scalar features. We address the book snippets' scaling issues and argument‑order pitfall, and show how a fixed lookback (L=504) bounds SADF cost to O(L²) per bar for regime detection.

We invite you to explore the ACEFormer architecture — a modern solution that combines the effectiveness of probabilistic attention with adaptive time series decomposition. This article will be useful for those seeking a balance between computational performance and forecast accuracy in financial markets.

Maximum drawdown is one number that hides what really matters: how often an equity curve declines, how long it stays below a previous peak, and how quickly it recovers. This article builds a native MQL5 tool that reconstructs the underwater curve, breaks it into individual drawdown episodes (depth, duration, recovery time), computes the Ulcer Index, Pain Index, and Recovery Factor, and combines them into a single resilience grade with practical recommendations. No external libraries, no Python, no AI.

The article delivers MQL5 implementations of FIGARCH and HARCH and updates the volatility library for long‑memory processes. It provides code for Hurst and GPH testing, parameter setup (truncation and horizons), and scripts for fitting, forecasting, and simulations. Readers learn how to apply and compare the models on market data to select an appropriate specification.