Articles on the MQL5 programming and use of technical indicators

This article benchmarks CUSUM_Breakpoint.mq5 against the Siegmund ARL₀ prediction on live‑like data. The empirical false‑alarm rate is about five times higher than theory for all tested symbols and timeframes, and confirmations show sensitivity to variance changes over mean changes. Practitioners should calibrate h and k on the target instrument's history and apply the signal to manage volatility regimes, not to infer directional shifts.

This article presents a swing-based volume profile indicator in MQL5 that analyzes platform tick volume within completed high-to-low and low-to-high legs. It confirms swing points, partitions each leg's range into ATR(200)‑adaptive price bins, distributes tick volume, and marks the Point of Control. The indicator draws a ZigZag and renders the profile on chart rectangles, helping you study volume concentration within each swing relative to price structure.

This article walks through creating an MT5 indicator that ingests option chains from native symbols or CSV, inverts prices to implied volatility via a hybrid Newton–Raphson/bisection method, and assembles a clean strike–expiry grid. It then renders a shaded, rotatable 3D surface with the platform's DirectX layer, enabling clear, in-terminal analysis of skew and term structure using live or file-based data.

This article builds a Kolmogorov–Arnold Network (KAN) in MQL5, where every edge carries a learnable B‑spline curve rather than a scalar weight. We construct the spline basis, assemble edges and a layer, and fit all coefficients by ridge‑regularized least‑squares in a single solve. The model is delivered as an indicator that visualizes the learned curves and an Expert Advisor that acts on the prediction, providing an interpretable, reusable codebase.

This article implements the MMAR Simulation Engine that turns fitted parameters (H, distribution, coefficients, sample volatility) into synthetic price paths. It builds multifractal trading time via a multiplicative cascade, synthesizes fractional Brownian motion with Davies–Harte or Cholesky, scales it to target volatility, and composes the process by time deformation. Readers get a reusable MQL5 class, method choices by path length, and validation steps for scenario testing and Monte Carlo use in the next part.

This article implements BOSS from scratch in MQL5 and applies it to regime classification: SFA turns windows into words, bags record word frequencies, and an ensemble over window lengths votes on labels. We cover the encoding steps, the BOSS distance, training with auto-generated regime labels, and practical parameters. A BTCUSD benchmark versus DTW shows higher macro accuracy on clean data and markedly faster inference.

An MQL5 port of four entropy estimators — Shannon, Plug-In, Lempel-Ziv, and Kontoyiannis — operating on the intrabar tick-rule sequence. CopyTicksRange() limits data to the broker's cached tick window, so features apply to recent bars only. The implementation encodes bid-direction ticks from MqlTick, replaces NumPy-dependent steps with array-based methods, and ships CEntropyFeatures.mqh and EntropyViewer.mq5 for EA and indicator use.

This article implements a self-contained Isolation Forest library for MetaTrader 5 with no labels, no distribution assumptions and no external dependencies. It details a reproducible 64‑bit generator, tree/forest construction, scoring and feature design, then verifies results against Python and market data with two null models. The package includes an indicator that plots the decision variable and a gate example. Readers get a validated library, clear limits of applicability and a practical way to calibrate thresholds.

We outline a modular analyzer for MetaTrader 5 that separates detection, interpretation, and visualization. The engine identifies swing highs and lows, assigns structural labels, evaluates impulses and pullbacks, and stores results in a market state object. An on‑chart dashboard and interactive inspection tools make the latest structure and measurements immediately accessible.

This article delivers a native MQL5 MiniRocket: 84 fixed convolution kernels yield 9,996 features quickly and deterministically, requiring no training loop and no external runtime. We verify the port against sktime and a float64 reimplementation, then run a reproducible audit of seven classic setups; planted and coin‑flip controls confirm correctness, and a 25%‑flipped control sets the detection threshold.

We merge GARCH(1,1) variance projections with ATR plus Bollinger-Bands patterns to form an algorithm that could optionally be used with volatility-scaled LSTM within LSTM Wizard-ready signal class. We cover feature scaling, mode scoring, thresholds, and safety checks. Readers can replicate backtest/forward test results to verify if the recurrent layer gives incremental discrimination over our deterministic baseline.

The article introduces the APARCH volatility process to the MQL5 library via the CAparchProcess class, estimating the power exponent (delta) jointly with other parameters. It details the recursion, parameter bounds, stationarity constraints, and starting values and reports SLSQP solver updates that streamline optimization. Implementation correctness is partially validated by reproducing approximations of GARCH and GJR-GARCH conditional volatility under parameter restrictions. A companion APARCH indicator visualizes conditional volatility, standardized residuals, and delta to track volatility dynamics and parameter drift.

We consider the methods with which Isotonic Regression calibrates raw RSI, Stochastic and price-action signal scores into probabilities that are sorted, while a separate Probability based Neural Network evaluates similar historical market states. This article uses both approaches in a ready-made MQL5 custom signal class that is compatible with MQL5 Wizard and provides up to 7 selectable entry modes. Reproducible tests compare isotonic-only signals with the combined Isotonic-PNN model to assess whether the network adds useful information beyond the simpler baseline.

We introduce a Partial Information Decomposition library for MQL5 that decomposes two sources about a target into four atoms: unique to each, shared, and synergy. The implementation uses quantile binning, tabulated logarithms, and a maximum-entropy fit (for I_ccs), and it pairs results with a block-permutation null because atoms sit above zero on finite samples. Use it to screen indicator pairs and judge significance, including family-wise correction.

We implement a Time Series Shapelet library for MQL5 that finds the subsequence of price history whose z-normalized shape best separates two labels and derives the decision threshold from information gain. Because candidate searches on prices always return a winner, the fit includes a purged hold-out and a calibrated block permutation null. You get a reusable facade and rules you can plot and evaluate against an explicit noise floor.

This article delivers a native MQL5 MiniRocket: 84 fixed convolution kernels yield 9,996 features quickly and deterministically, requiring no training loop and no external runtime. We verify the port against sktime and a float64 reimplementation, then run a reproducible audit of seven classic setups; planted and coin‑flip controls confirm correctness, and a 25%‑flipped control sets the detection threshold.

This article implements the Maximal Information Coefficient (MINE) for MQL5, including the grid search with dynamic programming and the four MINE statistics. It explains why raw MIC has a nonzero noise floor and builds a permutation null to judge significance. The result is a verified library with a dependence scanner and a chart indicator, allowing you to test features and interpret scores consistently across relationship shapes.