Programming Articles on MQL5.com

Raw feature correlations contain estimation noise and a shared market-mode component that distort clustering. We fit the Marcenko–Pastur noise ceiling (with an effective sample size correction), apply constant-residual denoising and market detonation, and run the Optimal Number of Clusters routine. The result is a cleaned correlation matrix and stable cluster labels that avoid substitution effects and feed clustered MDI/MDA in the next article.

This article builds a Hull Moving Average Momentum indicator in MQL5 by combining raw price momentum with Hull MA smoothing. We compute momentum as the close-to-close difference over a user-defined length, form 2×Fast WMA − Slow WMA, then apply a final WMA with a square‑root period. The implementation covers inputs, buffers, warm-up/recalculation, and visualization with a color-coded line and zero-line filling, helping interpret positive/negative momentum without treating zero crossings as signals.

We implement a History Navigator for MetaTrader 5 that jumps the chart to an exact historical period by date and time. The dialog validates inputs, converts them to datetime, and searches bar times with a binary-search routine before centering the selected candle. The navigation logic is separated from chart control, improving testability and maintenance, and a one-click return restores the live market view.

This article opens a step-by-step 2D graphics engine for MetaTrader. It standardizes ARGB colors and implements a reusable surface: a uint pixel buffer uploaded as a dynamic resource and shown via one OBJ_BITMAP_LABEL. You will draw rectangles and a vertical gradient, check real transparency, and learn an efficient update path with a single Flush call.

We are pleased to present the final part of our series on GinAR — a neural network framework for time series forecasting. In this article, we analyze the results of testing the model on new data and assess its robustness under real-market conditions.

The Dendritic Cell Algorithm (DCA) is a metaheuristic inspired by the mechanisms of the innate immune system. Dendritic cells patrol the search space, accumulate signals about the quality of positions, and reach a collective decision: whether to exploit what they have found or to continue exploration. Let's take a look at how a biological model for detecting pathogens is transformed into an optimization algorithm.

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 invite you to explore a new implementation of the key components of the GinAR framework — an adaptive algorithm for working with graph-structured time series. This article provides a step-by-step breakdown of the architecture and the algorithms for the forward pass and error backpropagation.

This article develops a visual position planning tool in MQL5 for evaluating trade setups before execution. The tool utilizes interactive Entry, Stop-Loss, and Take-Profit lines to calculate the stop distance, risk amount, estimated position size, potential reward, and risk-to-reward ratio directly on the chart. It supports market, limit, and stop order scenarios while keeping the focus strictly on planning and analysis rather than trade execution.

A state-driven Position Lifecycle Manager brings structure to post-entry trade handling in MetaTrader 5. It discovers open positions, tracks them via managed objects, applies ATR-based protection, executes break-even transitions, and removes completed trades, with a clear NEW → PROTECTED → BREAKEVEN → CLOSED flow. The article shows integration with the standard MACD EA to enable reuse across strategies.

The article presents an adaptation of the Deterministic Dendritic Cell Algorithm (dDCA) for continuous optimization problems. The algorithm, inspired by the immune system's Danger Theory, uses a signal accumulation mechanism to automatically balance exploration and exploitation within the search space.

We invite you to explore an innovative approach to forecasting time series with missing data using the GinAR framework. The article demonstrates the implementation of key components using OpenCL, which ensures high performance. In our next publication, we will take a detailed look at how to integrate these solutions into MQL5. This will help understand how to apply the method in practice in trading.

This article builds a trend-following Expert Advisor that trades momentum spillover across markets, implemented fully in MQL5 without external solvers. It detects leaders with Derivative Dynamic Time Warping, learns a sparse weighted network by convex optimization, and propagates momentum through it with a reverting response. Readers get a step-by-step, reproducible pipeline and a working EA ready to run in the Strategy Tester.

A robust breakeven implementation for MQL5 is built around live spread sampling and correct pip-to-price conversion by symbol digits. CBreakevenManager moves SL to open_price ± spread ± buffer once a real‑pip activation threshold is reached and prevents duplicate modifications. A demo EA shows the behavioral difference versus a naive breakeven, and a script verifies core calculations.

The article describes the practical implementation of a hybrid algorithmic trading system that combines quantum computing (IBM Qiskit) and gradient boosting (CatBoost) to predict movements in the EURUSD pair on the hourly time frame. The system extracts four unique quantum features from a probability distribution across 256 states using eight qubits and, in combination with classical indicators and delta encoding of time categories, achieves 62% accuracy on 15,000 candlesticks.

We build the Matrix Profile for MQL5 from the ground up and keep it numerically stable on real prices. The library includes rolling statistics, a radix-2 FFT powering MASS, and a STOMP self-join, with results matched to stumpy. A compact facade, an indicator that draws the profile and flags discords, and a demonstration Expert Advisor show how to read and use the signal in practice.

Net profit and win rate do not tell you if a strategy's edge is statistically real. This MQL5 toolkit analyzes return series built from price data or deal history and reports t‑statistics, p‑values, and confidence intervals using one-sample and Welch t‑tests, the Mann–Whitney U test, and volatility‑regime analysis to support evidence‑based trading decisions.

The article details a complete MQL5 implementation of a draggable slider for controlling ranges on the chart. It introduces the CDragHandle class, private state, public APIs for dimensions, colors, range, and value, plus Refresh* and UpdateHandlePosition logic and event processing. A working example changes CHART_SCALE, demonstrating how to connect the control to platform properties.

We integrate parameter configuration into the indicator search workflow in MQL5. A central repository describes each indicator's inputs, a dynamic dialog renders controls from those definitions, and the dialog validates entries and converts them to MqlParam. The chart launcher then creates the indicator with IndicatorCreate using the provided values. This streamlines attaching indicators with custom settings on the chosen symbol.

We build a trade throttle for MQL5 EAs using a token bucket with a priority queue to control order submission rate. Tokens refill at a configurable per‑second rate, allowing short bursts up to capacity and then enforcing sustained throughput. When the bucket is empty, requests are queued and later released by priority with FIFO tiebreaks. This keeps execution within safe limits without discarding valid signals under load.

This article implements an MQL5 custom indicator that detects Larry Williams Oops gap reversals and marks bullish and bearish arrows on the chart. It details configurable gap and validity thresholds, same-bar or later confirmation, first-fill-only logic, historical backfilling, and incremental updates so signals remain consistent on both history and newly completed bars.

A practical bridge from the options market into MetaTrader 5 for gold. We compute near-the-money implied volatility by solving Black-Scholes from quoted prices, compare it with 30-day realized volatility, and use the ratio as a regime proxy. A Python feed publishes the value, an MQL5 script consumes it with WebRequest, and a background service keeps a panel current and alerts on changes. Source code for all parts is provided.

MQL5 lacks native unit testing, so utility bugs in lot sizing, pip value, and normalization often slip into production. This article presents a zero‑dependency framework built from preprocessor assertion macros, interface‑based suites, and a central runner/formatter. It runs as a script in OnStart, executes deterministic tests, and prints pass/fail summaries to the Experts tab to catch rounding, boundary, and error-handling defects before deployment.

We invite you to learn about the K²VAE framework and how the proposed approaches can be integrated into a trading system. You will learn how the hybrid Koopman–Kalman–VAE approach helps build adaptive and interpretable models. The article concludes with practical results from using the implemented solutions.

We build a rolling price channel by fitting the 0.1, 0.5 and 0.9 conditional quantile lines via IRLS with pinball loss, packaged as a reusable class and two MetaTrader 5 indicators. We verify in-sample coverage, examine quantile crossing, and compare the channel width with ATR, Bollinger and regression widths on matched horizons. Tests in the Strategy Tester show the edges are descriptive, while the normalized width works as a volatility/regime feature.

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.

Bollinger Band mean reversion degrades in trending regimes when ADX is high and bandwidth expands. We separate direction from trade selection with a two‑stage meta‑labeling pipeline: a gradient‑boosted secondary classifier trained with PurgedKFold on band‑specific features (BBP, BBB, bandwidth regime) outputs action probabilities that drive probability‑based bet sizing. The MQL5 implementation loads the ONNX model and applies position sizing within a two‑EA architecture to filter low‑quality band touches.

This article presents a circuit breaker for MQL5 that monitors combined daily P&L (realized plus floating) on every tick and compares it to a configured loss limit. On breach, it closes positions, cancels pending orders, and activates a HALTED state that blocks further order submission in the EA until server‑time midnight. The package provides a chart dashboard, a demo Expert Advisor, a verification script, and notes on extending the halt signal across EAs.

Learn how to build an MQL5 Expert Advisor that detects and trades Larry Williams’ Oops Gap Reversal pattern using objective gap rules and later-bar confirmation. The EA tracks setup expiration, prepares stop-loss and take-profit levels, supports manual or risk-based position sizing, executes market orders, and is evaluated through historical testing.

An MQL5 analyzer reconstructs completed trades, records acceptance labels, and measures the accepted-minus-rejected mean net-profit difference. It benchmarks that statistic against individual permutations, equal-block permutations, and circular shifts while preserving the accepted count. Block-size sensitivity, CSV exports, and coordinated base/filtered passes separate statistical selection evidence from operational effects on profit, drawdown, and efficiency metrics.

This article presents CSwingEngine, a reusable MQL5 class that detects H4 swing highs and lows, labels them HH, LH, HL, or LL, and classifies market structure as trend or range. Swings are always computed on H4, regardless of the attached chart, and each point draws correctly on lower timeframes via native datetime anchoring. The engine exposes a clean interface to query the current trend and retrieve the swing array for context-aware pattern logic.

This article presents a compact MQL5 library for market-complexity analysis: LZ76 complexity and Normalized Compression Distance built on a SAX symbolizer, exposed through a simple facade and an efficiency indicator. It explains the discretization choices, normalization, and distance formulation, and validates the code with unit checks and an independent cross-check. You get a ready-to-use library and indicator, plus a disciplined way to interpret readings with a shuffle null and a direction check.

A naive MQL5 port of trend-scanning features recomputes each candidate window per bar at O(H·L) cost. This article introduces CTrendScanningFeatures.mqh, which maintains three running sums per horizon and updates them in O(1) per bar, verified against a Python reference. The indicator exposes four causal buffers - window, slope, t_value, rsquared - at the confirmation bar and corrects a sign inversion present in the original backward labeling mode.

This article implements an online logistic‑regression trade filter in native MQL5 and integrates it into an EMA‑crossover EA with a closed‑trade feedback loop. It details the shared class, features, SGD update, persistence, and a read‑only probability view. Synthetic experiments cover multi‑seed separation, calibration, feature ablation, regime‑shift baselines, and hyperparameter sweeps. You get reproducible scripts and a walk‑forward protocol to validate the filter on your own instrument.

This MQL5 engine applies configurable profit ladders in R‑multiples to manage partial closes reliably. It prevents stranded remainders by rounding to lot step, computes close percentages from the original entry volume, and moves the stop to breakeven when configured. A supported filling mode is chosen automatically, and the download includes seven include files, a demo EA, and a verification script.

This article is a practical walk-through of the MetaEditor debugger using a rolling z‑score indicator with two planted bugs: an off‑by‑one array access and a silent wrong‑denominator variance. We show how to set breakpoints, step through code, read the call stack, and inspect values in the Watch window. You will learn a repeatable method to catch both crashing index errors and tiny numerical biases that charts cannot reveal.

This article details a practical framework for converting MetaTrader 5 trendlines from static drawings into managed runtime entities. It covers object discovery, event-driven synchronization of user edits, and confirmation logic based on ATR multipliers and closed candles. A central manager coordinates multiple lines and updates their visual state. Readers can implement consistent, extensible rules for detecting proximity, validating bounces, and confirming breakouts.

Aggregate metrics alone do not reveal how a trade sequence manages risk. This MQL5 tool analyzes closed positions to flag four structural patterns: post-loss volume escalation, overlapping same-direction entries, asymmetric payoffs, and a classical risk-of-ruin figure. The results are merged into a configurable A-F grade with concise recommendations to guide further review.

The article details a master–agent MQL5 framework that mitigates cross-symbol risk concentration. A single Portfolio Controller publishes risk limits and halt flags to Instrument Agents through shared channels and a readiness flag, while agents size orders only within the published budget. It contrasts global variables, named pipes, and files, and clarifies timer intervals and latency so data allocation may be up to one cycle stale without breaking coordination.

Price outliers distort indicators based on the mean and standard deviation. This article delivers a robust MQL5 library (RobustStats.mqh) implementing the median, 1.4826-scaled MAD, and Theil–Sen slope, plus three drop‑in indicators that replace Bollinger Bands, the linear regression channel, and the z‑score oscillator. A comparison overlay and a breakdown‑point measurement on EURUSD show how the robust instruments hold their shape when a single spike moves the classical ones.

The article describes the development of an MVP prototype for an autonomous trading bot for MetaTrader 5 that uses large language models (LLMs) via the OpenRouter API to analyze the market and make trading decisions. A Python script retrieves historical OHLCV data, sends it to an LLM for technical analysis based on support/resistance levels and Price Action patterns, and then automatically places orders with specified stop loss and take profit levels.

We invite you to explore a new approach that combines classical methods and modern neural networks for time series analysis. The article provides a detailed explanation of the architecture and operating principles of the K²VAE model.

The article describes the process of fine-tuning a language model for trading based on real historical data from MetaTrader 5. The base model, which has only theoretical knowledge of technical analysis, is trained on a thousand examples of the real behavior of currency pairs (EURUSD, GBPUSD, USDCHF, USDCAD) over 180 days. After being trained using Ollama, the model begins to understand the specific characteristics of each instrument.