Articles with MQL5 programming examples

Trade authorization controls what a cooperating Expert Advisor is permitted to send, but not every account action passes through that code. A trade may originate from the desktop terminal, a mobile device, the web terminal, or another Expert Advisor. This installment develops an independent auditor that records live transaction stages through OnTradeTransaction(), reconciles available historical orders and deals within a configured recovery window, and writes an account-bound CSV journal. An on-chart panel summarizes the selected period and provides refresh, detail, and export controls without modifying trades.

This update adds manual position adoption and multi-symbol lifecycle management. The manager detects and tracks positions regardless of the chart, applies initial protection, transitions to break-even under fixed or ATR triggers, and validates broker constraints per symbol. Using the MACD sample for context, it demonstrates reusable management across instruments and manually opened trades.

The article develops a practical MetaTrader 5 tool that synchronizes charts of the same symbol across timeframes, starting from a script and moving to an interactive indicator. It uses time as a common reference, converts it to bar indices with iBarShift(), and navigates charts with ChartNavigate(). The result is a synchronized workspace for efficient MTF analysis. This is Part 1 of a three-part development series.

In the "Every tick based on real ticks" mode, the Strategy Tester's journal reports the minutes for which real ticks were absent or discarded and says that every tick generation was used for them. We show where the journal records this, measure how far back usable tick history goes on two demo servers, and check the ticks from inside an Expert Advisor, so a test range can be chosen by its real-tick share.

The article introduces a centralized cache that serves indicators to multiple strategy nodes. It encodes full indicator identity in keys, stores Standard Library objects in CArrayObj entries, reuses matching instances, verifies readiness, refreshes data, and deletes each indicator on final release. Readers gain a predictable resource lifecycle and reduced duplication in multi‑symbol Expert Advisors.

This article presents an exception-like model for MQL5 that prevents VM faults through defensive guards and cooperative TRY/CATCH/FINALLY macros. It introduces structured error objects (CException), result propagation (CResult), manual stack tracing, and integrated logging for clear diagnostics. Readers learn how to validate risky operations, propagate failures predictably, and keep EAs stable when facing zero‑divide, null access, invalid handles, and array‑bounds conditions.

The article consolidates trade, basket, and account risk into a modular framework for MQL5 EAs. Risk logic moves into reusable classes and shared types, leaving the EA with its inputs, signal, configuration, and call sequence. The result is a shorter strategy file and a second EA that reuses the basket tier and the shared conversions.

This stage connects structural validation with reference matching for double bottoms. The Expert Advisor scans completed history, filters pairs by the current structural rules, and compares normalized close and low profiles against validated samples from the library. Results that meet the similarity requirement appear in the panel for chart review and are saved to REFERENCE_MATCHES.csv.

We introduce a versioned persistence layer that saves and restores the zone engine's logical state across sessions. The snapshot preserves lifecycle status, interaction history, scores, blacklist prices, and the geometry and time anchors of user‑drawn rectangles, and it is reconstructed and reconciled with chart objects on startup. User zones enter with the minimum score so they integrate into the same lifecycle and trading pipeline.

This part adds a visual review layer to the double-bottom workflow. It checks the chart context, moves to the recorded period, and renders the range, two qualifying lows, neckline, classification, score, and the first decisive close. The annotations mirror the validator's indices and prices, helping you audit outcomes without changing sample data.

This recorder bridges visual labeling and systematic research in MQL5. After you position a rectangle, the EA extracts the candles it spans, derives candle geometry, normalizes OHLC values, assigns an ID, and saves the sample for reuse across sessions. The current category is the double bottom, and the module records the chosen sequence without enforcing a predefined pattern definition.

We implement visible bookmark annotations and a hand-off mechanism for cross-symbol and cross-timeframe recall. The navigator stores the target in terminal Global Variables, waits for stable bars after reinitialization, and rebuilds only matching markers with safe cleanup. Traders can inspect saved events in context and navigate back to them without reconstructing chart settings.

We use the MetaTrader 5 AI Assistant to execute the full workflow end to end: create an EA from a natural‑language prompt, compile it, break and watch it self‑correct from compiler output, backtest it, and compare a controlled re‑run. The article details the underlying MCP extensions, configuration and safety limits, and how to connect external AI clients. Readers get a repeatable process for building and testing EAs inside the platform.

Learn how Smart Trendline Manager separates creation from lifecycle control while handling both manual and auto-generated trendlines. It demonstrates discovery, registration, proximity and touch handling, bounce/break confirmation, post-break resurrection, expiration, and state-driven visualization. This gives you a consistent, configurable way to manage multiple lines through one orchestrated update process.

MACD signal-line crossovers often reflect range noise rather than true momentum shifts, producing whipsaws. We apply a two-layer meta-labeling pipeline with an Optuna-optimized regime gate to filter entries on EURUSD H1, turning a gross-losing rule into a positive but not statistically significant track. A secondary Random Forest adds no lift due to too few gated samples, clarifying when filtering helps and when the ML layer is data-starved.

This article presents a practical debugging workflow with MetaEditor's integrated AI Assistant and a comparison to the previous external approach. We fix a controlled set of syntax and API errors in a D1 PriceMarker EA, inspect modifications, and recompile. The result is validated in the Strategy Tester, with clear boundaries between compilation success and required runtime checks.

The article presents a session analysis workflow in MQL5 that standardizes sessions as structured data with timing, OHLC, range, net move, and the sequence of extremes. It measures how range builds at 25%, 50%, 75%, and 100% of session duration. The chart shows session markers, a compact comparison panel, and an interactive inspector to review individual occurrences.

Many traders know what they want to analyze or automate but cannot navigate MetaTrader 5, use MetaEditor, or translate an idea into working MQL5 code. This article shows how the AI Assistant and MCP turn one natural-language prompt into a complete workflow: strategy development, editing, debugging, chart interaction, testing, and permission-controlled trading. We apply the process by building MCP_Accessibility_Assistant.mq5, leaving you with a working, accessible EA and a reusable prompt-driven development method.

In this article, we will examine how to build and apply Markov chains in market conditions: from selecting states and counting transitions to generating forecasts of trajectories and levels. We will also see how Markov chains can be applied to qualitative and quantitative data, ways to account for rare events, and the impact of the forecast horizon. Examples are provided using prices and indicators, as well as an option for evaluating a sequence of trades, with ready-to-use implementations in MQL5.

This article describes an MQL5 Expert Advisor that models the market as a random graph rather than a fixed structure. Bars are encoded into discrete states; bar-to-bar moves form a decaying, Laplace-smoothed transition matrix, and a k-step random walk yields a bounded directional signal gated by an Erdos–Renyi null-model test. A separate trade-outcome graph over R-milestones learns survival probabilities to manage exits, turning position management into evidence-based rules.

The article extracts the series' divergence logic into DivergenceEngine.mqh, a reusable header for MQL5 indicators and Expert Advisors. It details the struct-based design, oscillator options (MPO4 or RSI), pivot and state handling, and the minimal access API. A Parabolic SAR EA demonstrates integration by adapting the acceleration factor from the detected divergence, providing a clear pattern you can reuse without duplicating code.

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 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.

The article develops a multi-symbol basket EA that standardizes prices, derives PCA weights with native MQL5 matrices, forms a synthetic spread, and trades z-score deviations from a rolling mean. It validates symbols and synchronized history, stabilizes component orientation, maps signed weights to leg directions, and applies broker-aware volumes, stops, and netting rules. Basket entries run with rollback protection and chart status, with a reproducible testing procedure.

This article presents a reusable CTable class for building chart-based tables in MQL5. It covers table architecture, creation and destruction of objects, coordinates and sizing, cell properties, horizontal/vertical headers, dynamic row/column edits, object naming, index conversion, and efficient refreshing. You will be able to assemble consistent, aligned on-chart dashboards for market data, indicators, and signals with minimal boilerplate.

In this article, we will take a first step toward showing how to implement operator overloading for the index operator and the assignment operator, while striving to offer a practical and interesting approach for everyone. What we will see here is only part of what I still intend to show, and it is directly related to the overloading of these operators.

At first, this article may seem rather confusing because of the material I'm going to cover in it. Nevertheless, I've tried to explain everything as simply and clearly as possible. I hope you'll understand what I'm about to show you here, and that it will come in handy someday.