History of MQL5.community development

The article is dedicated to the event-driven architecture in MQL5 and describes the transition from the monolithic OnTick model to distributed processing. We will consider predefined and custom events, services and messaging between programs, as well as common architectural errors. A practical example demonstrates how to organize interactions between indicators and an EA to reduce load, improve readability, and simplify maintenance.

The article presents a systematic approach to news trading in MetaTrader 5 using the built-in economic calendar: data structure, API functions, time synchronization rules, and event filtering. Methods of caching and incremental updating without overloading the server are described. The article also provides a working mechanism for exporting history to an .EX5 resource for deterministic testing using the same algorithm.

The article replaces hardcoded cost assumptions in triple-barrier labeling with measured inputs. An MQL5 script captures spread distribution, swap rates, and symbol metadata from your broker, and a Python model converts them into a broker-calibrated min ret you can pass to get events. Labels then reflect the actual round-trip friction for your instrument and holding period.

We refactor the Tools Palette from a flat, function-based panel into a modular, class-driven sidebar in MQL5. The design introduces supersampled canvas rendering for anti-aliased shapes, theme control, a category registry, snap alignment, and selective corner rounding. The result is a reusable, scalable sidebar foundation that you can extend with tool selection, dragging, and fly-out menus in future steps.

This article builds the foundation layer of a twelve-part MQL5 market microstructure toolkit. It implements guarded math helpers (SafeDivide, SafeLog, SafeSqrt, SafeExp, SafeTanh), robust data validation (ValidateSymbolV2, SafeCopyClose), trimmed statistical estimators (robust mean var), a linear regression slope, shared structs, and an FFT. You compile a single include file that hardens indicators and expert advisors against silent numerical failures and standardizes data flow for later parts.

This article tests three common filters on a standard MACD crossover for US_TECH100 H1 using five years of broker-native data. Filters are layered incrementally: regime, higher timeframe (HTF) alignment, and US session timing, to isolate each one's marginal impact. Results show session timing contributes far more than indicator refinements, while regime and HTF add little on their own. Includes a reproducible MQL5 regime classifier.

The article replaces clock-based sampling with López de Prado's alternative bar types and provides two aligned implementations: a unified Python module for batch tick histories and an object‑oriented MQL5 library for live EAs. It covers Parquet/Dask infrastructure, data cleaning, and a single API. Practical issues are solved explicitly: zero‑tick time‑bar filtering, imbalance threshold initialization, EWM state persistence, and parity between Python and MQL5 outputs.

Higher-timeframe CRT ranges are informative, yet traders often execute on lower timeframes without that context. We implement an MQL5 indicator that reads higher-timeframe OHLC, projects the full candle range, body, and wicks onto the active lower-timeframe chart, and marks entries, stops, and targets. This improves situational awareness and removes the need to switch windows.

We introduce an MQL5 discipline engine that enforces risk consistently at the account level. It continuously scans positions from any source, validates SL/TP, equity-based exposure, and target R:R, and automatically corrects deviations by setting levels or adjusting volume. The result is uniform risk structure across manual and EA trades, supported by on-chart feedback and mode-based control.

Create an object-oriented fair value gap (FVG) scanner in MQL5 and display liquidity gaps directly on a MetaTrader 5 chart, this article formalizes the imbalance geometry based on three candlesticks, synchronizes OHLC arrays with CopyRates, manages rectangles without leaks, and monitors mitigation in real time. It also shows how to integrate this class into an Expert Advisor with a strict new bar filter for stable and efficient execution.

This article presents an MQL5 implementation of AutoARIMA that builds ARIMA models without manual tuning. It estimates d via a variance-based heuristic, fits ARMA(p,q) by gradient optimization with Adam, and selects p and q using AICc. The code returns a one-step-ahead price forecast by differencing, model estimation, and integration back to price level, ready to call on a Close series.

The article applies the A* heuristic to market structure by modeling validated swing highs and lows as graph nodes and weighting edges with ATR‑normalized distance, spread, and noise penalties. The engine searches the most efficient route to infer trade direction and targets, then filters signals by directional ratio, total path cost, and opposing swings. It anchors TP to the final node and SL to prior structure, with on‑chart visualization and configurable inputs.

Our next focus in these series on ideas that can be rapidly prototyped with the MQL5 Wizard, is a Custom Trailing class that uses the Blooming Filter. Trailing Stop systems are an optional but very resourceful part to any trading system that we want to explore more in these series besides the traditional Entry Signals.

We present a chart-embedded RSI panel that removes the need for a separate window by attaching momentum directly to live price. The article explains the design and MQL5 code: real-time RSI retrieval, slope-based signal classification, and adaptive positioning. Traders get RSI value, state, and signal strength where decisions are made, improving clarity across timeframes.

This article delivers a production-grade MQL5 implementation of fixed-width fractional differentiation for live MetaTrader 5 feeds. We introduce a header-only CFFDEngine that precomputes weights without a fixed cap, performs O(width) per-bar updates, and avoids per-tick allocations. The FFD.mq5 indicator supports all ENUM_APPLIED_PRICE types and prev_calculated optimization. Validation scripts confirm numerical equivalence with the standard Python frac diff_ffd pipeline.