Articles on manual and algorithmic trading in MetaTrader 5

The article presents a full MQL5 implementation of a multi-symbol trend system: dual EMA crossovers for entries, ADX to avoid ranges, ATR to normalize position size, and a heat monitor to cap total portfolio risk. We explain the architecture, calculation details, and entry/exit logic on daily bars. The result is a practical EA template for systematic, risk-aware portfolio trading.

This article implements a typed message bus over Windows named pipes to replace MetaTrader's untyped GlobalVariables for inter‑EA communication. A broker EA manages the server and registry, serves multiple slave EAs, and responds with a live, per‑symbol‑attributed portfolio risk measure. It also explains the non-blocking accept pattern that preserves terminal responsiveness, and includes a dashboard and a test script.

We convert the Part 15 decision‑forest classifier into a regime‑adaptive Expert Advisor that decouples statistical inference from trading authority. The EA trains on completed bars, scores each new completed bar, and confirms stable bullish, neutral, or bearish regimes before acting. It then applies spread, ownership, risk, and execution checks to authorize opening, holding, closing, or blocking a position.

The article discusses the Butterfly Optimization Algorithm, which is based on modeling foraging using the sense of smell. We will analyze the original formulas, identify and correct errors in motion equations, add a mechanism for maintaining population diversity, and present the test results.

Establishes the persistence foundation for a prop-firm compliance EA in MetaTrader 5. It introduces rule inputs and status enums, separates live account state from stored settings, validates percentages and thresholds, and implements SQLite open/close, schema creation, and prepared save/load operations. Using the account login and server as a composite key, the EA restores existing settings and updates them when inputs change.

The article discusses the Cricket Algorithm, a metaheuristic optimization method that combines elements of the Bat Algorithm and the Firefly Algorithm with the physical laws governing the propagation of sound in the atmosphere. The algorithm simulates the behavior of crickets that navigate by the chirping of their conspecifics, using Dolbear's law and acoustic formulas to guide the search for best solutions.

This article presents a modular position sizing engine for MetaTrader 5 that operates on normalized R-multiples. A layered pipeline combines enriched trade statistics, a generalized Kelly edge estimate, volatility-aware adjustment, Monte Carlo calibration under ruin and drawdown limits, a continuous risk policy, an exposure guard, and a broker-aware lot calculator. The output is a broker-valid lot size with an optional CSV audit trail, providing a transparent prototype for implementing modern risk controls in native MQL5.

The EA now defines risk by percentage, fixed cash, or fixed lot and can measure percentage against balance or equity. It supports market, limit, and stop orders, sizes from the planned entry, and enforces spread‑aware stop minima. Additional safeguards include downward volume rounding, explicit handling when the minimum lot exceeds target risk, and pending‑order distance/expiry checks, organized under a Plan–Validate–Execute structure.

Risk-based lot sizing can still exceed what free margin allows. The article adds a margin-aware cap using OrderCalcMargin(), an optional adaptive cap that scales with ACCOUNT MARGIN LEVEL, and a single pre-trade validation gate that unifies position limits, risk sizing, and margin checks. Readers get concrete code to prevent order rejections and over-committing margin, with clear logs when a trade is reduced or skipped.

The article discusses the ECO optimization algorithm, which is based on ecological concepts: populations are grouped into habitats based on territorial proximity, exchange genetic material within habitats, and migrate between them. Despite its wide range of operators and elegant biological metaphor, the algorithm produced a certain result discussed below.

Part 5 moves risk control from single trades to a basket-level framework. The EA aggregates its own positions, computes volume‑weighted entry, floating P/L including swap, and used margin, then enforces limits on combined loss, margin, position count, and time underwater, while logging maximum adverse excursion. A companion mean‑reversion EA demonstrates target‑based sizing and caps on implied risk that remains hidden when trades are evaluated in isolation.

We build a robust MQL5 detector for double tops and double bottoms that first confirms the H4 trend, then validates six conditions (point equality, neckline placement, ordering, width, height, and ATR‑based tolerances). The neckline break is timed on the chart's timeframe, and a three-state machine ensures each pattern trades once. The measured‑move target translates structure into clear exits.

The article examines the EOSA algorithm, which is inspired by the mechanisms of Ebola virus transmission: short-distance transmission through close contact (exploitation) and long-distance transmission through travel (exploration). An analysis of the original publication revealed critical issues in the mathematical formulas and an epidemiological model that was impractical to implement, which required a significant overhaul of the algorithm to produce a workable implementation.

This part implements risk-based position sizing for the EA. Lot size is derived from account balance, a chosen risk percent, and ATR-based stop distance, then confined and rounded to the broker's volume rules and minimum stop levels. An optional drawdown-aware layer reduces risk during equity declines. Readers get a reproducible sizing function that keeps per-trade risk consistent and orders acceptable to the server.

The ECO (Ecological Cycle Optimizer) algorithm offers an interesting metaphor for applying the concept of the ecological cycle to the field of metaheuristic optimization. The idea of dividing a population into trophic levels — producers, herbivores, carnivores, omnivores, and decomposers — creates a hierarchical search structure, in which each group contributes to the overall optimization process.

This article develops a market‑structure‑driven intraday breakout system based on Bill Williams fractals. We define session bounds, derive volatility‑scaled stops, use fixed risk and take‑profit multipliers, and limit trades to one per direction. An MQL5 Expert Advisor, visualization and statistics, tick-level backtests, an ORB comparison, and a cross-asset forward test provide a complete, replicable workflow.

The Dandelion Optimizer (DO) turns the simple flight of a seed carried by the wind into a mathematical search strategy. The three phases — vortex rising, drift toward the center of the population, and landing along a Lévy-flight trajectory — form an elegant metaphor that yields interesting results in practice.

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.

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.

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.

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.

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.

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.

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 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.

This article presents a unified news model and a set of reusable MQL5 classes for working with the MetaTrader 5 Economic Calendar. You will retrieve, filter, and cache events by time, currency, country, and importance using a single interface across three providers: built-in calendar, CSV, and SQLite. The framework supports export/import, next/previous event lookup, and reliable strategy‑tester backtesting without changing trading logic.

The article discusses the Differential Search Algorithm (DSA), which simulates the migration of a superorganism in search of optimal living conditions. The algorithm uses a Gamma distribution to generate a pseudo-stable random walk and offers four strategies for selecting the direction of movement, along with three coordinate mutation mechanisms. How will this method perform?

The article describes an approach to trade labeling using oscillators for machine learning models. This eliminates look-ahead bias. It has been shown that this type of labeling does not lead to model overfitting, and the strategies continue to perform well over the long term.

The article's system introduces CBasketManager: positions are grouped by a comment‑based basket ID, analyzed as a single snapshot, and controlled with a unified equity stop. CBasketScanner computes aggregate P&L and volume‑weighted pip performance; CBasketStopRegistry triggers coordinated closure on threshold breach; CBasketExecutor adapts to the broker's filling mode. A lightweight dashboard shows live legs, volumes, stops, and distances for faster basket decisions.

We complete the native MQL5 port of Kronos: the decoder, the predictor's decode_s1 and decode_s2 stages with their cross-attention traps, and the autoregressive loop that produces a multi-bar forecast. Then we profile and make it roughly 4.5x faster with an exact KV-cache and pre-transposed weights, verifying every stage against PyTorch.

We are adding to our web application the ability to retrieve and display information about the terminal instances’ trading accounts, including balance, profit, connection status, and other important details. We will also implement a flexible configuration system that lets you manage application settings via an external JSON file, and improve the user interface of the main page.