Articles on trading system automation in MQL5

A reusable MQL5 component measures floating P/L with swap, gross loss, two stop‑loss quantities, equity gap, margin level, and stop coverage, then classifies results for selected positions and the whole account. The snapshot records validity and readable reasons, supporting a quote‑age policy and missing‑stop handling. Integrate it into an Expert Advisor and block new exposure when thresholds or data checks fail.

We describe and implement CVO: infection as candidate generation, coordinate-wise normal perturbation, and a dynamic population. The algorithm is integrated into C_AO and has been tested on a standard benchmark. The analysis identifies a scaling-related cause of stagnation and provides a practical solution — a shift to a relative step based on the range width. The code is ready for use.

The article describes TradeMux as a bridge between a Python pipeline and the MetaTrader 5 terminal for the seamless transfer of trading decisions without duplicating logic. The article examines a four-layer production architecture and a complete Python execution service: connection setup, reading account and position data, signal generation (including CatBoost), pre-trade risk control, kill_switch, and a supervisor. Practical benefits include cross-broker normalization (RoboForex, IC Markets, Alpari, OANDA) and scaling from a single account to multi-account broadcasting without changing the trading logic.

This article implements a multi‑timeframe Granger causality monitor for MetaTrader 5. A Jupyter notebook tests predefined timeframe pairs, selects lags, applies multiple‑testing control, and exports a calibration table. An MQL5 Expert Advisor reloads the setup, recomputes the F‑test live on rolling windows, and marks pairs VALID/INVALID to gate its vote‑based BUY/SELL generator. The workflow shows alignment rules, live/offline parity, and a filtered vs unfiltered comparison on EURUSD.

This article builds a native MQL5 LOF engine without DLLs, ALGLIB or ONNX. Bars are mapped to five standardized features; k‑NN reachability and density produce a scale‑free LOF score, and the alarm level is a moving quantile of the reference cloud. The score gates only new entries in an EMA‑cross EA, with an indicator for charting and a script for independent checks on XAUUSD, EURUSD and GBPUSD.

The article examines the Archimedes Optimization Algorithm — a metaheuristic in which an agent is represented as a physical object with density, volume, and acceleration, and the search itself is reinterpreted as the tendency of bodies submerged in a fluid to reach equilibrium. The balance between exploration and exploitation is not dictated by an external schedule here, but rather stems from the physics of decaying turbulence. We will implement the algorithm in MQL5, test it on a standard test bench, and analyze where this approach works.

This article shows how to make EA close operations comply with broker-enforced FIFO (ACCOUNT FIFO CLOSE) on hedging accounts, so naive exits stop failing. It introduces CFifoPositionQueue to order an EA's own tickets by time and CFifoComplianceGuard to execute closes in two modes: strict reject or auto-cascade, with optional per-direction scope. The guard re-verifies eligibility before each step, normalizes volumes, and inspects real server retcodes. You get reusable code, tests, and clear limits.

The Hurst exponent indicator for MQL5 is based on R/S analysis using OLS regression in log-log space. The theoretical basis is the findings of Gatheral–Jaisson–Rosenbaum (2014), according to which volatility follows fractional Brownian motion with H ≈ 0.10. The indicator estimates H within a moving window, identifies anti-persistent (H < 0.3), neutral, and trending (H > 0.5) market regimes, colors the line, and triggers an alert upon a regime change, helping you choose a strategy type and manage risk.

Property-based tests exercise a sizing rule across thousands of generated scenarios instead of a few hand-picked cases. A deterministic MQL5 runner creates seeded symbol contracts, checks eight risk-volume properties, and writes text and CSV reports for reproducible evidence. A second script intentionally rounds volume the wrong way, replays the failing seed, and shrinks the counterexample to the half-step boundary, making the defect minimal and diagnosable.

Prop-firm accounts operate under a second rulebook that MetaTrader 5 does not model. This first part builds the observation layer: a per-pass frozen account snapshot read by every rule, a verdict record that reports without acting, explicit enable flags in the profile, and a session clock aligned to the firm's reset time. The result is a reproducible pass that prevents cascading effects and prepares the ground for safe decisions in Part 2.