Articles on trading system automation in MQL5

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.

The Mantis framework transforms complex time series into informative tokens and serves as a reliable foundation for an intelligent trading agent capable of operating in real time.

This article extends a Flask backend to reliably receive, validate, and store closed trade data from MetaTrader 5 using SQLite and Flask‑SQLAlchemy. It implements required‑field checks, timestamp conversion, transaction‑safe persistence, and working retrieval endpoints for all trades and single records, plus a basic summary. The result is a complete data pipeline with local testing that records trades and exposes them through a structured API for further analysis.

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.

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.

The article provides a detailed explanation of the SCNN architecture and one way to implement it using MQL5. We will show how time series decomposition can be combined with neural network methods and attention mechanisms.

This class provides one point of contact for trade operations in MQL5. It rounds and clamps lot sizes, validates SL/TP against the broker's minimum distance, resolves a compatible filling policy, and applies bounded retries for transient retcodes. Calls return a structured CGatewayResult instead of raw retcodes, simplifying error handling and maintenance across strategies.

Multi‑timeframe EAs that initialize every indicator handle in OnInit() pay a fixed startup cost even when most handles are never used. CIndicatorCache applies lazy loading with composite‑key lookup, reference‑counted Acquire/Release, and a deterministic FlushAll() for cleanup. Handles are created on first request and reused across ticks, reducing startup latency, avoiding repeated heap allocation, and preventing terminal resource leaks through centralized ownership.

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.

Cross-cutting concerns like logging, timing, and threshold filtering should not live inside indicator classes. We show how to apply the decorator pattern in MQL5 with a shared IIndicator interface, an owning CBaseDecorator, and concrete CLoggingDecorator, CTimingDecorator, and CThresholdFilterDecorator layers. You can stack behaviors per EA, keep computation code closed to modification, and get deterministic cleanup by deleting only the outermost decorator.

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.

The article outlines the complete process of creating a multi-currency Expert Advisor using the Adwizard library for MetaTrader 5: from setting up the environment for creating optimization projects to obtaining the final multi-currency Expert Advisors, which combine multiple instances of a simple trading strategy. We will walk through setting up the necessary input parameters, conventions for convenient file names, and launching three instances of the final Expert Advisors on different trading accounts with different parameters.

We continue to work on implementing the CATCH framework, which combines the Fourier transform and frequency patching mechanisms, ensuring accurate detection of market anomalies. In this article, we complete the implementation of our own vision of the proposed approaches and test the new models on real historical data.

This article presents CSymbolMetaCache, an MQL5 layer that preloads contract specifications and trading-session schedules for monitored symbols at EA startup and then serves typed getters from memory. It explains which properties are safe to cache versus dynamic ones, including the semi-dynamic tick value on cross-currency pairs, and implements an in-memory IsMarketOpen() evaluator. A benchmark quantifies latency reduction across a set of twenty symbols.

An MQL5 implementation sends trade lifecycle events to a local HTTP service through WinINet with a reusable session and per-request handles. The trade callback only enqueues JSON and returns, while a 500 ms timer drains the queue and retries failed posts, preserving order. A three-stage log policy keeps the Experts tab clear during downtime and summarizes recovery.

In Part 2, we introduce a reusable CCovarianceMatrix class that computes and stores a covariance matrix from raw return series using MQL5's native Cov() method. We verify symmetry, print a labeled matrix grid, and call Eig() to obtain eigenvalues and eigenvectors. Readers see how symbols co-move and which factors drive variance, enabling clearer portfolio diagnostics and reuse in scripts or EAs.

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.

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.

The article presents the full integration of the 3D-bar module into a quantum-enhanced trading system for forecasting the movement of currency pairs. The system combines stationary four-dimensional features, an 8-qubit quantum encoder, and CatBoost gradient boosting with 52+ features. The system is implemented in Python using MetaTrader 5, Qiskit, CatBoost, and optional integration with the Llama 3.2 LLM for interpreting forecasts.

We invite you to learn about an algorithm for decomposing a time series into meaningful layers and using them to build a parsimonious model. We systematically present the architecture, the practical implementation in MQL5/OpenCL, and real-world tests using historical market data.

The series develops machine learning in 100% native MQL5 with no external dependencies. Part 1 delivers logistic regression from first principles: a CLogReg class with standardization, a stable sigmoid, SGD training, and model persistence, plus a script that builds ATR-normalized features, labels the next bar, and tests out-of-sample against a baseline. Readers get a compact include file and a clear template for leakage-free evaluation.

A lightweight persistence design lets EAs retain counters, flags, and timestamps between terminal restarts. Using only MQL5, CPersistentStore writes a human-readable key=value file in MQL5/Files and serves reads from a CHashMap write-through cache via a typed API. The article analyzes O(1)/O(n) operations, partial‑write risks, and lack of locking, compares with GlobalVariables/SQLite, and provides a demo that reloads state deterministically.

In this article we consider a custom MQL5 Wizard class that processes Money Management. Our custom class is labelled ‘CMoneyWaveletLSTM’, and is developed by combining the Wavelet Thresholding algorithm with an LSTM network. As has been the case throughout these series, the developed model is testable with MQL5 Wizard-Assembled Expert Advisors that can be tuned with different trailing stops and entry Signals classes. We maintain our entry Signal, as in past articles as the built-in 'Envelopes' class and the RSI class.

This article lays out 'CMoneyKDTreeESN' custom money management class usable with the MQL5 Wizard, that combines the KD-Tree algorithm and the Echo State Network. We use the KD-Tree on log returns and ATR to give us a risk score, while the ESN tracks recent flow to give us a bounded lot size multiplier. Our class is usable in a variety of Wizard assembled Expert Advisors as shown here with the Envelopes and RSI signals, with a broad objective of modulating exposure in high-volatility and tail-risk environments.

In this article, we continue our hands-on exploration of SSCNN — a next-generation architectural solution capable of processing fragmented time series. Instead of blind scaling — smart modularity, attention to detail, and targeted normalization. Step by step, we are creating computational blocks in the MQL5 environment and laying the foundation for reliable predictive analysis.

Manual population of MqlTradeRequest leaves cross-field rules unchecked, creating silent misconfigurations at execution time. A fluent COrderBuilder for MQL5 adds pointer-based method chaining, per-field validation, and directional SL/TP checks against broker stop‑level constraints. Its Send() method runs a four-stage gate—flag completeness, cross-field consistency, OrderCheck(), then OrderSend()—so configuration errors are caught early and order code stays clear and reusable.

We implement a Hierarchical Risk Parity allocator in MQL5 as a single class, validate each stage against an independent Python reference, and package it in a rebalancing Expert Advisor. The pipeline covers returns, covariance/correlation, clustering, quasi-diagonalization, and recursive bisection, and contrasts HRP with Markowitz on stressed data. You finish with a verified allocator and an EA ready for basket-level testing.

This article implements the MMAR Simulation Engine that turns fitted parameters (H, distribution, coefficients, sample volatility) into synthetic price paths. It builds multifractal trading time via a multiplicative cascade, synthesizes fractional Brownian motion with Davies–Harte or Cholesky, scales it to target volatility, and composes the process by time deformation. Readers get a reusable MQL5 class, method choices by path length, and validation steps for scenario testing and Monte Carlo use in the next part.

We continue our acquaintance with the Mamba4Cast framework. Today, we will delve into the practical implementation of the proposed approaches. Mamba4Cast was designed not for lengthy warm-up on every new time series, but for immediate deployment. Thanks to the concept of Zero-Shot Forecasting, the model can produce high-quality forecasts on real-world data without additional training or hyperparameter tuning.