Articles on trading system automation in MQL5
Read articles on the trading systems with a wide variety of ideas at the core. Learn how to use statistical methods and patterns on candlestick charts, how to filter signals and where to use semaphore indicators.
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Cross Recurrence Quantification Analysis (CRQA) in MQL5: Building a Complete Analysis Library
This article extends the MQL5 RQA library to Cross-Recurrence Quantification Analysis (CRQA) for comparing two time series. We implement dual‑series embedding, cross‑recurrence matrix construction, adapted metrics (CRR, CDET, CLAM, CENTR, and others), and rolling‑window analysis, with optional GPU acceleration via OpenCL. A ready-to-use indicator compares two symbols in real time, supporting timestamp alignment and normalization for practical inter-market analysis.
Meta-Labeling the Classics (Part 2): Filtering and Sizing ADX Trades
The DI crossover often triggers in ranges where +DI and -DI oscillate without persistence. We build a two-layer hybrid: Optuna's TPE optimizes a regime gate over ADXR threshold, DI lookback, and minimum DI separation to maximize signal precision on a held-out window, then a Random Forest uses eleven ADX-derived features to accept or scale entries via afml.bet_sizing. The result filters ranging-market bursts and calibrates position size on EURUSD H1.
Neural Networks in Trading: Anomaly Detection in the Frequency Domain (Final Part)
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.
MQL5 Wizard Techniques you should know (Part 97): Using Convex Hull and a miniature GRU Network in a Custom Trailing Stop Class
For this article we look at a custom MQL5 Wizard class for Trailing Stops. Our implemented custom class ‘CTrailingConvexHullGRU’, is built from merging the Convex Hull algorithm with a GRU network. As always we seek to develop a model that is testable with MQL5 Wizard-Assembled Expert Advisors and can be tuned with various Money Management and entry Signals classes. Our testing is with the 'Envelopes' and the RSI classes for Signal.
MQL5 Wizard Techniques you should know (Part 91): Using Skip Lists and a Hopfield Network in a Custom Trailing Class
For our next Exploration on notions that are testable with the MQL5 Wizard we examine if Skip Lists and the Hopfield Network can give us a profit-guarding trailing strategy. Trailing Stop Management, as already argued, can be overlooked in most trading systems at the expense of Entry Signals or even Money Management. Trailing stops can make all the difference in certain situations such as trending markets, and thus we test this out with GBP USD.
Carry Trade Logic in MQL5: Building an EA That Factors Swap Rates Into Position Sizing and Holding Decisions
Most retail traders ignore overnight swap rates, but for long-term positions, these interest payments can make or break your strategy. This article shows you how to build a dynamic MQL5 module that retrieves real-time swap data and converts it into actual profit or loss in your account currency. You will learn how to program an Expert Advisor that automatically calculates if a trade is worth holding based on carry income and adjusts your position size to account for expected interest. It is a practical guide to turning a hidden cost into a mathematical advantage for your trading systems.
Market Simulation: Getting started with SQL in MQL5 (IV)
Many people tend to underestimate SQL, or even not use it at all, because they do not fully understand how it actually works. When running queries against an SQL database, we are not always looking for a universal answer; in some cases, we need a very specific and practical answer. If a database is created with a proper structure and data model, almost any type of information can be integrated into it.
Lazy-Loading Indicator Handles in MQL5: A Resource Manager Pattern for Multi-Timeframe EAs
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.
MQL5 Wizard Techniques you should know (Part 100): Sliding Window Median and Bidirectional LSTM for a Custom Trailing Stop
CTrailingSlidingMedianBiLSTM is a custom MQL5 Wizard trailing module that combines robust median/MAD outlier filtering with a BiLSTM context score in the range [-1, 1]. Four algorithm modes (standard, bands, RSI, adaptive) target noise, mean-reverting bursts and liquidity spikes, reducing premature stop adjustments. This module is intended for side-by-side evaluation with diverse entry signals and money management settings.
MetaTrader 5 Machine Learning Blueprint (Part 18): Sequential Bootstrap, Corrected — Clone, Class Erasure, and the Comparison Toolkit
The article diagnoses two defects that neutralize sequential bootstrap during cross‑validation: type erasure of SequentiallyBootstrappedBaggingClassifier and a fold‑level shape mismatch from cloning full samples info sets. It retains the classifier's identity, adds find seq bagging to re‑inject fold‑sliced t1 in CalibratorCV.fit, and resets state per split. A new bootstrap_comparison module reports OOF and OOB metrics and memory, letting you verify that sequential sampling is applied correctly and quantify its impact.
MQL5 Wizard Techniques you should know (Part 98): Using an Unscented Kalman Filter and a Capsule Network in a Custom Signal Class
This article presents 'CSignalUKFCapsNet', as a custom class coded in MQL5. This class is meant to be used with the MQL5 Wizard when assembling an Expert Advisor and when selected in the Wizard it defines the Expert Advisor's entry signals. In building this custom class, we brought together the algorithm Unscented Kalman Filter and the Capsule Neural Network. Our algorithm is showcased with four operation modes, and the coding of this as a custom class for the MQL5 Wizard, allows testing with various Trailing Stop methods and Money Management systems.
Implementing a Fluent Interface Builder Pattern for MQL5 Order Construction
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.
Implementing the Decorator Pattern in MQL5: Adding Logging, Timing, and Filtering to Any Indicator Non-Invasively
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.
Engineering Trading Discipline into Code (Part 8): Building a Setup Confirmation and Trade Authorization Layer in MQL5
This article introduces an MQL5 trade authorization framework built around CDisciplineLayer, CDisciplineGuardian, and CDisciplinePanel. The framework manages setup lifecycles, signal freshness, session restrictions, setup expiry, and global trading locks through a centralized authorization layer. It also provides automated enforcement of violations and a real-time dashboard, enabling consistent trade validation and monitoring before and after execution.
Beyond GARCH (Part V): Fitting the Multifractal Spectrum in MQL5
This article builds the Spectrum Fitter: from tau(q) we compute f(alpha) with a discrete Legendre transform, then fit Normal, Binomial, Poisson, and Gamma spectra under box constraints using BLEIC. The best model by SSE is selected, and its parameters (eg, alpha min, alpha max or alpha_0, gamma) become the cascade inputs for multifractal simulation.
MQL5 Trading Tools (Part 36): Adding Shape and Annotation Tools with In-Place Label Editing to the Canvas Drawing Layer
We add eight shape tools and nine annotation tools to the canvas and implement a full in-place label-editing system. The article walks through geometry, AA rendering, shared word-wrap and supersampled text helpers, and the caret-driven state machine for typing, navigation, and selection. This yields a complete, consistent annotation toolkit with editable labels that plugs into the prior interaction pipeline.
Building an Object-Oriented Session VWAP Engine in MQL5
This article shows how to implement a session vwap in MQL5 as a reusable include class with a strict daily reset at broker midnight. The engine computes VWAP and volume‑weighted deviation bands only on closed bars and anchors accumulation with MqlDateTime to avoid distortions from missing candles. A companion indicator plots the baseline and bands, while an Expert Advisor reads signals once per bar for consistent, CPU‑efficient execution and reliable testing.
Feature Engineering for ML (Part 10): Structural Break Tests in MQL5
We port AFML Chapter 17 structural break tests to MQL5 as a single include, CStructuralBreaks, delivering six bar-indexed features for EAs: CSW statistic and critical value, Chow-Type DFC, SADF with a rolling lookback (default 252), SM-Exp, and SM-Power. SADF uses O(L²) rolling windows for real-time viability. A companion StructuralBreaksViewer indicator plots all series with per‑series visibility and optional z‑score normalization. SB_EMPTY marks invalid values for safe integration.
Beyond GARCH (Part VI): Fractional Brownian Motion And The Multiplicative Cascade in MQL5
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.
MQL5 Wizard Techniques you should know (Part 94): Using Reservoir Sampling and Linear Regression in a Custom Trailing Stop Class
For this article we rotate to a custom MQL5 Wizard class implementation that explores Trailing Stops. Our custom class is ‘CTrailingReservoirLinReg’ that we derive by combining the Reservoir Sampling algorithm with a Linear Regression network. As has been the case throughout these series, this formulation is testable with MQL5 Wizard Assembled Expert Advisors that can be tuned with various entry signals and money management classes.
Persistent Key-Value Store in MQL5: Using Flat Files as a Lightweight Database for EA State
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.
MQL5 Wizard Techniques you should know (Part 99): Using a KD-Tree and an Echo State Network in a Custom Money Management 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.
Neural Networks in Trading: Time Series Forecasting Using Adaptive Modal Decomposition (Final Part)
The article discusses the adaptation and practical implementation of the ACEFormer framework using MQL5 in the context of algorithmic trading. It presents key architectural decisions, training features, and model testing results on real data.
MQL5 Wizard Techniques you should know (Part 96): Using Wavelet Thresholding and LSTM Network in a Custom Money Management Class
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.
Beyond GARCH (Part VII): Monte Carlo Volatility Forecasting in MQL5
We implement the CMonteCarlo module that turns the fitted MMAR parameters into a volatility forecast via Monte Carlo. It runs N independent simulations over a chosen horizon and reports mean, median, standard deviation, and a percentile-based 95% confidence interval, with access to per-run values if needed. Adaptive cascade depth selects the minimal k such that b^k covers the horizon, keeping the run fast and consistent.
Feature Engineering for ML (Part 8): Entropy Features in MQL5
An MQL5 port of four entropy estimators — Shannon, Plug-In, Lempel-Ziv, and Kontoyiannis — operating on the intrabar tick-rule sequence. CopyTicksRange() limits data to the broker's cached tick window, so features apply to recent bars only. The implementation encodes bid-direction ticks from MqlTick, replaces NumPy-dependent steps with array-based methods, and ships CEntropyFeatures.mqh and EntropyViewer.mq5 for EA and indicator use.
Neural Networks in Trading: Generalizing Time Series Without Data-Specific Dependence (Mamba4Cast)
In this article, we introduce the Mamba4Cast framework and take a closer look at one of its key components: timestamp-based positional encoding. The article shows shows how time embedding is formed taking into account the calendar structure of the data.
Building a Broker-Agnostic Symbol Resolution Layer in MQL5
We implement a symbol resolution framework that abstracts broker naming differences in MetaTrader 5. Using a persistent mapping store, layered resolution with validation, a hash-indexed registry, and a cache, it returns selectable symbols with live market data and logs unresolved cases. Practically, you can deploy the same EA across brokers and keep symbol access consistent at low runtime cost.