Articles on trading system automation in MQL5

Your Expert Advisor runs clean on your demo, then throws errors on a client's broker and quietly stops trading - and the code never changed. What changed is the broker's rulebook. This first article of the Broker Reality Check series builds a diagnostic EA that reads every relevant symbol trading condition - filling policy, stops and freeze levels, volume step, trade mode, swap and the triple-swap day - and flags the ones that silently break EAs, in plain language. It shows a green/amber/red panel, prints a report and dumps every Market Watch symbol to CSV, so you see why an OrderSend fails (10030, invalid stops, invalid volume) before it costs you a trade.

A revolutionary approach to machine learning in trading through quantum computing. The article demonstrates a practical implementation of an adaptive QRC system with continuous retraining for predicting market movements in real time.

In this article we present yet another custom MQL5 Signal Class that we are labelling ‘CSignalBTreeBayesian’. We are marrying the algorithm of a balanced tree with a neural network that is built on Bayesian principles to formulate yet another custom signal testable independently or with other signals thanks to the MQL5 Wizard.

We replace native MetaTrader chart objects with a canvas-based drawing engine that renders tools pixel-by-pixel on a full-chart bitmap layer. The article implements persistent object storage with per-tool style memory, precise hit testing, selection, whole-object dragging, and handle manipulation. It also adds new line tools, a reorganized category system with a one-click delete action, and a rubber-band preview for multi-click placement.

This article performs a numerical verification of MQL5 eigendecomposition for a covariance matrix using the spectral theorem A = V Λ Vᵀ. It reconstructs the matrix with Diag(), Transpose(), and MatMul(), computes the residual and its Frobenius norm, and shows that deviations remain at floating‑point precision, with results printed to the Experts journal.

The article presents CJsonConfigLoader and a typed SStrategyConfig that move EA inputs to a shared JSON file. A hand-written, quote-aware tokenizer parses a flat object without any DLLs. A hotkey triggers reload so all instances can pick up new lot size, SL/TP, and spread limits without reattaching the EA. On malformed input, the loader falls back to safe defaults and keeps the previous configuration.

The article presents a production-oriented Order Block engine for MQL5 packaged as an include class, it validates zones via displacement and market structure break, maintains mitigation state only on closed bars, and avoids heavy copies by passing data by reference. A diagnostic indicator plots zones, and an EA gates logic to new bars for stable performance and reproducible tests.

Building on the partition function analysis from Part 1, this article deepens the theoretical foundation before completing the analytical pipeline. We first give a full treatment of the Hurst exponent: what it measures, what it implies about market memory, and why it matters for the MMAR. This is followed by an intuitive exploration of multifractal spectra and what f(α) reveals about volatility heterogeneity. We then move to implementation: extracting the scaling function τ(q), estimating H via R/S analysis, and fitting the multifractal spectrum across four candidate distributions. By the end, we have the complete parameter set needed to construct the MMAR process in Part 3. Part 2 of an eight-part series.

This article starts the MMAR pipeline on EURUSD M5 data. We load market data via the MetaTrader5 Python API and run partition-function analysis with non-overlapping intervals to test for multifractal scaling. The result is an evidence-based decision on fractality, a prerequisite for building MMAR and for choosing whether to proceed beyond GARCH.

The article builds a reusable validation layer for Expert Advisors in MQL5. It implements lot-size rules and normalization, SL/TP and freeze-level guards, price digit normalization, margin sufficiency checks, unchanged-level filtering on modifications, account order-limit control, new-bar detection, symbol tradability checks, economic-calendar news windows, and session detectors. The result is cleaner code and fewer terminal errors in live trading.

AlternativeBarsViewer is a subwindow indicator that renders all ten alternative bar types as color‑coded candles using the same CBarConstructor hierarchy as BarBuilderEA, ensuring identical bars. It supports three data sources (real ticks, synthetic OHLC ticks, or the EA's CSV) and two render modes (TIME and INDEX) toggleable at runtime. Degenerate bars are highlighted and summarized on a compact panel, enabling live calibration without leaving the terminal.

We build a session-based reversal program in MQL5 using the Bread and Butter Judas Swing model. It derives a higher-timeframe daily bias, defines New York kill zones, maps each session's premium and discount from the live range, and requires a sweep before a market structure shift confirms entry. Readers get a ready approach to arm setups only during active sessions and execute in the bias direction with clear, testable rules.

This article presents CTradeJournal, a self-contained MQL5 class for live tracking of open positions at tick frequency. It maintains MAE, MFE, and initial risk in money, calculates the R-multiple when a position closes, and writes a complete CSV record. The text explains the design choices, provides the implementation, and shows simple EA integration so you can analyze entries, stop placement, and outcome distribution.

We extend the canvas drawing layer from the previous part with seven new categories of multi-anchor analytical drawing tools, covering three channel variants, three pitchfork variants, three Gann tools, and the six Fibonacci tools. We work through how each tool encodes its geometry on the canvas, how derived handles let users reshape compound shapes coherently, and how shared helpers handle ray clipping, scanline filling, and anti-aliased arc rendering. By the end, we will have a full set of analytical drawing tools that live on the same interactive canvas alongside the basic line tools from the previous part.

Batch normalization is the pre-processing of data before it is fed into a machine learning algorithm, like a neural network. This is always done while being mindful of the type of Activation to be used by the algorithm. We therefore explore the different approaches that one can take in reaping the benefits of this, with the help of a wizard assembled Expert Advisor.

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.

This article introduces ap.mqh, the ALGLIB port for MQL5, and demonstrates its use in multi‑asset workflows that require robust linear algebra. It covers why built-in indicators fall short, then implements polynomial regression, a rolling correlation matrix indicator, and an adaptive hedge ratio estimator using ridge regression with Cholesky. Practical code shows how to compute spread z‑scores and execute coordinated pairs trades entirely within MetaTrader 5.

For this article we switch to a custom MQL5 Wizard class that examines entry Signals. Our custom class is ‘CSignalDSUDBN’ this time around, and is coded by combining the Disjoint Set Union algorithm with a Deep Belief network. As has been the case throughout these series, our model is testable with MQL5 Wizard-Assembled Expert Advisors that can be tuned with different trailing stops and money management classes.

After a break in development and improvement of the service used for replay/simulator, we are resuming work on it. Now that we've abandoned the use of resources like terminal globals, we'll have to completely restructure some parts of it. Don't worry, this process will be explained in detail so that everyone can follow the development of our service.

This work presents an end-to-end pipeline: collect MetaTrader 5 data, engineer entropy/volatility/trend features, train a PyTorch classifier, and expose predictions through a Flask API. An MQL5 EA posts rolling prices each tick, receives probability and regime, and applies adaptive position sizing and stop distances. The result is a clear recipe for integrating ML inference with MetaTrader 5.

We build a tCISD program in MQL5 that pairs Quarterly Theory cycles anchored to New York time with a correlated-symbol SSMT divergence to time reversals. The article shows how to map cycles and quarters, detect the cross-symbol sweep disagreement, and derive the tCISD level whose break confirms the change in delivery. You will get a working entry logic that arms on divergence and executes on a confirmation close or a retest.

In this article, we shift from Python research to native MQL5 engineering. We build the first module of the MMAR library: a shared constants header, an SVD-based OLS regression class, a Generalized Hurst Exponent estimator, and the partition analysis engine that computes the partition function, extracts tau(q), estimates H via zero-crossing interpolation, and scores multifractality through three diagnostic tests. Tested on 500,000 bars of EURUSD M10, the engine correctly classifies the data as multifractal in under four seconds. Part 4 of an eight-part series. Part 5 fits the tau(q) curve to four candidate distributions via the Legendre transform.

We invite you to explore the modern Time-MoE framework, which has been adapted for time series forecasting tasks. In this article, we will implement the key components of the architecture step by step, providing explanations and practical examples along the way. This approach will allow you not only to understand how the model works, but also to apply those principles to real-world trading scenarios.

In this article we make the case for merging Divergence Mapping with a Temporal Fusion Proxy in a Trading Robot. Rather than depending on lagging price confirmations, the Divergence Mapping's thesis is that acting like a structural sensor can help identify hidden momentum shifts from price action and indicator anomalies. To establish how these anomalies are interpreted over time we use a Temporal Fusion Transformer proxy. This network incorporates historical context to weigh developing trends such that merging it with Divergence Mapping should set us up to spot shifts in accumulation and distribution before price breakouts.

We invite you to explore the practical implementation of a sparse mixture of experts block for time series in the OpenCL computing environment. This article provides a step-by-step explanation of how masked multi-window convolution works, as well as how gradient-based training is organized in the presence of multiple information streams.

This article focuses on EA architecture rather than signal design. Starting with a flawed Moving Average crossover EA, we add new‑bar detection to prevent duplicate entries, Magic Number and position awareness, ATR‑based risk levels, and data and trade result validation, along with basic safeguards. You obtain a practical base to build and test advanced systems.

This article shows how to implement a production Z-Score engine in MQL5 using an object-oriented include file, the library computes a rolling mean and population standard deviation, exposes a shift parameter for historical queries, and avoids redundant tick work by running on bar close. An Expert Advisor executes rule-based entries at positive/negative sigma thresholds and closes on mean reversion; a custom indicator provides visual verification.

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.

Nested if-else logic inside OnTick() creates implicit states that are hard to isolate, debug, and extend without regressions. A formal finite state machine in MQL5 uses an IState interface, a CStrategyContext mediator, and four concrete states to separate detection from behavior. A three-file include structure resolves circular dependencies and keeps declarations, definitions, and instantiation clean, making changes safer and debugging faster.

A new original population-based algorithm, ECEA, is presented. Inspired by the process of water freezing, it adapts ideas from the Crystal Energy Optimizer (CEO) algorithm, which uses graph-based search, for general optimization problems. The algorithm uses a dynamic elite group, three search strategies, and a periodic diversification mechanism.

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.