Programming Articles on MQL5.com

The article examines the architecture of an Expert Advisor based on a cellular automaton with 10,000 adaptive parameters and an independent binary predictor with a 10-bar prediction horizon. The article demonstrates a three-level online learning process, the evolution of strategies, and validation using a ring buffer and a confusion matrix. The input parameters are reduced to Magic Number, while trading settings are calculated from ATR and five genomes. The EURUSD H1 test yielded an approximate hit rate of 58%, compared to roughly 51% for the fixed MLP.

In this article, we'll show you how to create an MQL5 indicator that plots multiple moving averages on a single chart, reducing the amount of duplicate code. This uses iMA, indicator buffers, CopyBuffer, PlotIndexSetInteger/String, and a const array of structures that brings together periods, methods, and colors. The sizes of `indicatorbuffers` and `indicatorplots` are determined based on `Averange.Size()`. This approach simplifies maintenance and allows you to add or remove moving averages by modifying just one list.

This article presents a Trade Replay Tool for MetaTrader 5 implemented as an MQL5 indicator. It replays historical candles within an anchor-defined range and automatically reconstructs historical trades as paper positions with entry, SL, and TP. Users control start/end anchors and playback speed via UI buttons and hotkeys, enabling focused study of price evolution and trade lifecycle in a precise, event-driven environment.

In this article, I will try to explain as simply and clearly as possible one of the most controversial issues that arises when discussing neural networks: how to find the best possible point, or the minimum of the loss function. I'll show you the difference between linear regression and gradient descent. Both methods are fairly simple and serve as examples of how the path that seems obvious isn't always the best one.

In this article, we will examine the basic principles of using the technical indicators built into MetaTrader 5 and supported by this platform. Knowing and understanding these indicators can make implementing a solution much simpler, faster, and more efficient, since you won't have to worry about the necessary calculations. The platform handles them for us automatically.

This part extends the news filter with an ordered tier scale that adds a special tier above the broker's High, per‑event time windows, and one classifier shared by live calendar data and historical CSV. A rule table (with optional CSV overrides) promotes or demotes specific releases and can set custom windows. A diagnostic script prints your broker's calendar, event codes, and server time so your rules match what the terminal actually reports.

The article presents CPdfBuffer and CPdfWriter, a minimal MQL5 pair that derives all offsets from the output buffer instead of hand-maintaining numbers. CPdfWriter records object starts, writes a fixed-width cross-reference table and trailer, and ensures structural consistency. The result passes Part 2's validator, and a ready-to-use script converts arbitrary text to a paginated PDF.

The TMA framework offers a new perspective on market dynamics, enabling models to capture not only the state of the market but also the passage of time itself. Its ability to identify patterns in a continuous stream of data makes the analysis more in-depth and accurate than with traditional approaches. Recurrent adaptation turns this method into a practical tool for working with real-world quotes.

The article develops a volume-change strategy that transforms tick or real volume with Ln(volume+1), differences it bar to bar, and applies arctan to produce a bounded, scale‑independent signal. A threshold drives entries, with selectable gates for positive, negative, or bi‑directional signals and candle‑based confirmation. An MQL5 Expert Advisor implements the logic and supports fixed TP/SL, N‑candle exits, or both, enabling straightforward testing and deployment.

We take a closer look at the Color Harmony Algorithm (CHA) — a meta-heuristic optimization method based on Munsell's theory of color harmony. We explore the structure of the hue circle, harmony templates, the alternation between the concentration and dispersion phases, and the role of solution memory. From a theoretical framework to a working implementation in MQL5 and unbiased testing on a standard benchmark.

Here, we implement deletion using operator overloading. Without a doubt, many people will have to spend quite a bit of time studying this article to fully grasp everything that will be covered here. I want to remind you that what we're about to see is just a small and insignificant part of what we call programming.

In this article, I will try to show you, dear reader, that things are not always what they seem. We often tend to think that everything is arranged in a certain way, even though we may end up believing something that isn't necessarily true. Neural networks are undoubtedly one of the most interesting topics, whether viewed from the perspectives of mathematics, electronics, or software. Contrary to what many people believe or claim, neural networks are neither a problem nor a definitive solution. This is just one area of research where we should continue to delve deeper and explore what goes on behind the scenes.

The article describes the two-level MarkovMLPOscillator indicator: a three-state Markov chain uses historical data to construct a transition matrix and generate 15 probabilistic features for each bar, and an MLP is trained on these features to predict price direction at a specified forecast horizon. The article covers feature generation, the validation procedure using a holdout set, and parameter settings. The result is an interpretable oscillator with a color histogram, a smoothed signal, and a display of the current transition matrix.

This part finishes the geometry layer: arcs use a sagitta‑based angular step tied to the pixel tolerance, so segment counts are computed, not guessed. The path gains ArcTo, AddCircle, AddEllipse and AddRoundRect with per‑corner radii, enabling common UI shapes such as tabs, pills, badges, rings, gauges and donut wedges.

This article extends the A‑B‑C‑D constraint model with a measurement layer. We capture confirmation timing, breakout beyond Candle C, and post‑signal MFE/MAE; aggregate results by A‑B relationship; and write one CSV row per completed D window while showing concise CCanvas comments. The separation of detection, replay, visualization, measurement, and optional execution provides clean, analyzable data for further research.

This article models position exits as an American option and implements the Longstaff–Schwartz least‑squares Monte Carlo in native MQL5. The engine calibrates drift and volatility from recent returns, simulates forward price paths, and uses backward induction to compare immediate exercise to continuation. It integrates into an EA with hard time and price backstops and a live indicator, and is tested on four symbol/timeframe combinations with mixed, reproducible Strategy Tester results.

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.

Introducing the RAFT framework — a powerful tool for analyzing and forecasting financial time series. Its flexible and optimized architecture ensures accurate forecasts, stable performance, and faster data processing. RAFT reduces the risk of errors and makes it easier to create effective trading strategies.

We build a Python reader that attaches to the MetaTrader 5 shared memory segment, reproduces the Arrow columnar layout, and constructs ArrowArray/ArrowSchema via ctypes so pyarrow imports ticks without copying. The code honors the three-way schema contract and the seqlock generation counter to read consistent batches. A standalone benchmark verifies performance against a legacy per-field struct.unpack path with real numbers.

In the previous article, we looked at an example of implementing a class in MQL5 to provide basic support. Its purpose is specifically to allow SQL code to be stored in a separate script file. This way, we won't need to write the same SQL statements as string literals in the MQL5 source code. Although this solution is functional, there are some aspects of it that we can and should improve.

Although many people believe that we can easily embed SQL code into other code, this is usually not the case. The reason is that the SQL code is included in the executable as a string. Embedding SQL code as a string, while not a problem for small snippets, can ultimately cause us quite a few headaches.

We present an evolutionary algorithm without a selection operator: the best individual becomes the sole mating partner for the entire population, and classical mutation is replaced by multiplicative mutation with a self-adjusting step size. In this article, we examine three key ideas, implement the algorithm in MQL5 using the C_AO framework, and test it on the standard benchmark suite and in an anti-cheat test — where BOA comes very close to the cutoff for the top 45 but does not make the ranking.

This article implements a path-dependent volatility model in MQL5 that keeps the sign of returns. The engine uses two-timescale EWMA kernels (four accumulators) to form activity and trend features, with closed-form coefficients and a lightweight kernel search. We provide a calibration workflow and an indicator that plots the decomposition, then test out of sample against EWMA and GARCH on multiple symbols. Results show clear gains on equity indicators and minimal gains on FX.

This article measures a live-to-train behavioral-cloning loop for an RL trading system across 83 models. Outcome-weighted imitation shifted the policy toward Hold (48% → 82%), with 12 final models not trading at all. Live records were instead used to fit spread and slippage to broker fills, revealing execution costs ~85× higher than in simulation. We describe the loop, the failure mechanism, the calibration, and a protocol to apply.

The article discusses the fundamentals of Bayesian statistics in both discrete and continuous settings. We will progress from classical Bayes’ theorem and simple examples involving coin flips to conjugate distributions and dynamic Bayesian updating, which enables real-time analysis of market quotes. Using the beta-binomial model as an example, a simple change-point detection indicator has been implemented to help identify shifts in market regimes.

This article implements the Probability of Backtest Overfitting in MQL5 with Combinatorially Symmetric Cross-Validation. It shows how to build the bar-by-bar returns matrix of a parameter grid, aggregate it by blocks and rank the in-sample winner out-of-sample across every split of the history. You get a single probability that this winner ends up in the lower half of the grid out-of-sample, along with the MQL5 code to compute it on your own optimizations.

Session filters can run an hour off when a broker and a market change their clocks on different dates. This article presents an MQL5 include file, SessionClock.mqh, that converts London, New York, and Tokyo sessions to broker hours for a stated daylight saving time (DST) schedule (EU, US, or fixed) and winter offset. A second script estimates the schedule from server history. It uses calendar arithmetic, so it also works in the Strategy Tester.

In this article, we explore the inner workings of the RAFT framework — one of the most precise and elegant approaches to analyzing dynamic processes. Step by step, we adapt its idea of iterative refinement to financial time series, laying a solid foundation for a future model. Readers can look forward to an engaging exploration of the architecture, where every component has its own purpose and function.

The article describes an architecture in which the MQL5 Expert Advisor (EA) handles only data collection and execution, while the logic is moved to a Python server with three LangChain agents: a signal agent, a news agent, and a risk manager. Agents process the WebSocket request sequentially; if any agent rejects it, "hold" is returned. Decisions and actual PnL are stored in SQLite, forming memory and statistics. The reader will receive an interaction diagram, a command protocol, and a feedback approach.

Metaprompting is an approach in which the LLM itself optimizes trading instructions based on actual P&L and signal quality metrics. The article demonstrates a practical implementation in Python and MQL5: a prompt version registry, an execution agent, an evaluator for directional accuracy and profit factor, and a meta-LLM that generates improvements in a loop. The solution is integrated into the Expert Advisor without interrupting trading.

This article benchmarks one MLP forward pass implemented three ways in MQL5: native code, ALGLIB, and ONNX Runtime. We quantify numerical fidelity versus a float64 ground truth and execution latency (mean/percentiles), then outline practical criteria and a ready‑to‑use harness to help you choose and integrate the right engine in an EA.

Build a responsive session volume profile indicator in MQL5 from tick data with draggable session bounds and dual anchoring. The article details hash-based bucket aggregation for historical and live ticks, a timer-driven rendering loop with partial updates, automatic or custom bucket sizing, pixel-anchored proportional bars, POC highlighting, and multi‑instance safety—so you can implement, optimize, and run multiple profiles on one chart without UI lag.

This part introduces cubic Bézier curves and a quadratic verb, flattened on append by recursive midpoint splitting (De Casteljau). A conservative flatness test sums distances of control points to the chord and stops within a pixel-based tolerance, adapting detail to curvature. The design keeps downstream code intact while giving you efficient, scale-independent curve quality.

The RAFT framework offers a fundamentally different approach to forecasting market dynamics — not as a one-time snapshot, but as an iterative refinement of the state in real time. It takes both local and global changes into account, maintaining a high degree of accuracy even with complex price structures.

The article implements a deterministic MQL5 detector for head and shoulders and its inverse, validating structure from swing labels (HH, LH, HL, LL) rather than raw peak geometry. Seven ATR-aware checks enforce time order, pattern span, head clearance, shoulder symmetry, and height. A three-state lifecycle separates formation from neckline break, locks a setup, and prevents re-entry. Entries trigger only on a chart-timeframe close through the neckline.

BUZOA is a population-based metaheuristic algorithm in which each agent randomly selects one of three hunting tactics at each iteration: a narrow search around its personal best, a classic PSO step toward the flock leader, or full teleportation to a random point in the search space. The article examines the implementation of the algorithm in MQL5, identifies a coefficient sign error in the original formulation, and presents benchmark results on a standard test bench.

This article develops an A-B-C-D candlestick constraint model for MetaTrader 5. Candles A and B establish a completed D1 relationship, Candle C supplies a historical M1 setup, and closed M1 candles inside the developing Candle D either confirm or invalidate the sequence. We implement the design as a visual Expert Advisor with optional trading disabled by default, sequence-specific chart objects, restart-safe history reconstruction, spread and position gates, normalized trade parameters, and a reproducible testing plan.

The classic Market Profile from forty years ago is still replicated in dozens of indicators that differ only in the color of their bars. In this article, I examine three conceptual blind spots in the original theory — the monolithic Value Area in bimodal distributions, TPO’s blindness to the aggressor, and the lack of memory between sessions — and build an indicator that addresses each of them: bimodality detection with a dead zone, order flow via CopyTicksRange with absorption detection, and composite market memory using Naked POC and HVN/LVN. The complete source code is included.

This part turns stored account references into a financial monitoring engine for MetaTrader 5. It calculates daily drawdown from the confirmed session balance, overall drawdown from the initial challenge balance with a static equity floor, and profit‑target progress from realized balance. The EA derives usage, remaining allowance, and status for each rule, then aggregates them into a single guard state where risk conditions override target completion.

Build and deploy a conservative, offline‑trained policy entirely in MQL5. Logged trades become a fixed dataset for a two‑layer Q‑network trained with Conservative Q‑Learning, which pushes down Q‑values for poorly supported actions to limit extrapolation error. The EA loads the frozen weights and applies an out‑of‑distribution guard using saved feature statistics. This yields reproducible training and execution without live exploration.

The article implements two swing setups in MQL5: Anti, which enters on the first 3/10 oscillator pullback after a signal line zero confirmation with price confirmation, and Holy Grail, which enters on the first pullback to the 20 EMA in a strong rising ADX trend. A single EA runs two independent state machines with precise entry and exit rules, ATR-based stops, and state recovery. You get runnable code and traceable logging.

The article guides you through building a real‑time Hilbert Spectrum indicator in MQL5: EMD produces IMFs, a discrete FIR Hilbert transform yields analytical signals, and a CCanvas renderer draws a time–frequency–energy heatmap. Implementation details include extrema detection, SD stopping, phase unwrapping, Nyquist clipping, and bin accumulation. Use the panel to identify active cycle bands and regime shifts while keeping trade rules separate.

The article implements Online Portfolio Selection in MQL5: exact simplex projection, four allocators (EG, Universal Portfolio, PAMR, OLMAR), and a BCRP solver on a concave objective. We verify weight paths against the published Python reference and self-check the optimizer. A drift-aware transaction cost model measures turnover and net wealth on a multi-asset basket. Readers get a tested library and evidence on when regret-bounded methods lose to costs.

This article presents a native MQL5 Fractional Fourier indicator for measuring whether the dominant cycle is speeding up or slowing down. It searches for the rotation that maximizes spectral concentration in a detrended analytic window, using a unitary eigenvector FrFT and a golden‑section entropy search, and reconstructs a band‑limited cycle line. Results on known chirps and on real closes across symbols/timeframes show where the method is reliable, its bias, and its limited forward information in practice.

The article continues the work on implementing the STE-FlowNet framework, which combines multi-stream processing with recurrent structures for accurate analysis of complex data. The tests conducted confirmed its stability and flexibility in various scenarios. This architecture speeds up computations and enables deeper modeling of dependencies in time series. This approach opens up new possibilities for practical application in trading and analytics.