MQL5 Programming Articles

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 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.

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 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 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.

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.

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.

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 presents a regression-based approach to forecasting Renko bars using CatBoost: the model estimates the logarithmic return of the next bar and the uncertainty of the forecast. We will examine a cascade of residual models with OOF validation using TimeSeriesSplit, shrinkage, and overall early stopping, as well as conditional bias correction. On EURUSD D1, a reduction in OOF-MAE was achieved, along with approximately 65% directional accuracy. A working script for MetaTrader 5 is provided that generates a signal, position size, SL, and TP in brick units.

Trade authorization controls what a cooperating Expert Advisor is permitted to send, but not every account action passes through that code. A trade may originate from the desktop terminal, a mobile device, the web terminal, or another Expert Advisor. This installment develops an independent auditor that records live transaction stages through OnTradeTransaction(), reconciles available historical orders and deals within a configured recovery window, and writes an account-bound CSV journal. An on-chart panel summarizes the selected period and provides refresh, detail, and export controls without modifying trades.

We present a dynamic news‑sentiment filter for MQL5 Expert Advisors. It uses WebRequest to query a REST API, parses JSON sentiment and confidence, and gates entries and exits alongside Moving Average and RSI signals. You get an example EA, integration steps, timer‑based updates, and Strategy Tester workarounds. The outcome is fewer trades against dominant news flow and earlier risk reduction when sentiment flips.

The article presents the comprehensive architecture of the STE-FlowNet encoder, which combines stack memory, recurrent processing, and a correlation mechanism to extract hidden market dependencies. The article demonstrates how these modules are sequentially integrated into a single computational chain capable of performing comprehensive time-series analysis.

We adapt the Savitzky–Golay filter to a strictly causal, real-time form for MQL5 and derive the fixed coefficients for end-point evaluation. The article provides a full indicator and an Expert Advisor, explains the edge-evaluation noise sensitivity, and implements a multi-bar confirmation safeguard. Backtests on several symbols are shown without cherry-picking. You get ready-to-compile code, parameter guidance, and a reduced-lag alternative to moving averages.

The article constrains the optimal trading rule by a prop-firm stop-loss ceiling and corrects OU fitting by pooling within-trade pairs per AFML eq. 13.6. We apply the ceiling as a feasibility mask on a shared PT–SL mesh, ensuring the constrained set is a subset of the baseline. Practical guidance covers when the ceiling binds, the Sharpe cost, and how exposure and live budget state affect the bound.

The algorithms were rerun on the updated functions, and a method was proposed for quickly verifying their "honesty". The composite test combines five different landscapes and eliminates the possibility of gaining an advantage based on the geometry of individual problems, allowing for a quick assessment of the algorithm's actual search capability. A script is provided for preliminary validation of algorithms before they are applied to the optimization of trading strategies.