History of MQL5.community development

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.