A single profitable MT5 backtest can hide whether an EA would survive prop-style constraints. This article adds a reusable MQL5 evaluation module that simulates challenge rules during Strategy Tester runs: profit target, daily loss, overall drawdown, minimum trading days, and optional time limits.

It normalizes tester balance/equity to a configurable “virtual” challenge account, then evaluates either one attempt or rolling attempts that restart daily/weekly/monthly to expose start-date sensitivity. Each attempt tracks state (dates, trading days, daily reference, status, failure/incomplete reason) and confirms breaches before marking a pass.

Results are surfaced as an Experts journal summary plus an HTML dashboard with stats, attempt history, and optional charts, helping traders and developers diagnose whether failures come from daily limits, overall draw...

👉 Read | Calendar | @mql5dev

Part 2 extends the Wyckoff EA from entry logic to exits using Wyckoff’s Law of Cause and Effect with a point-and-figure horizontal count.

A self-contained MQL5 Expert Advisor is built with a finite state machine: range detection, spring→SOS→LPS for longs, and upthrust→SOW→LPSY for shorts. Sequencing is enforced to reduce false positives; invalidation resets to idle.

Take profit is computed from P&F: count line at LPS/LPSY, columns counted at that level, box size derived from range ATR (~0.25 ATR), and a 1-box reversal. Targets outside bounds fall back to 2R. Only Trade/Trade.mqh is required.

👉 Read | Calendar | @mql5dev

This article extends MT5 money management beyond linear lot scaling by adding a regime-aware circuit breaker for tail-risk, high-volatility periods. The core idea is to size positions based on how close current conditions are to historical liquidity failures, not just per-trade risk percentages.

A KD-Tree maps market states into a 2D space (log returns, ATR) and uses fast nearest-neighbor queries to detect proximity to “crash” clusters. An Echo State Network adds low-latency temporal context via reservoir dynamics, producing a bounded lot-size multiplier that dampens exposure when return sequences look unstable.

Implementation centers on an MQL5 CExpertMoney-derived class, using ATR for volatility normalization and MACD as a momentum vector to modulate behavior around detected risk zones, enabling modular switching between offensive and defensive siz...

👉 Read | AppStore | @mql5dev

Trading losses are usually tied to risk drift: daily limits get exceeded after a losing trade, drawdowns accumulate unchecked, and predefined rules are ignored at execution time.

A MetaTrader 5 risk layer can remove that failure mode. An include-based EnhancedRiskManager.mqh centers on CEnhancedRiskManager, enforcing per-trade risk, daily drawdown, and total drawdown with conservative/moderate/aggressive presets, plus adaptive balance/equity drawdown measurement and state persistence via terminal globals.

Stress test: an aggressive martingale grid (1.5x, 200-point steps, up to 8 positions, no SL). Baseline lifespan averaged 3.2 days with 100% drawdown. With limits (5% daily, 10% total, 2% per trade), the 9-year run stayed profitable: max daily DD 4.8%, max total DD 9.7%, total return 127%, using trade blocking, forced close near limits, and profit trailing.

👉 Read | Docs | @mql5dev

MT5 restarts clear an EA’s in-memory state, resetting risk counters, regime flags, optimization schedules, and runtime toggles. Production systems need deterministic continuity, not a fresh start after every terminal interruption.

The article builds a native MQL5 persistence layer using a readable key=value flat file in MQL5/Files/, avoiding SQLite, terminal GlobalVariables, and external dependencies. Flat files stay inspectable and per-EA, while GlobalVariables are shared, double-only, and opaque.

The design is modular: a typed value wrapper with safe type inference, a strict line parser (whitespace, comments, key validation), a serializer for consistent formatting, and a CHashMap-backed cache for O(1) reads. Writes rewrite the small file (O(n)), trading simplicity for reliability and easy integration into existing EAs.

👉 Read | Forum | @mql5dev

Four intrabar entropy estimators—Shannon, Plug-In (w-grams), Lempel–Ziv complexity, and Kontoyiannis entropy rate—are ported from a NumPy/Numba Python reference to practical MQL5 code for MetaTrader 5.

The design works around MT5 constraints: intrabar ticks come only from the broker-limited CopyTicksRange() cache, so older bars are marked with a sentinel (ENT_EMPTY). Bars also require a minimum tick count to avoid meaningless estimates.

Tick directions are encoded from bid changes into a compact ternary uchar stream {0,1,2}. Sequential estimators avoid missing Python primitives by using a base-3 hash for overlapping w-gram counts and a bounded look-back window to cap Kontoyiannis’ O(n²) search.

Integration targets live use: a single Calculate() call per new bar updates feature arrays, with explicit sentinel checks to prevent trading on missing tick history....

👉 Read | Signals | @mql5dev

Multi-symbol risk often gets understated when per-trade sizing is evaluated in isolation. During macro releases, EURUSD, GBPUSD, and XAUUSD can move in the same direction on the same driver, turning three 1% allocations into a fast 3% portfolio hit.

Single-instrument volatility is useful but incomplete for portfolios. The missing component is co-movement, captured by the covariance matrix. Portfolio variance is wᵀΣw, not the sum of individual variances; cross terms dominate when correlations rise.

A practical MQL5 implementation centers on PortfolioRiskAnalyzer.mq5: fetch multi-symbol closes, compute log returns, assemble a returns matrix, then compute Σ via matrix.Cov() and risk via MatMul(). Recent MT5 builds back these operations with OpenBLAS for scalable linear algebra.

👉 Read | VPS | @mql5dev

Many EAs generate signals but lack an authorization layer between detection and execution. That gap causes premature entries, late trades after expiry, and interference from other EAs or manual orders.

A discipline model can formalize setup lifecycle states: NO_SETUP, SETUP_FORMING, SETUP_CONFIRMED, SETUP_ACTIVE, SETUP_EXPIRED. Execution becomes state-driven, not pattern-driven.

Trade authorization rules can be centralized in a CanTrade() gate: confirmation, expiry window, freshness, session filter, and a global lock.

Enforcement is handled separately via CDisciplineGuardian: alert, auto-close, or auto-close plus terminal-wide lock. Visibility is provided by CDisciplinePanel with on-chart status for state, permission, expiry, freshness, session, and guardian mode.

Integration into an EA routes every order through the layer before any execution call.

👉 Read | Docs | @mql5dev

A free script is available for closing all open positions in compliance with FIFO rules. It can be attached to a chart via drag-and-drop and will process open trades regardless of whether they are in profit or loss.

Autotrading must be enabled before execution. After activation, the routine issues close requests in FIFO order until no eligible positions remain.

Operational use should account for broker constraints, partial fills, and slippage, and should be validated on a demo environment before running on a live account.

👉 Read | Calendar | @mql5dev

Transformers bottleneck on market history because attention scales as O(N²), making multi-thousand-bar context too slow for latency-sensitive trading. Mamba replaces attention with Selective State Space Models, delivering O(N) sequence processing and effectively unbounded context.

Its core gain is selective memory: SSM dynamics adapt to the input so the model reinforces regime shifts (volatility spikes, news-like shocks) while damping routine noise. The block design combines local convolution for short-term structure, a selective SSM for long memory, gating for controlled information flow, plus stability-focused initialization (HiPPO) and training with AdamW. Patching further reduces compute by turning bar groups into meaningful tokens.

A MetaTrader 5 implementation (ModernAI_Expert.mq5) shows practical integration: normalized price/volume inputs,...

👉 Read | Quotes | @mql5dev

Quasimodo reversals are hard to trade manually because the “shape” is subjective and entries often feel late. This article turns the QM idea into a rule-based MT5 EA that detects the pattern, confirms it with a break of structure, then enters only after a retrace to the QM (left-shoulder) level.

Detection is built on confirmed swing pivots: a pivot is accepted only after N bars close on both sides, then compressed into an alternating zig-zag by merging consecutive same-type pivots into the most extreme point. A prior-trend filter validates there was a real trend before the reversal.

Execution is fully structured: entry at the QM line, invalidation beyond the head, target at the broken leg level, with optional close-back-through confirmation, reward/risk filtering, risk-based lot sizing, trailing/TP modes, trade-record syncing after restarts, and vi...

👉 Read | Signals | @mql5dev

Parts 2–6 generate eleven one‑minute environment metrics. Acting on all of them in real time is impractical for sizing and risk controls, so Part 7 reduces the vector to a single regime label plus quality scores.

RegimeClassifier() maps the metrics into six regimes: Normal, Stressed, Noisy, Informed, Trending, Mean‑Reverting. It returns confidence in [0,1] and a composite directional score in [-1,+1]. Boundaries are percentile thresholds calibrated on 514 NQ M1 sessions (May 2024–May 2026).

Classification is rule-based with priority ordering: Stressed, Informed, Trending, Noisy, Mean‑Reverting, Normal. Reliability is the geometric mean of MFDFA fit confidence and flow_confidence; roll confidence is excluded. Deliverables include MARKET_REGIME, RegimeAnalysis, RegimeClassifier(), and PopulateRegimeAnalysis() calling Parts 2–6 sequentially.

👉 Read | Docs | @mql5dev

In June, MetaQuotes participated as a Gold Sponsor at iFX EXPO International 2026, in Limassol, one of the major international events.

At the exhibition, we recorded a series of short interviews with representatives of brokerage companies.

They shared their experience using MetaQuotes solutions, discussed the evolution of MetaTrader 5 and new products in the ecosystem, and highlighted the opportunities these technologies create for modern brokerage businesses.

• Ultency — a liquidity aggregation and order matching engine integrated with MetaTrader 5
• metatrader.com — a new portal offering financial news, market analysis, trading ideas, educational content, and algorithmic trading tools
• MetaTrader Access Servers — a global network of access servers ensuring fast and stable connections to the trading platform for traders worldwide
• Integrated payments, enabling traders to fund their accounts directly from MetaTrader

Discuss the video:
👉 MQL5.community for traders
👉 MetaQuotes official YouTube channel

John F. Ehlers treats price as a signal with frequency components, not a chart pattern. The focus is a practical MQL5 DSP library that ports published coefficients and formulas, with indicators and an EA sharing identical code paths.

Core point: smoothing is filtering. The SMA is a weak low-pass filter: material lag plus a poor frequency response that leaks noise. A 2-pole IIR design reduces noise with less delay.

Library design uses stateful recursive filters with explicit history, warm-up handling, and a single include file as the source of truth. No iCustom dependency for the EA.

Implemented tools: Super Smoother (2-pole low-pass), Roofing Filter (2-pole high-pass cascaded into Super Smoother to isolate a tradeable band), and Even Better Sinewave, which flags non-cycling regimes by sustained railing near ±1.

👉 Read | CodeBase | @mql5dev

Part 7 finalizes the MMAR library’s generative stack by adding CMonteCarlo, a thin orchestration layer that turns a single simulated path into a volatility forecast distribution.

The class initializes with fitted multifractal parameters, runs N independent MMAR simulations over a chosen horizon, and aggregates per-path volatilities into mean, median, standard deviation, and a percentile-based 95% confidence interval. Raw per-run outputs remain accessible for custom diagnostics.

Key implementation details include adaptive cascade depth (choosing the smallest b^k that covers the horizon), clean per-trial engine instantiation, and resilient failure handling that skips rare FBM factorization issues without aborting the run.

A full EURUSD M10 pipeline completes in ~1.3s for 100 simulations, making periodic EA recalibration practical without running o...

👉 Read | Signals | @mql5dev

Algorithmic trading keeps running into the same constraint: higher model complexity improves backtests while increasing overfitting risk, and non-stationary markets invalidate static patterns. Full retraining also amplifies catastrophic forgetting, forcing a tradeoff between stability and adaptability.

Quantum Reservoir Computing (QRC) addresses this by keeping the reservoir fixed and training only the output layer. A four-qubit circuit maps features into a 16-state space via qubit rotations, relying on quantum nonlinearity and feature coupling.

The implementation uses Monte Carlo approximation, a 1000-sample experience buffer with decay, online plus batch updates, and adaptive learning rates. Tests on EURUSD M15 (2017–2025) reported +USD 505 on 0.01 lots from USD 1,000, Sharpe 1.22, win rate 82%, with strong sensitivity to data artifacts and news...

👉 Read | CodeBase | @mql5dev

The article shows why moving MT5/MQL projects from “code + ZIP attachment” to MQL Algo Forge becomes necessary once libraries evolve across many articles. Git-backed history removes the pain of repackaging archives, comparing versions, and supporting users with unclear change sets.

A practical workflow emerges: start projects in Shared Projects, choose a project type for executables or use empty projects for multi-file or mixed outputs, then commit frequently. For older libraries, import versions sequentially and create Releases (stable/beta) so readers can target exact published states.

Key pitfall: Algo Forge reliably tracks diffs only for UTF-8 files; MetaEditor may save as Unicode, causing files to be treated as binary and hiding history. Documentation matters too: meaningful commit messages and a clear README.md turn a repository from a file dump into m...

👉 Read | Freelance | @mql5dev

An updated MQL5 implementation of the Shved Supply and Demand indicator is available with multi-timeframe support and a history review mode.

Zones are calculated using fractals and ATR, then rendered on the chart for a selectable timeframe independent of the current chart. Zone states include weak, untested, verified, proven, and broken (broken state not used for weak zones). These classifications reflect whether price has retested and failed to break a level multiple times.

History mode can be enabled by setting the historyMode parameter to true, then double-clicking a point on the chart to display support/resistance zones for that moment.

Recent revisions address compile warnings, low-bar edge cases, alignment with the MT4 version, corrected labels in history mode, and an optional mobile notification when price enters a zone.

👉 Read | Signals | @mql5dev

Duelist Algorithm (DA) is a population-based optimizer proposed in 2015 by Biyanto’s group to reduce reliance on blind mutation and crossover. The model separates roles: losers copy parts of winners, winners apply controlled innovation, and top champions spawn new candidates while skipping duels.

An MQL5 implementation can be structured as C_AO_DA_duelist with parameters for population size, luckCoefficient, learningProbability, innovationProbability, and championsCount. Core steps: initialize population on a discrete grid, select champions, run randomized duels with a luck term, apply learning to losers, mutate winners, retrain from champions, sort by fitness, and cull to fixed size.

Benchmarks on Hilly, Forest, and Megacity functions (10,000 runs) produced an overall score of 4.34477 (48.28%). DA ranked 42nd among tested methods, with low disper...

👉 Read | NeuroBook | @mql5dev

MetaTrader 5 lacks a native volume profile, but a session-based implementation can be built entirely in MQL5 using CopyTicksRange() plus standard chart objects. The indicator pulls ticks for a defined London/New York/custom window, bins prices into a histogram, then derives POC and the 70% Value Area (VAH/VAL) by expanding outward from the highest-volume bin.

A key implementation detail for FX/CFDs: real volume fields are often zero, so tick filtering must rely on non-zero bid/ask, with each tick weighted as 1 to form a tick-density profile.

The design is modular: session rollover detection, tick collection, histogram building, POC/Value Area calculation, and renderer. Complexity stays linear in ticks, with far fewer bins than ticks in practice.

👉 Read | Docs | @mql5dev