History of MQL5.community development

Trading is characterized by high demands on risk management discipline. The article presents an analysis of the main reasons for traders' failures and proposes a technical solution in the form of the CEnhancedRiskManager class for the MQL5 platform. It includes practical testing on an aggressive grid EA.

This article lays out 'CMoneyKDTreeESN' custom money management class usable with the MQL5 Wizard, that combines the KD-Tree algorithm and the Echo State Network. We use the KD-Tree on log returns and ATR to give us a risk score, while the ESN tracks recent flow to give us a bounded lot size multiplier. Our class is usable in a variety of Wizard assembled Expert Advisors as shown here with the Envelopes and RSI signals, with a broad objective of modulating exposure in high-volatility and tail-risk environments.

This article builds a self-contained MQL5 Expert Advisor that completes the Wyckoff cycle: it detects accumulation/distribution with a finite state machine, enters at the last point of support/supply, and calculates exit point-and-figure counts under Wyckoff's Cause and Effect. We detail the box size from range ATR, a 1-box reversal, target validation, and a 2R fallback. Readers get runnable code without external dependencies.

This article demonstrates how to build a reusable prop‑firm evaluation module for MQL5 Expert Advisors and export results to an HTML dashboard. The module monitors balance and equity during backtests, simulates single or rolling challenges, checks profit target, daily and overall drawdown, and minimum trading days, then outputs both a terminal summary and a browser‑readable report.

Multi‑timeframe EAs that initialize every indicator handle in OnInit() pay a fixed startup cost even when most handles are never used. CIndicatorCache applies lazy loading with composite‑key lookup, reference‑counted Acquire/Release, and a deterministic FlushAll() for cleanup. Handles are created on first request and reused across ticks, reducing startup latency, avoiding repeated heap allocation, and preventing terminal resource leaks through centralized ownership.

The article diagnoses two defects that neutralize sequential bootstrap during cross‑validation: type erasure of SequentiallyBootstrappedBaggingClassifier and a fold‑level shape mismatch from cloning full samples info sets. It retains the classifier's identity, adds find seq bagging to re‑inject fold‑sliced t1 in CalibratorCV.fit, and resets state per split. A new bootstrap_comparison module reports OOF and OOB metrics and memory, letting you verify that sequential sampling is applied correctly and quantify its impact.

Building on Part 2, the implementation introduces restart-aware breakeven and trailing-stop systems for MetaTrader 5. The EA persists the state, such as breakeven activation, last trailing price, and virtual SL in SQLite, then restores them on startup. This preserves dynamic protection flow and prevents lost progress after terminal interruptions.

This article shows how to implement a session vwap in MQL5 as a reusable include class with a strict daily reset at broker midnight. The engine computes VWAP and volume‑weighted deviation bands only on closed bars and anchors accumulation with MqlDateTime to avoid distortions from missing candles. A companion indicator plots the baseline and bands, while an Expert Advisor reads signals once per bar for consistent, CPU‑efficient execution and reliable testing.

The article provides production-ready entropy estimators (Shannon, plug-in, Lempel–Ziv, Kontoyiannis) operating on tick-rule–encoded sequences. It resolves three correctness and performance issues in the original code, verifies outputs against chapter references, and extends encoding with quantile and sigma options. Users gain reproducible results and markedly improved computation speed for large bar sets.

The article discusses the application of Grey models to forecasting financial time series. We will consider the operating principles of Grey models and the specifics of their application to financial series. We will also discuss the advantages and limitations of using these models in trading.

In this section, we will look at the implementation of the key interfaces of the library of Gaussian processes in MQL5: IKernel, ILikelihood, and IInference. We will also demonstrate its operation on synthetic data and implement indicators for classification and regression, demonstrating its operation in online mode - with retraining of the model on each new bar.

The article implements rolling OLS regression channels in MQL5 and computes confidence and prediction bands with Student's t critical values instead of a fixed standard-deviation multiplier. It explains the leverage-driven widening at window edges, contrasts the result with Bollinger and Donchian channels, and reviews OLS assumptions on price data. A five-line rendering is documented to ensure reliable display in MetaTrader 5.

We present a complete workflow for adaptive filtering in MQL5 using the CNlEq Levenberg–Marquardt–like solver. The EA fits a VAMAC model—two EWMAs with an ATR‑based scaling—by supplying residuals and a Jacobian through CNlEq's reverse‑communication loop, with optional numerical or analytical derivatives. Code, setup instructions, and GBPUSD H1 tests show how to replace static thresholds with on‑bar re‑estimation.

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.

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.