MQL4 and MQL5 Programming Articles

The article delivers AdaptivePSARTrail.mqh: a reusable header that lets an EA trail positions (its own or external, by symbol/magic) with an adaptive Parabolic SAR. The utility adapts the step from divergence, guards flips with a side check, and orders broker and improvement gates (stops/freeze, tick grid, break‑even, start and step thresholds). A calculation‑only build serves indicators, result codes aid logging, and a demo EA verifies the integration.

We introduce the permutation process for backtest validation: permute bars, rerun the complete development process, and compare the observed Sharpe to the null distribution it produces. Outputs include one-sided p-values, robust z, and quantile confidence intervals, plus a spurious performance curve that quantifies selection versus trial budget. A cache-isolation check prevents false passes. The EURUSD H1 case shows how to set a tolerance and assess whether performance is distinguishable from selection.

This article implements the Garman–Klass volatility estimator as an MQL5 indicator that uses OHLC data for higher statistical efficiency than close-to-close measures. A percentile-based squeeze/expansion detector produces breakout signals, and a companion Expert Advisor executes them via iCustom. Implementation details include series-aware indexing, recursive squeeze-state handling in indicator buffers, and a baseline-on-attach rule for the EA, giving a ready-to-use toolkit for volatility-regime trading.

The article explores the practical application of the L1 Trend Filter for noise reduction and the construction of structural features suitable for live trading. The full workflow is presented: H1 data for 29 instruments from MetaTrader 5, causal filtering, CatBoost with a horizon of three L1 bars, rigorous walk-forward testing, and VaR-based allocation of lots. The reader is provided with a reproducible code pipeline and a portfolio evaluation methodology.

We implement an adaptive trend stop indicator in MQL5 that reseeds on every flip, then accelerates toward price, with speed boosted by distance from VWAP and all distances scaled by ATR. The article covers the acceleration engine, the VWAP agreement filter that grades each flip, the anti-aliased stop line with its cloud, and persistent flip levels and arrows. The result highlights where the trend turned and whether VWAP agreed.

The article shows why a fixed posterior threshold, checked trade by trade, lacks error control and retires healthy strategies far too often on a real EURUSD case. It introduces WinRateSPRT (with Page's CUSUM for continuous use) and a permutation-tested profit factor trend to cover distinct failure modes. LiveStrategyMonitor integrates both and reports a normalized retirement-progress measure for operational decisions.

The implementation of the EV-MGRFlowNet framework demonstrates its key advantages: modularity, resilience to market fluctuations, and the ability to develop strategies independently. These features make the framework a powerful tool for analysis, forecasting, and the development of autonomous trading strategies.

This article presents a single MQL5 include file that adds runtime text-to-speech through Windows SAPI. The library starts a hidden PowerShell process through WinAPI and exposes one function, Speak(), with control of voice, speed, volume, XML tags, and a timeout. It also covers the limits: Windows only, DLL imports required, a blocking call, and refusal in indicators and the tester.

Oscillator thresholds are often treated as direct reversal instructions, causing traders to sell strong bullish markets or buy persistent bearish declines too early. We develop an analytical Expert Advisor that interprets Relative Strength Index events through confirmed swing structure. The model distinguishes continuation, reversal, pending, and expired conditions by combining closed-bar RSI crossings with higher highs, higher lows, lower highs, lower lows, and breaks of structure. It displays each validated interpretation without placing trades.

A simple grid trading strategy becomes a resilient system thanks to three components: an entry filter based on RSI divergence, a strict limit on the number of grid levels, and an independent risk manager that monitors equity on every tick and blocks trading when the daily limit or maximum drawdown is reached. The article examines the mathematics behind the classic martingale trap, the implementation of pivot point and RSI divergence detection in MQL5, the complete code for the CRiskManager class with protection against floating losses, and the architecture of the grid itself, with TP based on the weighted average entry price.

The article takes readers deep into the heart of the EV-MGRFlowNet framework, showing how its architecture is applied in a practical implementation for financial forecasting tasks. Step by step, we build a carefully designed combination of modules capable of detecting subtle temporal patterns and translating them into meaningful market signals.

This article examines the use of subwindows in MetaTrader 5 in detail and shows how the calculation direction in OnCalculate affects indicator buffers and the plotting of averages. Practical examples explain how indicators are linked using FIRST INDICATOR’S DATA and PREVIOUS INDICATOR’S DATA, the effect of cascading removal of dependent indicators, and how indicator_separate_window behaves. Readers will learn how to diagnose display issues and create indicators that work correctly in subwindows.

Implements the Lo–MacKinlay Variance Ratio Test in MQL5 to assess serial dependence via random‑walk variance scaling. Provides a reusable CVarianceRatio class with classical (homoskedasticity) and heteroskedasticity‑robust statistics, plus a script that scans multiple horizons and logs VR and Z‑values. Shows how to embed the test in an Expert Advisor as a statistical gate for mean‑reversion logic.

An Expert Advisor that takes signals from an external process must know which messages were delivered, which were acknowledged, and which orders reached the broker. This article builds that confirmation protocol on Redis Streams: MQL5 publishes market events, Python suggests trades, and MQL5 alone decides what is executed. Live tests on a demo account show duplicate detection, guard refusals, and dead-lettering; crash recovery is verified with unit tests.

An evidence-first workflow for Range Breakout in MQL5: a non-trading logger captures session/time windows and records 61 fields per breakout to CSV, with a strict split between entry features and hindsight labels. The MT5 AI Assistant processes the dataset in nine phases, helping validate data, quantify baselines, and locate stable, entry-usable thresholds for subsequent EA coding.

The article implements the Andean Condor Algorithm (ACA) for MQL5 — a compact optimizer with a multiscale intensification operator. A significant improvement in solution quality was observed with a small population: a single adjustment to the settings propels it into the top 45 — and behind this lies a distinctive feature of the algorithm worth knowing about. This resource provides ready-to-use code and practical guidelines for its application.

Rolling lookback windows implicitly fix a probability vector over past bars. We implement entropy pooling in MQL5 to keep all historical scenarios, reweight them by conditioning on today's volatility under an effective‑scenarios budget, and add views on volatility, tails and correlation. A walk‑forward test, a chart indicator and an ES‑based sizing EA demonstrate how to calibrate risk forecasts and position size to the current regime.

We describe ANANTA, a rules-based FX portfolio model that measures deviations in Interest Rate Swap differentials via moving averages and rebalances constant-dollar exposure across G10 currencies. Designed for intraday operation, it complements or substitutes carry logic without relying on trade volume. Performance is examined from January 2020 to October 2026 for five of the most liquid currencies. Code and free data pipelines are included.

The article discusses the adaptation of the EV-MGRFlowNet architecture — originally developed for processing event-based video data — to the field of financial time series. The approach presented here offers a new perspective on the market as a stream of micro-movements, where price, volume, and liquidity form a dynamic structure that lends itself to recurrent analysis without explicit supervision.

We build a Two‑Pole Momentum Oscillator in MQL5 that measures deviation from a 25‑period SMA, normalizes it by its mean and standard deviation, and smooths the result with two EMA‑style passes. The code defines inputs, buffers, warm‑up and recalculation logic, signal rules, and colorized plots, and derives dynamic targets from the 100‑period average range. It helps detect momentum shifts around the zero line and display price levels tied to recent volatility.

We implement a native Weibull maximum-likelihood fit in MQL5 and use it to model strategy wear-out from trade history. Failures are defined as the rolling profit factor dropping below a floor, producing lifetimes in trades. The article wires a survival-based governor into an EA and a companion indicator, so you can estimate conditional survival over the next N trades and apply explicit retirement thresholds with cooldown.

Most EAs trade more than one setup, yet the Strategy Tester judges them all by a single equity curve. CSetupLedger is a small MQL5 class that stores each trade's setup in its magic number and rebuilds the per-setup record from the account's deal history, so a restart never wipes it. It compares a Wilson interval of each setup's win rate with the breakeven rate implied by that setup's own average win and loss, and gives no verdict before 30 closed trades. Each setup ends up trusted, rejected or still undecided. In two backtests the per-setup totals match the tester exactly. Full source and two working EAs are attached.

This article uses practical examples to explain how to calculate and apply gradients of the weights and bias (intercept) in a linear neuron implemented in MQL5, and also discusses a stochastic variant of gradient descent. We analyze stopping criteria, iteration limits, and the effects of training on data subsets. The results are displayed in the MetaTrader 5 terminal, where a simple chart is also plotted. Readers are invited to modify the training set and analyze the model's behavior.

Introducing the TMA framework — an intelligent system capable of forecasting market dynamics with sufficient accuracy. In this article, we have integrated all the components into a single architecture and transformed it into a fully-fledged trading agent that analyzes the market and makes decisions in real time.

The article focuses on label-auditing for meta-labeling, not on a new entry signal. It presents a native MQL5 implementation of confident learning with out-of-fold scoring and calibrated pruning, plus an EA switch that trains the meta-filter on raw or cleaned datasets. A controlled Strategy Tester comparison helps you quantify noise and choose a pruning level that preserves generalization.

The article continues the series on AI Hedge Fund and addresses three limitations of v4: analysts' reputation scores are now stored persistently in SQLite, the EA has been removed from the critical execution path, and signals from the council of fifteen are distributed to multiple brokers via the TradeMux REST API. The logic behind the council and risk management has remained the same: Python retrieves data via the MetaTrader 5 SDK and executes orders directly. The result is resilience to restarts and scalability across multiple terminals.

This article introduces the foundations of connecting MetaTrader 5 to a cryptocurrency exchange using MQL5 and the MEXC Futures API. It covers terminal configuration, JSON response processing, WebRequest() execution and validation, and conversion of API data into native MQL5 types. Practical examples demonstrate retrieving and processing exchange data. These components establish the basis for a reusable MEXC Futures client, developed progressively throughout the series. The series focuses exclusively on exchange integration; how readers use the retrieved data remains outside its scope.