MQL4 and MQL5 Programming Articles

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.

Build Constant Proportion Portfolio Insurance natively in MQL5. The article details the floor and cushion formulas, a rebalance engine that operates safely on both netting and hedging accounts, and a price‑only reference indicator that reuses the same math. A real Strategy Tester run on XAUUSD M5 demonstrates configuration, order sizing, and stability under a rebalance band.

Build and deploy a Neural Additive Model natively in MQL5: one small network per feature, additive output, and no post-hoc explainability. The article provides a PyTorch trainer, two‑output ONNX export (signal and contributions), an include wrapper using OnnxCreateFromBuffer/OnnxRun, and an EA that prints real per‑feature contributions each bar. It covers identical feature math for training/export, threshold measurement, Strategy Tester results, and edge cases such as NaNs and out‑of‑distribution inputs.

In this article, theory meets practice. We implement the key modules of the TMA framework — MPE and MPA. Here, data takes on meaning, and cross-attention becomes a tool for precise analysis of market dynamics. Minimal unnecessary operations, maximum efficiency — a step toward next-generation intelligent trading.

In this article, we'll show you how to create an MQL5 indicator that plots multiple moving averages on a single chart, reducing the amount of duplicate code. This uses iMA, indicator buffers, CopyBuffer, PlotIndexSetInteger/String, and a const array of structures that brings together periods, methods, and colors. The sizes of `indicatorbuffers` and `indicatorplots` are determined based on `Averange.Size()`. This approach simplifies maintenance and allows you to add or remove moving averages by modifying just one list.

This article presents a Trade Replay Tool for MetaTrader 5 implemented as an MQL5 indicator. It replays historical candles within an anchor-defined range and automatically reconstructs historical trades as paper positions with entry, SL, and TP. Users control start/end anchors and playback speed via UI buttons and hotkeys, enabling focused study of price evolution and trade lifecycle in a precise, event-driven environment.

In this article, we will examine the basic principles of using the technical indicators built into MetaTrader 5 and supported by this platform. Knowing and understanding these indicators can make implementing a solution much simpler, faster, and more efficient, since you won't have to worry about the necessary calculations. The platform handles them for us automatically.

This part extends the news filter with an ordered tier scale that adds a special tier above the broker's High, per‑event time windows, and one classifier shared by live calendar data and historical CSV. A rule table (with optional CSV overrides) promotes or demotes specific releases and can set custom windows. A diagnostic script prints your broker's calendar, event codes, and server time so your rules match what the terminal actually reports.