History of MQL5.community development

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.

Realized backtests reflect a single ordering of trades, so their maximum drawdown and prop‑firm breach risk are often understated. We resequence trade returns via a stationary bootstrap with a derived block length to estimate the drawdown distribution and a sequence risk ratio, compute path‑dependent breach probabilities for drawdown and daily‑loss limits, and replace WFE with a paired IS–OOS degradation statistic. The tools are demonstrated on a real EURUSD RSI strategy to guide sizing and risk limits.

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.

This article shows how to make EA close operations comply with broker-enforced FIFO (ACCOUNT FIFO CLOSE) on hedging accounts, so naive exits stop failing. It introduces CFifoPositionQueue to order an EA's own tickets by time and CFifoComplianceGuard to execute closes in two modes: strict reject or auto-cascade, with optional per-direction scope. The guard re-verifies eligibility before each step, normalizes volumes, and inspects real server retcodes. You get reusable code, tests, and clear limits.

A practical introduction to multiple regression with deterministic factors for financial time series. Topics covered include the formulation and matrix form of ordinary least squares (OLS), residual diagnostics, EDA/CDA, R-squared and adjusted R-squared, the F-test, RESET, the Breusch–Pagan test, VIF, and Cook’s distance. Techniques for working with regressors are demonstrated: dummy variables, nonlinear transformations, and interaction terms. Examples involving trends, days of the week, and news help select factors and build models suitable for forecasting.

The Hurst exponent indicator for MQL5 is based on R/S analysis using OLS regression in log-log space. The theoretical basis is the findings of Gatheral–Jaisson–Rosenbaum (2014), according to which volatility follows fractional Brownian motion with H ≈ 0.10. The indicator estimates H within a moving window, identifies anti-persistent (H < 0.3), neutral, and trending (H > 0.5) market regimes, colors the line, and triggers an alert upon a regime change, helping you choose a strategy type and manage risk.

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.

Property-based tests exercise a sizing rule across thousands of generated scenarios instead of a few hand-picked cases. A deterministic MQL5 runner creates seeded symbol contracts, checks eight risk-volume properties, and writes text and CSV reports for reproducible evidence. A second script intentionally rounds volume the wrong way, replays the failing seed, and shrinks the counterexample to the half-step boundary, making the defect minimal and diagnosable.

We implemented a neuron in MQL5 that was trained using stochastic gradient descent and compared its loss function to that of linear regression. Using code and graphs, we demonstrate how normalization, the choice of learning rate, and the structure of the problem affect convergence. This article provides guidance on debugging the training process, interpreting error behavior, and selecting the appropriate architecture or activation function.

In this article, we'll start exploring how to work with subwindows in MetaTrader 5 using MQL5. This is a broad topic with several practical aspects that may prove challenging, so we'll start with a brief introduction. And yet, dear reader, it’s important that you understand what we’re going to discuss here, because it could play an important role in your future.

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.