History of MQL5.community development

Despite what was shown in the previous article, all of this may seem simple at first. In reality, several problems remain, along with many tasks that still need to be completed. You, dear reader, may imagine that everything is easy and straightforward. Out of inexperience, you may simply accept whatever is presented to you. And that is a mistake you should try to avoid. Even worse is trying to use something without truly understanding what exactly you are using. Beginners often pass through a copy-and-paste stage. If you do not want to remain stuck at that stage forever, you should learn how to use certain tools. One of the tools most often used by programmers is documentation. The second is testing, supported by log files. Here we will see how to do this.

In today's article, we will explore random access to file contents for the first time. This applies to both writing and reading information stored in a file. However, since the topic is too broad to cover in a single article, we will limit ourselves here to an introduction to random access.

Kronos is a pretrained transformer that models OHLCV bars the way a language model predicts words. We reimplement its tokenizer/encoder and transformer block in native MQL5, export weights to flat .bin files, and remove Python from runtime entirely. Part 1 delivers preprocessing and BSQ tokenization plus a bit-for-bit verification harness against PyTorch, so you can run the encoder inside MetaTrader 5 with confidence.

A rolling-window Approximate Entropy oscillator for MQL5, built without external dependencies. Covers the full mathematics of template matching, Chebyshev distance, and the Phi-function derivation before presenting a reusable CApEnCalculator class and a color-zoned subwindow indicator. Includes a synthetic-data verification script and an honest discussion of bias, parameter sensitivity, and computational cost.

This article shows how to build a synthetic custom symbol in MQL5 by averaging OHLC data from multiple instruments into a single derived price series. It covers symbol collection and validation, custom symbol creation and configuration, timestamp alignment, historical reconstruction, and lightweight live updates. The result is a reusable method for creating synthetic instruments suitable for correlation analysis, index-style modeling, indicator development, and strategy testing.

Does better return conditioning buy strategy performance? We hold bar count fixed across time, tick, tick-imbalance, and tick-runs on 60.5 million EURUSD ticks, then meta-label RSI, Bollinger, and ADX/DI entries and score with purged cross-validation. No family delivers a consistent edge; efficacy varies narrowly and the best case fails a permutation test. Readers learn how to control overlap, leakage, and multiple testing in bar studies.

Here we will start bringing together different components or applications that were previously completely isolated from each other. Chart Trade, the mouse indicator, and the Expert Advisor had already been linked to one another, but there was still no way to directly display on the chart the positions open on the trading server, which are often managed using a cross-order system. From this point on, this becomes possible, opening the way for various ideas and future implementations. Although we are only beginning to put these components into operation, we already have a direction for further development.

In today’s article, we will look at several ways to work with the FileSave and FileLoad library functions. Although many people consider them of limited use because of certain limitations or difficulties they create in specific scenarios, properly understanding how these two functions work can save us a great deal of effort at certain points. They are also an excellent way to work with log files.

The article presents a complete implementation of TimeGPT, a specialized Transformer-based architecture for forecasting financial time series on the MetaTrader 5 platform. Adaptation of the attention mechanism to financial data, selective tokenization of price changes, hardware-aware optimizations, and advanced learning techniques are discussed. Included are practical testing results showing 87% forecast accuracy over a 24-bar horizon with a training time of 15 minutes on the CPU. We also present a ready-made trading EA with automatic retraining.

This article shows how to build an MQL5 Expert Advisor around the UT Bot Alerts indicator. The EA reads custom indicator signals via iCustom() and CopyBuffer(), evaluates entries only on new bars, using the last closed candle at index 1, and enforces a one-direction-at-a-time model by closing opposite positions before taking new entries. It also adds optional ATR-based stop-losses, reward-to-risk take-profits, dedicated buy/sell execution functions, magic-number tracking, and basic backtesting for repeatable evaluation.

This article presents a standalone Portfolio Analyzer dashboard implemented as an Expert Advisor for MetaTrader 5. It reads account deal history, reconstructs closed positions, and attributes results by magic number or normalized comment to deliver clear per-strategy metrics. The interface provides a vector equity curve, date filters, and strategy selectors, plus a Pearson correlation matrix to reveal strategy redundancy. You can attach it to a separate chart without modifying existing trading EAs.

This article builds a constant-memory EW covariance engine and a chart heatmap for monitoring cross-symbol correlations in MQL5. CEWCovariance updates in O(N²) time per bar and exposes covariance/correlation accessors; CHeatmapRenderer shows a five‑symbol matrix with values and colors. You will learn λ-to‑window mapping, how to set a meaningful min_obs warm‑up, and how to size the variance guard epsilon for real FX M1 data.

Implementation of the N-BEATS architecture for Forex trading in MetaTrader 5 with quantile forecasting and adaptive risk management. The architecture is adapted through bilinear normalization and specialized loss functions for financial data. Backtesting on 2025 data shows inability to generate profits, confirming the gap between theoretical achievements and practical trading performance.

This article walks through creating an MT5 indicator that ingests option chains from native symbols or CSV, inverts prices to implied volatility via a hybrid Newton–Raphson/bisection method, and assembles a clean strike–expiry grid. It then renders a shaded, rotatable 3D surface with the platform's DirectX layer, enabling clear, in-terminal analysis of skew and term structure using live or file-based data.

We extend the stateful supply and demand framework for MetaTrader 5 with a quantitative admission model and a dedicated interaction engine. Candidate zones are scored by structural symmetry, volume participation, and ATR‑normalized displacement, then classified into objective tiers. Admitted zones follow a deterministic lifecycle that tracks first touch, validates bounces, or confirms breakouts, with full telemetry for analysis and reproducibility.

This article turns the verified TDA pipeline into a live MQL5 indicator. It reduces each price window to two persistence-entropy lines (H0 and H1), computes a normalized loop-strength metric with an adaptive percentile band, and places fade marks only when loop strength is high and price hits a window extreme. You can attach the indicator, read six buffers from an Expert Advisor, and tune key window, ranking, and performance parameters.

The article presents a production-oriented Order Block engine for MQL5 packaged as an include class, it validates zones via displacement and market structure break, maintains mitigation state only on closed bars, and avoids heavy copies by passing data by reference. A diagnostic indicator plots zones, and an EA gates logic to new bars for stable performance and reproducible tests.

This article extends single-candlestick analysis to ordered double-candlestick patterns using an MQL5 script. The script encodes candles into symbols, extracts every consecutive two-symbol sequence (treating Aa and aA as different), counts occurrences and percentages, and writes sorted frequency tables to a text file. Readers can quickly identify the most recurrent transitions by symbol, timeframe, and lookback for further statistical testing.

The article presents the revolutionary architecture of PatchTST, a tailored transformer for financial time series analysis that breaks market data into 16-bar patches for efficient processing. We will discuss the full implementation of a trading robot in MQL5 covering everything from mathematical fundamentals and data structures to a ready-made EA with risk management and continuous learning systems.