History of MQL5.community development

Upgrade your market reading with the Candle-Range Theory suite for MetaTrader 5, a fully MQL5-native solution that converts raw price bars into real-time volatility intelligence. The lightweight CRangePattern library benchmarks each candle’s true range against an adaptive ATR and classifies it the instant it closes; the CRT Indicator then projects those classifications on your chart as crisp, color-coded rectangles and arrows that reveal tightening consolidations, explosive breakouts, and full-range engulfment the moment they occur.

In this article, we create a dynamic holographic dashboard in MQL5 for monitoring symbols and timeframes with RSI, volatility alerts, and sorting options. We add pulse animations, interactive buttons, and holographic effects to make the tool visually engaging and responsive.

This article shows how to programmatically identify bullish and bearish Wolfe Wave patterns and trade them using MQL5. We’ll explore how to identify Wolfe Wave structures programmatically and execute trades based on them using MQL5. This includes detecting key swing points, validating pattern rules, and preparing the EA to act on the signals it finds.

The article presents the transformation of the well-known and popular ADAM gradient optimization method into a population algorithm and its modification with the introduction of hybrid individuals. The new approach allows creating agents that combine elements of successful decisions using probability distribution. The key innovation is the formation of hybrid population individuals that adaptively accumulate information from the most promising solutions, increasing the efficiency of search in complex multidimensional spaces.

We follow up on our last article, where we introduced the indicator couple of the Awesome-Oscillator and the Envelope Channel, by looking at how this pairing could be enhanced with Supervised Learning. The Awesome-Oscillator and Envelope-Channel are a trend-spotting and support/resistance complimentary mix. Our supervised learning approach is a CNN that engages the Dot Product Kernel with Cross-Time-Attention to size its kernels and channels. As per usual, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

In this article, we develop a London Session Breakout System that identifies pre-London range breakouts and places pending orders with customizable trade types and risk settings. We incorporate features like trailing stops, risk-to-reward ratios, maximum drawdown limits, and a control panel for real-time monitoring and management.

In this article, we continue our exploration of building an ensemble of trading strategies and using the MT5 genetic optimizer to tune the strategy parameters. Today, we analyzed the data in Python, showing our model could better predict which strategy would outperform, achieving higher accuracy than forecasting market returns directly. However, when we tested our application with its statistical models, our performance levels fell dismally. We subsequently discovered that the genetic optimizer unfortunately favored highly correlated strategies, prompting us to revise our method to keep vote weights fixed and focus optimization on indicator settings instead.

In this article, we develop a rolling ticker tape in MQL5 for real-time monitoring of multiple symbols, displaying bid prices, spreads, and daily percentage changes with scrolling effects. We implement customizable fonts, colors, and scroll speeds to highlight price movements and trends effectively.

Training Transformer models requires large amounts of data and is often difficult since the models are not good at generalizing to small datasets. The SAMformer framework helps solve this problem by avoiding poor local minima. This improves the efficiency of models even on limited training datasets.

The article dwells on multivariate 3D price charts and their creation. We will also consider how 3D bars predict price reversals, and how Python and MetaTrader 5 allow us to plot these volume bars in real time.

N-BEATS is a revolutionary deep learning model designed for time series forecasting. It was released to surpass classical models for time series forecasting such as ARIMA, PROPHET, VAR, etc. In this article, we are going to discuss this model and use it in predicting the stock market.