RSD Histogram is currently recognized as a versatile indicator in the market, initially completed in 2018. Significant development efforts have transitioned this tool into its final form, which now operates on a subscription basis. While earlier versions are available for experimentation, the latest iteration incorporates all recent enhancements. The final release aims at providing advanced functionality and is designed to meet the evolving needs of trading analysts and developers. Although the legacy version remains accessible, the subscription model offers sustained updates and support for the latest market demands.

👉 Read | Freelance | @mql5dev

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Improving library classes involves handling changes in graphical object properties across multiple charts and simplifying event tracking with a custom indicator. We can control standard graphical object properties in the chart window using an event model. Events are handled via the OnChartEvent() handler, which simplifies code and resolves property filling issues upon object creation. However, events from other charts don't directly reach the primary chart's OnChartEvent(). To overcome this, custom events via EventChartCustom() are sent to the primary chart.

This process involves assigning event IDs and transferring them with chart IDs using the lparam parameter, while the object name is sent through the sparam parameter. Thus, events from all charts can be managed by a program on a single chart. The CChartObjectsControl class helps manage event con...

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Implementing a button for managing advisor functions can streamline workflow. This example demonstrates how a button is used to close all active positions across various instruments. The example also includes functionality for handling button events. Methods are provided for closing positions based on the symbol name and for counting positions related to each symbol. This allows for efficient control and management of trading positions. By integrating these methods, advisors can enhance their trading logic and improve execution efficiency. Such implementations aid in maintaining structured and responsive trading systems. This systematic approach benefits both new and experienced developers in optimizing code performance.

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The "Quarters Theory" indicator segments price movements around a central reference level, enhancing analysis for multiple assets including Forex, stocks, and commodities. This tool provides visual representation of quarter divisions on the chart, aiding in technical analysis by highlighting potential support and resistance levels.

The indicator calculates a flexible base level, with options including current daily close, previous day, week, and month closes, as well as user-defined levels. It supports various units for quarter size, appropriate for different instruments: pips for Forex, points for other assets, percentages of the base level, or fixed price values.

It adapts automatically to distinguish between Forex and non-Forex assets, adjusts pip calculations accordingly, and manages situations with unavailable timeframes. This makes it a versat...

👉 Read | Signals | @mql5dev

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Explore the advanced framework for enhancing algorithmic trading with MetaTrader 5. The article introduces a disciplined pipeline that filters noise in indicator selection, highlighting its transformation into a predictive engine using a compact neural network. This network is trained on multi-quarter data, outputting an ONNX model for seamless integration with MQL5. Practical advancements include adaptive indicators like FrAMA and TRIX, alongside a robust feature and label engineering process ensuring reliable predictions. Emphasizing a straightforward multi-layer perceptron (MLP) structure, the approach focuses on efficient, transparent model deployment, minimizing overfitting risks and enhancing real-time trading strategy execution.

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#MQL5 #MT5 #Strategy

In this update of our AI-powered trading system in MQL5, we've focused on further modularization and the automation of trading functionalities. The UI components have been separated into a dedicated include file, enhancing code cleanliness and maintainability. This also streamlines development, testing, and integration of new features like dynamic UIs and automatic signal execution.

Automatic signal checks have been implemented for new bars, enabling trade execution based on AI-generated signals parsed from JSON input. These signals outline entry points and Stop-Loss/Take-Profit levels, offering a clear trading strategy devoid of emotional biases, and improving efficiency.

Our visualization system now signals patterns like engulfing or divergences on charts using arrows, lines, and labels, facilitating clear pattern recognition. We've also implemen...

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In the realm of trading automation, an Expert Advisor (EA) can be designed to execute trades randomly based on certain parameters. For instance, consider using variables such as "price_open" for the trade entry point and "price_stoploss" for defining risk thresholds. Determining "risk_percent_equity" allows the EA to calculate risk exposure in terms of percentage of the total deposit. Proper configuration of these parameters is crucial for maintaining a balance between risk management and potential returns. Randomized trading algorithms may illustrate the unpredictability of market conditions and the importance of controlled risk strategies in algorithmic trading environments. Ensure thorough backtesting to evaluate the EA's performance and adjust parameters as needed for optimal outcomes.

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