The CBitBuffer class is vital for bit-level data serialization in MQL5, essential for efficient data storage and retrieval, especially in network communication or file compression. With support for a wide range of data types, the class optimizes space using techniques like Variable-Length Quantity (VLQ) with ZigZag encoding. This approach can significantly save space when dealing with frequently small integer values.

Robust error handling is ensured through an ENUM_BIT_BUFFER_ERROR enum alongside GetLastError() and GetLastErrorString() methods. The class design is optimized with features like internal buffering and exponential array growth to enhance performance. Updates as of 2025 improve operations by eliminating restrictions on mixed read/write processes and enhancing clarity. Such updates contribute to maintaining data integrity, thereby broadening ...

👉 Read | Docs | @mql5dev

#MQL5 #MT5 #Data

In the realm of AI-powered forecasting systems, the importance of data pre-processing cannot be underestimated. Effective pre-processing techniques such as standard scaling, min-max scaling, robust scaling, and one-hot encoding are crucial for enhancing the performance of machine learning models. These techniques ensure that raw financial data becomes 'model-ready,' addressing issues like differing scales, outliers, and categorical features. Within the Python ecosystem, sci-kit learn provides powerful tools for pre-processing. However, MQL5 lacks these native methods, prompting developers to build reusable classes that emulate this functionality. Such pipelines improve data consistency, maintainability, and ultimately the robustness of trading algorithms, blending Python's proficiency with MQL5's capabilities.

👉 Read | Quotes | @mql5dev

#MQL5 #MT5 #Data