IEEE 754 Floating-Point Representation and Precision in MQL5

Summary

This article introduces how floating-point values are represented in memory and why decimal numbers used in MQL5 calculations may not be stored exactly as written. It places the discussion in the context of the IEEE 754 standard and explains that floating-point behavior differs from integer arithmetic. Example code lets readers inspect a float or double value’s underlying bytes in hexadecimal form, illustrating the gap between a displayed decimal and its binary representation.

The practical lesson for trading software is to account for finite precision and rounding when calculations influence prices or other financial decisions. The article emphasizes that rounding effects exist even when programmers do not explicitly request rounding, and encourages further study of rounding rules and representable values. It is an introductory treatment: the author does not fully develop floating-point arithmetic or provide a complete guide to robust financial calculation methods, and notes that other number formats exist outside the scope of the discussion.

Key ideas

• IEEE 754 defines the floating-point representation used by MQL5 and many other languages.
• A decimal value may not have an exact binary floating-point representation.
• A union-based example exposes a float or double value’s memory bytes in hexadecimal form.
• Rounding and finite precision can affect calculations used in trading applications.
• The discussion is introductory and does not cover all floating-point formats or calculation practices.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.