Class CRandom
This software component for MetaTrader 5 is built to enhance the capabilities of your trading environment. This library provides a collection of modular, reusable code. It is utilized by developers to organize common functions, allowing for the integration of complex logic across multiple Expert Advisors, indicators, or scripts without the need for code duplication.
How to Setup and Use Class CRandom
1. Storage: Place library files in the MQL/Libraries directory to ensure they are accessible to your projects.
2. Implementation: Include the library in your code using the #import directive, ensuring you match the exact function names and parameters.
3. Compilation: Ensure the library is present in the directory before you compile your main EA or script, as the compiler links them during this phase.
4. Management: Keep libraries organized in sub-folders if you manage many custom functions to maintain a clean project structure.
Frequently Asked Questions
Q: What is a library file used for? A: Libraries store reusable code modules, allowing you to centralize common logic used by multiple EAs or indicators.
Q: Is a library executable? A: No, libraries are non-executable files containing functions; they must be imported into an EA, indicator, or script to function.
Q: Can I update a library while the platform is running? A: You should compile your EA or script after updating a library to ensure the latest code changes are integrated.
Description & Settings
The standard random number generator in MQL has a fairly limited number of possible values from 0 to 32767 (15-bits usable, 2^15 =
32,768 values
).
It is classified as linear congruential generator (LCG) which is considered a very basic random number generator.
The implementation of MQL can be found here:
This generator has easily detectable statistical flaws which fail most statistical tests of randomness from the test suite.
For this reason, MathRand() is not suitable for large-scale Monte Carlo simulations or where high-quality randomness is critical.
PCG Random Number Generator
This class is a wrapper class around the high-quality 32-bits PCG generator. This RNG has an output range of 2^32 which means it can generate
4,294,967,296 possible values
.
A permuted congruential generator (PCG) is a pseudorandom number generation algorithm developed in 2014 which applies an output transformation (random shift or rotation) to eliminate the short period problem in the low-order bits that other LCG generators suffer from. It achieves excellent statistical performance with small and fast code, and small state size.
The class provides an easy interface for random number generation.
This GRAPH was obtained for random double numbers from 0 to 10
And, that one for random integer numbers from 0 to 10
The BITMAP of the low-order bits shows also a random pattern
