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- 2019.06.16 06:20
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- 2022.03.15 22:32
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MathRand() Function
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 MathRand() can be found here: https://www.mql5.com/en/docs/predefined/_randomseed
This generator has easily detectable statistical flaws which fail most statistical tests of randomness from the PractRand 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
PCG is a family of random number generators, which are fast, statistically excellent, and have small code size.
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.
//+------------------------------------------------------------------+ //| Class CRandom | //| Usage: Random number generation using the 32-bit PCG generator. | //+------------------------------------------------------------------+ class CRandom { public: //--- Constructor and destructor CRandom(void); // Constructor (auto-seed) CRandom(const ulong initstate,const ulong initseq); // Constructor (custom seed) //--- Methods for generation of random numbers int RandomInteger(void); // Random integer [0,2147483648) int RandomInteger(const int max); // Random integer [0,max) int RandomInteger(const int min,const int max); // Random integer [min,max) double RandomDouble(void); // Random double [0.0,1.0) double RandomDouble(const double max); // Random double [0.0,max) double RandomDouble(const double min,const double max); // Random double [min,max) bool RandomBoolean(void); // Random true/false (equal probability) bool RandomBoolean(const double prob_true); // Random true/false (with prob_true) double RandomNormal(void); // Random double (normal distribution) double RandomNormal(const double mu,const double sigma); // Random double (normal distribution) };
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

Advisor for trading Gbp/Chf. Algorithm for bar opening prices. Timeframe H1.

This indi will draw OBJ_ARROW_BUY/OBJ_ARROW_SELL when find signals on Arrow Indi Buffers.

Smoothed CCI

Improved version based on RSI_candles by © mladen 2018 https://www.mql5.com/en/code/20968