Generators

The quotek rand generators namespace contains c++11 distributions wrappers which add an extra abstraction layer and also fit the quotek dataset format. It mainly allows to generate randoml value datasets having different kinds of distributions, which can prove really useful for financial modeling.

namespace quotek::rand::generators

Functions

quotek::data::records uniform(int size, float min, float max)

Generates a set of random values which are uniformly distributed.

Return
the generated dataset.
Parameters
  • size -

    number of values we want in the dataset.

  • min -

    Minimum value that must be present inside result dataset.

  • max -

    Maximum value that must be present inside result dataset.

quotek::data::records normal(int size, float mean, float sigma)

Generates a set of random values which are normally distributed. (probaly the most useful to modelize asset prices)

Return
the generated dataset.
Parameters
  • size -

    number of values we want in the dataset.

  • mean -

    average value we want for our generator.

  • sigma -

    wanted standard deviation.

quotek::data::records lognormal(int size, float mean, float sigma)

Generates a set of random values which are log-normally distributed.

Return
the generated dataset.
Parameters
  • size -

    number of values we want in the dataset.

  • mean -

    mean we want to use for the underlying normal distribution. value we want for our generator.

  • sigma -

    standard deviation we want to use for the underlying normal distribution.

quotek::data::records binomial(int size, int experiments, float success_probability)

Generates a set of random values which are distributed using a binomial law.

Return
the generated dataset.
Parameters
  • size -

    number of values we want in the dataset.

  • experiments -

    the number of tries for a number

  • success_probability -

    probability that each experiment succeeds .

quotek::data::records poisson(int size, int average_occurence)

Generates a set of random values which are distributed using a poisson law.

Return
the generated dataset.
Parameters
  • size -

    number of values we want in the dataset.

  • average_occurence -

    number of times an event must occur on everage for the series.