RiskSimp 0.1.3

RiskSimp is a versatile Python library designed to streamline the management and manipulation of random variables to empower analysts to effortlessly integrate random distributions into their processes, facilitating non-deterministic analysis

RiskSimp: Simplifying Random Variables and Simulation in Python

Version: 0.1.0

RiskSimp is a versatile Python library designed to streamline the management and manipulation of random variables, empowering analysts to effortlessly integrate random distributions into their processes and facilitate non-deterministic analysis.

Table of Contents

• Installation
• Usage
• Modules
  • Objects
  • Utils
  • Variables
    • Continuous
    • Discrete
    • Constant
• Example
• License

Installation

You can install RiskSimp using pip:

pip install RiskSimp

Usage

Import the required modules and classes from RiskSimp:

from RiskSimp import Continuous, Discrete, Constant, Simulation

Modules

Objects

In the "objects" module, you'll find the base classes used throughout the library.

Utils

The "utils" module contains classes to simulate processes with random variables.

Variables

The "variables" module houses various classes for different types of random variables.

Continuous

• Uniform(cls, a: Union[int, float], b: Union[int, float])
• Triangular(cls, min_val: Union[int, float], mode: Union[int, float], max_val: Union[int, float])
• Normal(cls, mean: Union[int, float], std_dev: Union[int, float])
• Exponential(cls, scale: Union[int, float])
• Beta(cls, alpha: Union[int, float], beta: Union[int, float])
• Gamma(cls, shape: Union[int, float], scale: Union[int, float])
• Weibull(cls, alpha: Union[int, float], beta: Union[int, float])
• LogNormal(cls, mean: Union[int, float], std_dev: Union[int, float])

Discrete

• Poisson(cls, lam: Union[int, float])
• Uniform(cls, a: int, b: int)
• Triangular(cls, min_val: int, mode: int, max_val: int)
• Binomial(cls, n: int, p: float)
• Bernoulli(cls, p: float, v_occurrence: Union[int, float] = 1, v_non_occurrence: Union[int, float] = 0)

Constant

• Constant(subclass of Distribution): Represents a non-random constant value.

Example

from RiskSimp import *
import numpy_financial as npf


def restaurante(adecuacion,
                costo_fijo,
                costo_variable,
                inflacion,
                precio_cerveza,
                precio_alitas,
                afluencia_anual,
                variacion_demanda,
                tasa_de_comparacion,
                tasa_descuento):
    years = 11
    adec = [0] * years
    adec[0] = adecuacion
    c_fijo = [costo_fijo * (1 + inflacion) ** i for i in range(years)]
    demanda = [0] + [round(afluencia_anual * (1 + variacion_demanda) ** i) for i in range(years - 1)]
    p_cerveza = [0] + [precio_cerveza * (1 + inflacion) ** i for i in range(years - 1)]
    p_alitas = [0] + [precio_alitas * (1 + inflacion) ** i for i in range(years - 1)]
    ingreso_cerveza = [p * d for p, d in zip(p_cerveza, demanda)]
    ingreso_alitas = [p * d for p, d in zip(p_alitas, demanda)]
    ingreso = [c + a for c, a in zip(ingreso_cerveza, ingreso_alitas)]
    c_variable = [i * costo_variable for i in ingreso]

    flujo = [ing - fij - var - inv for ing, fij, var, inv in zip(ingreso, c_fijo, c_variable, adec)]
    return npf.npv(tasa_descuento, [0] + flujo), npf.irr(flujo)



adecuacion = Constant(1_500_000_000)
adecuacion.change_name("adecuacion")
costo_fijo = Constant(5_000_000)
costo_fijo.change_name("costo_fijo")
costo_variable = Continuous.Uniform(.01, .04)
costo_variable.change_name("costo_variable")
inflacion = Continuous.Normal(.047, 0.002)
inflacion.change_name("inflacion")
precio_cerveza = Continuous.Triangular(12_000, 13_500, 15_000)
precio_cerveza.change_name("precio_cerveza")
precio_alitas = Continuous.Uniform(25_000, 30_000)
precio_alitas.change_name("precio_alitas")
afluencia_anual = Continuous.Triangular(4000, 6000, 8000)
afluencia_anual.change_name("afluencia_anual")
variacion_demanda = Continuous.Uniform(0.02, 0.04)
variacion_demanda.change_name("variacion_demanda")
tasa_de_comparacion = Constant(0.14)
tasa_de_comparacion.change_name("tasa_de_comparacion")
tasa_descuento = Constant(0.1522)
tasa_descuento.change_name("tasa_descuento")

sim = Simulation(restaurante)
sim.set_inputs(adecuacion,
               costo_fijo,
               costo_variable,
               inflacion,
               precio_cerveza,
               precio_alitas,
               afluencia_anual,
               variacion_demanda,
               tasa_de_comparacion,
               tasa_descuento)

sim.complete_analisys()

License

This project is licensed under the MIT License - see the LICENSE file for details.