Blogs |

ISG Forum Q/R

Wed, 27 Apr 2016 00:00:00 +0000

Lectures

Wed, 27 Apr 2016 00:00:00 +0000

In this section, you will find my course lectures at the University of Gabès

All websites are in french language (the official leaning language at the university of Gabès)

My Packages

Wed, 27 Apr 2016 00:00:00 +0000

1- Tunisianfirms

The goal of tunisianfirms is to spread cotation of Tunisian stock Exchange Firms

Installation

Coming soon You can install the released version of tunisianfirms from CRAN soon. However, you can install from github with these

library(devtools)

## Warning: le package 'devtools' a été compilé avec la version R 4.2.3

## Le chargement a nécessité le package : usethis

devtools::install_github("Foha2001/tunisianfirms")

## Skipping install of 'tunisianfirms' from a github remote, the SHA1 (397c5975) has not changed since last install.
## Use `force = TRUE` to force installation

you have to install devtools before, when installing from github call the package with :

library(tunisianfirms)

Examples

basic example code

cotation("ATTIJARIBANK")

To show a sample_example run

head(sample_data)

## date lib prix
## 1 2019-01-02 GIFDA5/42931 0.01
## 2 2019-01-02 SOMCDA3/3725 0.01
## 3 2019-01-02 TLSDS3/2021 0.01
## 4 2019-01-03 GIFDA4/23727 0.01
## 5 2019-01-03 GIFDA5/42931 0.01
## 6 2019-01-03 SAHDA1/1425 0.01

2- fdesc

The goal of fdesc is to measure describtive statistics and export values into excel file in table format.

Installation

You can install the development version of fdesc like so:

library(devtools)

#devtools::install_github("Foha2001/fdesc")

Example 1

library(fdesc)

To describe your database in raw data, run this code

mytable<- desc(data)
mytable

## mean sd median min max skew
## GSPC.Close 3982.242 104.83348 3983.17 3783.220 4179.76 -0.02180676
## X000001.SS.Close 3208.683 76.10007 3224.02 3045.866 3328.39 -0.46713210
## N225.Close 27198.244 713.81456 27453.48 25716.859 28309.16 -0.49455138
## kurt
## GSPC.Close -0.9946808
## X000001.SS.Close -0.8228077
## N225.Close -1.0899745

Example 2

To describe your database in log return data, run this code

mytable<- Rdesc(data)
mytable

## mean sd median min max
## GSPC.Close -0.0002398655 0.011346004 -1.195375e-03 -0.02523748 0.02258384
## X000001.SS.Close -0.0004534132 0.006795638 -8.528099e-04 -0.01935933 0.01748493
## N225.Close -0.0008768654 0.009052766 3.181096e-05 -0.02489128 0.02465426
## skew kurt
## GSPC.Close 0.05479284 -0.9396836
## X000001.SS.Close 0.11974311 0.4084317
## N225.Close -0.10200599 0.5799189

To export your Table to Excel format

export(mytable)

You will find your Excel table with the name descstatistics.xlsx

Risk Management blog

Wed, 27 Apr 2016 00:00:00 +0000

Material Building packages code
build Normal distribution
function to create asymetric distribution

Material Building packages code

To build package use these commands:

 library(devtools)

## Warning: le package 'devtools' a été compilé avec la version R 4.2.3

## Le chargement a nécessité le package : usethis

 library(usethis)

usethis::use_r(“nomdufichier”) : to add R file and write function
devtools::load_all() : to load all related functions to yours
devtools::install() : to install your package
devtools::document() : to document your package using roxygen
usethis::create_package() : to directly create package
usethis::use_git() : to use package version control vith github
devtools::load_all() : to load all source functions to be able to test package
devtools::check() : to check our package
use_data_raw() : create file to build data
usethis::use_data(my data, compress=“xz”): to add data to my package
use_package(“add any package you need”) : add any package to import from to description file

build Normal distribution

x <- seq(-4,4, length=1000)
hx <- dnorm(x,mean=0, sd=1)
plot(x, hx, type="l", xlab = "valeur de x", ylab="density", main="loi normale")

function to create asymetric distribution

fs = function(x,epsilon,delta) dnorm(sinh(delta*asinh(x)-epsilon))*delta*cosh(delta*asinh(x)-epsilon)/sqrt(1+x^2)
vec = seq(-5,5,0.001)
plot(vec,fs(vec,0,1),type="l")
points(vec,fs(vec,-2,1.3),type="l",col="red")
points(vec,fs(vec,1.3,1),type="l",col="blue")

Videos tutorials

Wed, 27 Apr 2016 00:00:00 +0000

In this section, you’ll find tutorial videos for the various courses taught at the University of Gabes.

Blogs |

ISG Forum Q/R

Lectures

My Packages

1- Tunisianfirms

Installation

Examples

2- fdesc

Installation

Example 1

Example 2

Risk Management blog

Material Building packages code

build Normal distribution

function to create asymetric distribution

Videos tutorials

1) My videos

calcul du taux de rendement du vendeur d’un call

calcul du taux de rendement de l’acheteur d’un call

Définition de la VaR

Gain de l’acheteur d’un Call

Option Call: notion de profit

Définition de l’option: partie 1

Définition de l’option: partie 2

Gordon Shapiro

Le TRI

Evaluation de taux d’actualisation avec la méthode de fisher

Simulation historique a M titres de la VaR

Simulation historique VaR

Evaluation de la valeur finale dans ma méthode de Fisher

Evaluation par la méthode de Fisher

Valeur Actuelle Nette (VAN) présentation 2/2

Valeur Actuelle Nette (VAN) présentation 1/2

2) Other videos

The Garch model

V-Lab (volatility)

Research in finance

What is Entropy ?

Gradient descent

Neural Network

Time series (introduction)

Time series (concepts)

model construction