WilsonMongwe

#### Author: Wilson Mongwe
#### Date: 21/06/2015
#### Website: www.wilsonmongwe.co.za
#### Title: Implementing the EM algorithm to the coin tossing example

####################### Simulating the population data #########################
N= 10000
numberOfCOins=2
components = sample(1:numberOfCOins,prob=c(0.45,0.55),size=N,replace=TRUE) 
thetas= c(0.7,0.2)  ### probablity of obtaining  a head on Coin 2 and Coin 3 respectively

WilsonMongwe

#### Author: Wilson Mongwe
#### Date: 21/06/2015
#### Website: www.wilsonmongwe.co.za
#### Title: This code is for implementing the EM algorithm  to estimate paramters from a mixture 
     #### of three normal distributions.

########################### Simulating the population data #############################

N= 10000 ### Number of draws from the mixture
normals= 3 ### number of normals in the mixture. We have chosen to use 3 in this example.

WilsonMongwe

#### Author: Wilson Mongwe
#### Date: 05/10/2015
#### Website: www.wilsonmongwe.co.za
#### Title: Training the nueral network for the jump test


#### This code tests fro the presence of jumps using nueral networks


#### Training the neural network #####

WilsonMongwe

library(shiny)
library(RQuantLib)

shinyServer(function(input, output) {
  

  
## The model class is the backbone of thsi application  
  

WilsonMongwe

# Load packages ----
library(shiny)
library(quantmod)
library(randomForest)

### The simulation functions

simulateJump=function(mu_,ss_,lambda_,mu2_,sigma_,TotalTime,delta,compare,size)
{
  Sn=0

WilsonMongwe

########## This code calculates non parametric estimates of the jump-difusion parameters #######
simulateJump=function(mu_,ss_,lambda_,mu2_,sigma_,TotalTime,delta)
{
  Sn=0
  times <- c(0)
  while(Sn <= TotalTime)
  {
    n <- length(times)
    u <- runif(1)
    expon <- -log(u)/lambda_

WilsonMongwe

#Creating a twitter authentication in R
#The infomation can be found under the "Keys and Acces Tokens" tab of your twitter aplication.

api_key <- "Enter your API key here" #This is called the: Consumer Key (API Key)
api_secret <- "Enter your secret API key here" #This is called the: Consumer Secret (API Secret)
access_token <- "Enter your access token here" #This is called the: Access Token
access_token_secret <- "Enter your secret access token here" #This is called the: Access Token Secret

#Run this code to set up the twitter authentication.
setup_twitter_oauth(api_key,api_secret,access_token,access_token_secret)

WilsonMongwe

#This code reads in the tweets from the timelines of the three news websites
#Note that there is a limit of 3200 tweets that can be extracted from a users timeline.

fin24Tweets <- userTimeline('Fin24', n=3200)
bdLiveTweets <- userTimeline('BDliveSA', n=3200)
moneyWebTweets <- userTimeline('Moneyweb', n=3200)

WilsonMongwe

#Clean the tweets by removing silly symblos/characters
cleanTweets <- function(tweets)
{
  tweets = gsub("@", "", tweets)
  tweets = gsub("@\\w+", " ", tweets)
  tweets = gsub("https", "", tweets)
  tweets <- gsub("[ |\t]{2,}", " ", tweets)
  tweets <- gsub("[ |\t]{2,}", " ", tweets)
  tweets <- gsub("amp", " ", tweets)
  tweets <- gsub("^ ", "", tweets)  

WilsonMongwe

texts_from_tweets=cleanTweets(texts_from_tweets)
tweets_corpus = Corpus(VectorSource(texts_from_tweets))
tweets_corpus_cleaned<- tm_map(tweets_corpus, function(x)removeWords(x,stopwords()))

# create document term matrix applying some transformations
add_more_stopwords=c("reports","report","opinion","column","can","will","still","read","wants","says","national","world","sport","life","video",
                     "lunchbox","comment","must","miss","new","editorial","popular", "cartoon","international","national","politics","companies",
                     "business","day","top","week","markets","economy","subscribe" , "case","missed","ahead","editor","premium","tomorrow","stories",
                     "click","keep","needs","interview","moneyweb","year","soapbox","news","expo","register","today","need","now","podcast","lineup",
                     "bafana","line-up","reader","question","wednesday","conversation","money","company","writes","how",