dalamar66

require(quantmod)
require(SIT)

dvi.indicator = function(prices) {
   dvi = TTR:::DVI(prices)[,3]
   
   indicator.long = ifelse(dvi <= 0.5, 1, 0)
   indicator.short = ifelse(dvi > 0.5, -1, 0)
   
   indicator = reclass(cbind(indicator.short + indicator.long), prices)

dalamar66

#' Saves symbols (from an environment) to a specified directory
#' 
#' The files (one per symbol) are saved in \code{dir}. The file name
#' is the symbol itself, and the file extension is RData.
#' 
#' @param symbols The symbols
#' @param dir The destination folder (file system directory). It must exist.
#' @param env The environment containing the symbols
#' 
#' @examples

dalamar66

require(quantmod)

prepare.indicator = function(close, indicator, roc.n, normalize=FALSE, func=mean) {
   rets = ROC(close, type="discrete", n=roc.n)
   
   if(normalize) {
      # Normalize the returns to daily
      rets = ((1 + rets) ^ (1/roc.n)) - 1
   }
   

dalamar66

garchAutoTryFit = function(
   ll,
   data,
   trace=FALSE,
   forecast.length=1,
   with.forecast=TRUE,
   ic="AIC",
   garch.model="garch" )
{
   formula = as.formula( paste( sep="",

dalamar66

svmComputeOneForecast = function(
      id,
      data,
      response,
      startPoints,
      endPoints,
      len,
      history=500,
      trace=FALSE,
      kernel="radial",

dalamar66

armaSearch = function(
   xx,  
   minOrder=c(0,0),
   maxOrder=c(5,5),
   trace=FALSE )
{
   bestAic = 1e9
   len = NROW( xx ) 
   for( p in minOrder[1]:maxOrder[1] ) for( q in minOrder[2]:maxOrder[2] )
   {

dalamar66

findBestArma = function(
   xx,  
   minOrder=c(0,0),
   maxOrder=c(5,5),
   trace=FALSE )
{
   bestAic = 1e9
   len = NROW( xx ) 
   for( p in minOrder[1]:maxOrder[1] ) for( q in minOrder[2]:maxOrder[2] )
   {

dalamar66

# Some code to asses an SVM with a two-dimensional feature space:
# trend (price - simple moving average) and relative strength index.
# The code is an adaptation of the code found in the following linkedin post:
# "Trading the RSI using a Support Vector Machine"
# "https://www.linkedin.com/pulse/article/20141103165037-172934333-trading-the-rsi-using-a-support-vector-machine"

# Settings
sma.window = 50 # Number of observations in simple moving average.
rsi.window = 3  # Number of observation in relative strength index (RSI)

dalamar66

# Code to fetch news streams from 5 live sources, process the streams and text
# and apply a simple sentiment scoring algorigthm.
#
# A writeup of the analysis can be found here:
# https://www.linkedin.com/pulse/article/20141109035942-34768479-r-sentiment-scoring-hsbc-w-harvard-general-inquirer

# Define the packages we want to load:
packs = c(
  "tm",                         # Text mining
  "tm.plugin.webmining",        # Web-source plugin for text mining

dalamar66

# Short scripts for testing three different sentiment classifiers on tweets,
# acquiring the tweets used for testing,
# calculating systems' precision, recall and F-measures.


require(RCurl)   # For downloading file from a given URL.
require(twitteR) # Used for the 'twitter' class.
require(sentiment) # For bayes and voter classifiers.
source("sent140.R")  # Used for the Sentiment 140 API. Can be downloaded from here:
# https://github.com/okugami79/sentiment140/blob/master/R/sentiment.r