StuartGordonReid

results <- emh::is_random(my.zoo.object)
emh::plot_results(results)
View(results)

StuartGordonReid

library(devtools)
devtools::install_github(repo="stuartgordonreid/emh")

StuartGordonReid

testRobustness <- function(t = (252 * 7)) {
  # Generate an underlying signal.
  signal <- sin(seq(1, t)) / 50
  signal <- signal - mean(signal)
  
  # For different noise levels
  sds <- seq(0.0, 0.020, 0.0005)
  cratios <- c()
  for (s in sds) {
    # Generate a noisy signal

StuartGordonReid

bettingStrategyProof <- function(t = (252 * 7), w = 5, sd = 0.02, sims = 30) {
  # Store market and strategy returns.
  markets <- NULL
  strats <- NULL
  
  for (i in 1:sims) {
    # Generate an underlying signal.
    signal <- sin(seq(1, t)) / 50
    signal <- signal - mean(signal)
    

StuartGordonReid

testRobustness <- function(t = (252 * 7)) {
  # Generate an underlying signal.
  signal <- sin(seq(1, t)) / 50
  signal <- signal - mean(signal)
  
  # For different noise levels
  sds <- seq(0.0, 0.020, 0.0005)
  cratios <- c()
  for (s in sds) {
    # Generate a noisy signal

StuartGordonReid

compressionTest <- function(code, years = 7, algo = "g") {
  # The generic Quandl API key for TuringFinance.
  Quandl.api_key("t6Rn1d5N1W6Qt4jJq_zC")
  
  # Download the raw price data.
  data <- Quandl(code, rows = -1, type = "xts")
  
  # Extract the variable we are interested in.
  ix.ac <- which(colnames(data) == "Adjusted Close")
  if (length(ix.ac) == 0) 

StuartGordonReid

#' @title Given a log price process, X, compute the Z-score which can be used 
#' to accept or reject the hypothesis that the process evolved according to a 
#' Brownian Motion model with drift and stochastic volatility.
#' 
#' @description Given a log price process, X, and a sampling interval, q, this 
#' method returns a Z score indicating the confidence we have that X evolved 
#' according to a Brownian Motion mode with drift and stochastic volatility. This
#' heteroskedasticity-consistent variance ratio test essentially checks to see 
#' whether or not the observed Mr statistic for the number of observations, is 
#' within or out of the limiting distribution defined by the Asymptotic Variance.

StuartGordonReid

#' @title Estimator for the value of the asymptotic variance of the Mr statistic. 
#' This is equivalent to a weighted sum of the asymptotic variances for each of 
#' the autocorrelation co-efficients under the null hypothesis.
#' 
#' @details Given a log price process, X, and a sampling interval, q, this 
#' method is used to estimate the asymptoticvariance of the Mr statistic in the 
#' presence of stochastic volatility. In other words, it is a heteroskedasticity 
#' consistent estimator of the variance of the Mr statistic. This parameter is 
#' used to estimate the probability that the given log price process was 
#' generated by a Brownian Motion model with drift and stochastic volatility. 

StuartGordonReid

#' @title Compute the Mr statistic.
#' 
#' @description Compute the Mr statistic. The Md statistic is the ratio between 
#' two estimate values for Sigma computed using the calibrateSigma function for 
#' sampling intervals 1 and the estimate value for Sima computed using the
#' CalibrateSigmaOverlapping function for sampling inveral q minus 1. 
#' This statistic should converge to zero.
#' 
#' @inheritParams calibrateSigma
#' @return Mr double :: The Mr statistic defined by Lo and MacKinlay.

StuartGordonReid

#' @title Compute the Md statistic.
#' 
#' @description Compute the Md statistic. The Md statistic is the difference 
#' between two estimate values for Sigma computed using the calibrateSigma 
#' function for sampling intervals 1 and the estimate value for Sima computed 
#' using the CalibrateSigmaOverlapping function for sampling inveral q. 
#' This statistic should converge to zero.
#' 
#' @inheritParams calibrateSigma
#' @return Md double :: The Md statistic defined by Lo and MacKinlay.