[Returnanalytics-commits] r2661 - in pkg/PerformanceAnalytics/sandbox/Shubhankit: . Week2 Week2/Code Week2/Vignette Week4/Code Week4/Vignette
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Mon Jul 29 12:37:59 CEST 2013
Author: shubhanm
Date: 2013-07-29 12:37:59 +0200 (Mon, 29 Jul 2013)
New Revision: 2661
Added:
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/ACStdDev.annualized.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/CalmarRatio.Normalized.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/Return.GLM.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/UnsmoothReturn.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/table.ComparitiveReturn.GLM.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/table.UnsmoothReturn.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV-Graph10.pdf
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV-concordance.tex
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV.log
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV.pdf
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV.rnw
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV.synctex.gz
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV.tex
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/NormCalmar-Graph10.pdf
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/NormCalmar-concordance.tex
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/NormCalmar.log
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/NormCalmar.pdf
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/NormCalmar.rnw
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/NormCalmar.synctex.gz
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/NormCalmar.tex
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Code/AcarSim.R
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Vignette/Rplots.pdf
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Vignette/ShaneAcarMaxLoss-003.pdf
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Vignette/ShaneAcarMaxLoss-concordance.tex
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Vignette/ShaneAcarMaxLoss.Rnw
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Vignette/ShaneAcarMaxLoss.log
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Vignette/ShaneAcarMaxLoss.pdf
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Vignette/ShaneAcarMaxLoss.synctex.gz
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week4/Vignette/ShaneAcarMaxLoss.tex
pkg/PerformanceAnalytics/sandbox/Shubhankit/Week6-7/
Log:
Vignette : for Week 2 cum 4
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/ACStdDev.annualized.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/ACStdDev.annualized.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/ACStdDev.annualized.R 2013-07-29 10:37:59 UTC (rev 2661)
@@ -0,0 +1,77 @@
+#' calculate a multiperiod or annualized Autocorrleation adjusted Standard Deviation
+#'
+#' @aliases sd.multiperiod sd.annualized StdDev.annualized
+#' @param x an xts, vector, matrix, data frame, timeSeries or zoo object of
+#' asset returns
+#' @param lag : number of autocorrelated lag factors inputted by user
+#' @param scale number of periods in a year (daily scale = 252, monthly scale =
+#' 12, quarterly scale = 4)
+#' @param \dots any other passthru parameters
+#' @author R
+#' @seealso \code{\link[stats]{sd}} \cr
+#' \url{http://wikipedia.org/wiki/inverse-square_law}
+#' @references Burghardt, G., and L. Liu, \emph{ It's the Autocorrelation, Stupid (November 2012) Newedge
+#' working paper.http://www.amfmblog.com/assets/Newedge-Autocorrelation.pdf \cr
+#' @keywords ts multivariate distribution models
+#' @examples
+#'
+#' data(edhec)
+#' ACsd.annualized(edhec,3)
+
+#'
+#' @export
+#' @rdname ACStdDev.annualized
+ACStdDev.annualized <- ACsd.annualized <- ACsd.multiperiod <-
+ function (R,lag=6, scale = NA, ...)
+ {
+ columns.a = ncol(R)
+ columnnames.a = colnames(R)
+ if(is.na(scale) && !xtsible(R))
+ stop("'x' needs to be timeBased or xtsible, or scale must be specified." )
+
+ if(is.na(scale)) {
+ freq = periodicity(R)
+ switch(freq$scale,
+ #kChec
+ minute = {stop("Data periodicity too high")},
+ hourly = {stop("Data periodicity too high")},
+ daily = {scale = 252},
+ weekly = {scale = 52},
+ monthly = {scale = 12},
+ quarterly = {scale = 4},
+ yearly = {scale = 1}
+ )
+ }
+
+ for(column.a in 1:columns.a) { # for each asset passed in as R
+ # clean the data and get rid of NAs
+ column.return = R[,column.a]
+ acf = as.numeric(acf(as.numeric(column.return), plot = FALSE)[1:lag][[1]])
+ coef= sum(acf*acf)
+ if(!xtsible(R) & is.na(scale))
+ {
+ stop("'x' needs to be timeBased or xtsible, or scale must be specified." )
+ }
+ else
+ {
+ if(column.a == 1) { result = as.numeric(StdDev.annualized(column.return))*(1+2*coef) }
+ else { result = cbind (result, as.numeric(StdDev.annualized(column.return))*(1+2*coef)) }
+ }
+ }
+ dim(result) = c(1,NCOL(R))
+ colnames(result) = colnames(R)
+ rownames(result) = "Autocorrelated Annualized Standard Deviation"
+ return(result)
+ }
+
+###############################################################################
+# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
+#
+# Copyright (c) 2004-2013 Peter Carl and Brian G. Peterson
+#
+# This R package is distributed under the terms of the GNU Public License (GPL)
+# for full details see the file COPYING
+#
+# $Id: ACStdDev.annualized.R
+#
+###############################################################################
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/CalmarRatio.Normalized.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/CalmarRatio.Normalized.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/CalmarRatio.Normalized.R 2013-07-29 10:37:59 UTC (rev 2661)
@@ -0,0 +1,137 @@
+#' calculate a Normalized Calmar or Sterling reward/risk ratio
+#'
+#' Normalized Calmar and Sterling Ratios are yet another method of creating a
+#' risk-adjusted measure for ranking investments similar to the
+#' \code{\link{SharpeRatio}}.
+#'
+#' Both the Normalized Calmar and the Sterling ratio are the ratio of annualized return
+#' over the absolute value of the maximum drawdown of an investment. The
+#' Sterling ratio adds an excess risk measure to the maximum drawdown,
+#' traditionally and defaulting to 10\%.
+#'
+#' It is also traditional to use a three year return series for these
+#' calculations, although the functions included here make no effort to
+#' determine the length of your series. If you want to use a subset of your
+#' series, you'll need to truncate or subset the input data to the desired
+#' length.
+#'
+#' Many other measures have been proposed to do similar reward to risk ranking.
+#' It is the opinion of this author that newer measures such as Sortino's
+#' \code{\link{UpsidePotentialRatio}} or Favre's modified
+#' \code{\link{SharpeRatio}} are both \dQuote{better} measures, and
+#' should be preferred to the Calmar or Sterling Ratio.
+#'
+#' @aliases Normalized.CalmarRatio Normalized.SterlingRatio
+#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
+#' asset returns
+#' @param scale number of periods in a year (daily scale = 252, monthly scale =
+#' 12, quarterly scale = 4)
+#' @param excess for Sterling Ratio, excess amount to add to the max drawdown,
+#' traditionally and default .1 (10\%)
+#' @author Brian G. Peterson
+#' @seealso
+#' \code{\link{Return.annualized}}, \cr
+#' \code{\link{maxDrawdown}}, \cr
+#' \code{\link{SharpeRatio.modified}}, \cr
+#' \code{\link{UpsidePotentialRatio}}
+#' @references Bacon, Carl. \emph{Magdon-Ismail, M. and Amir Atiya, Maximum drawdown. Risk Magazine, 01 Oct 2004.
+#' @keywords ts multivariate distribution models
+#' @examples
+#'
+#' data(managers)
+#' Normalized.CalmarRatio(managers[,1,drop=FALSE])
+#' Normalized.CalmarRatio(managers[,1:6])
+#' Normalized.SterlingRatio(managers[,1,drop=FALSE])
+#' Normalized.SterlingRatio(managers[,1:6])
+#'
+#' @export
+#' @rdname CalmarRatio
+#' QP function fo calculation of Sharpe Ratio
+QP.Norm <- function (R, tau,scale = NA)
+{
+ Sharpe= as.numeric(SharpeRatio.annualized(edhec))
+return(.63519+(.5*log(tau))+log(Sharpe))
+}
+
+CalmarRatio.Normalized <- function (R, tau = 1,scale = NA)
+{ # @author Brian G. Peterson
+
+ # DESCRIPTION:
+ # Inputs:
+ # Ra: in this case, the function anticipates having a return stream as input,
+ # rather than prices.
+ # tau : scaled Time in Years
+ # scale: number of periods per year
+ # Outputs:
+ # This function returns a Calmar Ratio
+
+ # FUNCTION:
+
+ R = checkData(R)
+ if(is.na(scale)) {
+ freq = periodicity(R)
+ switch(freq$scale,
+ minute = {stop("Data periodicity too high")},
+ hourly = {stop("Data periodicity too high")},
+ daily = {scale = 252},
+ weekly = {scale = 52},
+ monthly = {scale = 12},
+ quarterly = {scale = 4},
+ yearly = {scale = 1}
+ )
+ }
+ Time = nyears(R)
+ annualized_return = Return.annualized(R, scale=scale)
+ drawdown = abs(maxDrawdown(R))
+ result = (annualized_return/drawdown)*(QP.Norm(R,Time)/QP.Norm(R,tau))*(tau/Time)
+ rownames(result) = "Normalized Calmar Ratio"
+ return(result)
+}
+
+#' @export
+#' @rdname CalmarRatio
+SterlingRatio.Normalized <-
+ function (R, tau=1,scale=NA, excess=.1)
+ { # @author Brian G. Peterson
+
+ # DESCRIPTION:
+ # Inputs:
+ # Ra: in this case, the function anticipates having a return stream as input,
+ # rather than prices.
+ # scale: number of periods per year
+ # Outputs:
+ # This function returns a Sterling Ratio
+
+ # FUNCTION:
+ Time = nyears(R)
+ R = checkData(R)
+ if(is.na(scale)) {
+ freq = periodicity(R)
+ switch(freq$scale,
+ minute = {stop("Data periodicity too high")},
+ hourly = {stop("Data periodicity too high")},
+ daily = {scale = 252},
+ weekly = {scale = 52},
+ monthly = {scale = 12},
+ quarterly = {scale = 4},
+ yearly = {scale = 1}
+ )
+ }
+ annualized_return = Return.annualized(R, scale=scale)
+ drawdown = abs(maxDrawdown(R)+excess)
+ result = annualized_return/drawdown*(QP.Norm(R,Time)/QP.Norm(R,tau))*(tau/Time)
+ rownames(result) = paste("Normalized Sterling Ratio (Excess = ", round(excess*100,0), "%)", sep="")
+ return(result)
+ }
+
+###############################################################################
+# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
+#
+# Copyright (c) 2004-2013 Peter Carl and Brian G. Peterson
+#
+# This R package is distributed under the terms of the GNU Public License (GPL)
+# for full details see the file COPYING
+#
+# $Id: CalmarRatio.R 1955 2012-05-23 16:38:16Z braverock $
+#
+###############################################################################
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/Return.GLM.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/Return.GLM.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/Return.GLM.R 2013-07-29 10:37:59 UTC (rev 2661)
@@ -0,0 +1,82 @@
+#' True returns represent the flow of information that would determine the equilibrium
+#' value of the fund's securities in a frictionless market. However, true economic
+#' returns are not observed. Instead, Rot
+#' denotes the reported or observed return in
+#' period t, which is a weighted average of the fund's true returns over the most recent k þ 1
+#' periods, includingthe current period.
+#' This averaging process captures the essence of smoothed returns in several
+#' respects. From the perspective of illiquidity-driven smoothing, is consistent
+#' with several models in the nonsynchronous tradingliterat ure. For example, Cohen
+#' et al. (1 986, Chapter 6.1) propose a similar weighted-average model for observed
+#' returns.
+#'
+#' The Geltner autocorrelation adjusted return series may be calculated via:
+#'
+#' @param Ra an xts, vector, matrix, data frame, timeSeries or zoo object of
+#' asset returns
+
+#' @param q order of autocorrelation coefficient
+#' @author R
+#' @references "An econometric model of serial correlation and
+#' illiquidity in hedge fund returns
+#' Mila Getmansky1, Andrew W. Lo*, Igor Makarov
+#' MIT Sloan School of Management, 50 Memorial Drive, E52-432, Cambridge, MA 02142-1347, USA
+#' Received 16 October 2002; received in revised form 7 March 2003; accepted 15 May 2003
+#' Available online 10 July 2004
+#'
+#'
+#' @keywords ts multivariate distribution models
+#' @examples
+#'
+#' data(edhec)
+#' Return.GLM(edhec,4)
+#'
+#' @export
+Return.GLM <-
+ function (Ra,q=3)
+ { # @author Brian G. Peterson, Peter Carl
+
+ # Description:
+
+ # Ra return vector
+ # q Lag Factors
+ # Function:
+ R = checkData(Ra, method="xts")
+ # Get dimensions and labels
+ columns.a = ncol(R)
+ columnnames.a = colnames(R)
+
+ clean.GLM <- function(column.R,q=3) {
+ ma.coeff = as.numeric(arma(edhec[,1],0,q)$theta)
+ column.glm = ma.coeff[q]*lag(column.R,q)
+
+ return(column.glm)
+ }
+
+ for(column.a in 1:columns.a) { # for each asset passed in as R
+ # clean the data and get rid of NAs
+ column.glma = na.skip(R[,column.a],clean.GLM)
+
+ if(column.a == 1) { glm = column.glma }
+ else { glm = cbind (glm, column.glma) }
+
+ }
+
+ colnames(glm) = columnnames.a
+
+ # RESULTS:
+ return(reclass(glm,match.to=Ra))
+
+ }
+
+###############################################################################
+# R (http://r-project.org/) Econometrics for Performance and Risk Analysis
+#
+# Copyright (c) 2004-2012 Peter Carl and Brian G. Peterson
+#
+# This R package is distributed under the terms of the GNU Public License (GPL)
+# for full details see the file COPYING
+#
+# $Id: Return.GLM.R 2163 2012-07-16 00:30:19Z braverock $
+#
+###############################################################################
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/UnsmoothReturn.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/UnsmoothReturn.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/UnsmoothReturn.R 2013-07-29 10:37:59 UTC (rev 2661)
@@ -0,0 +1,36 @@
+UnSmoothReturn<-
+ function(R = NULL,q, ...)
+ {
+ columns = 1
+ columnnames = NULL
+ #Error handling if R is not NULL
+ if(!is.null(R)){
+ x = checkData(R)
+ columns = ncol(x)
+ n = nrow(x)
+ count = q
+ x=edhec
+ columns = ncol(x)
+ columnnames = colnames(x)
+
+ # Calculate AutoCorrelation Coefficient
+ for(column in 1:columns) { # for each asset passed in as R
+ y = checkData(edhec[,column], method="vector", na.rm = TRUE)
+
+ acflag6 = acf(y,plot=FALSE,lag.max=6)[[1]][2:7]
+ values = sum(acflag6*acflag6)/(sum(acflag6)*sum(acflag6))
+
+ if(column == 1) {
+ result.df = data.frame(Value = values)
+ colnames(result.df) = columnnames[column]
+ }
+ else {
+ nextcol = data.frame(Value = values)
+ colnames(nextcol) = columnnames[column]
+ result.df = cbind(result.df, nextcol)
+ }
+ }
+ return(result.df[1:q,]*R) # Unsmooth Return
+
+ }
+ }
\ No newline at end of file
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/table.ComparitiveReturn.GLM.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/table.ComparitiveReturn.GLM.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/table.ComparitiveReturn.GLM.R 2013-07-29 10:37:59 UTC (rev 2661)
@@ -0,0 +1,76 @@
+#' Compenent Decomposition of Table of Unsmooth Returns for GLM Model
+#'
+#' Creates a table of comparitive changes in Normality Properties for Third
+#' and Fourth Moment Vectors i.e. Skewness and Kurtosis for Orignal and Unsmooth
+#' Returns Respectively
+#'
+#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
+#' asset returns
+#' @param ci confidence interval, defaults to 95\%
+#' @param n number of series lags
+#' @param digits number of digits to round results to
+#' @author R
+#' @keywords ts unsmooth GLM return models
+#'
+#' @export
+table.ComparitiveReturn.GLM <-
+ function (R, n = 3, digits = 4)
+ {# @author
+
+ # DESCRIPTION:
+ # Downside Risk Summary: Statistics and Stylized Facts
+
+ # Inputs:
+ # R: a regular timeseries of returns (rather than prices)
+ # n : Number of lags
+ # p = Confifence Level
+ # Output:
+ # A table of estimates of Moving Average
+
+ y = checkData(R, method = "xts")
+ columns = ncol(y)
+ rows = nrow(y)
+ columnnames = colnames(y)
+ rownames = rownames(y)
+
+ # for each column, do the following:
+ for(column in 1:columns) {
+ x = y[,column]
+ skew = skewness(x)
+ arma.coeff= arma(x,0,n)
+ kurt= kurtosis(x)
+ z = c(skew,
+ ((sum(arma.coeff$theta^2)^1.5)*(skew/(sum(arma.coeff$theta^3)))),
+ kurt,
+ (kurt*(sum(arma.coeff$theta^2)^2-6*(sum(arma.coeff$theta^2)*sum(arma.coeff$theta^2)))/(sum(arma.coeff$theta^4))))
+ znames = c(
+ "Skewness ( Orignal) ",
+ "Skewness (Unsmooth)",
+ "Kurtosis (Orignal)",
+ "Kurtosis (Unsmooth)")
+ if(column == 1) {
+ resultingtable = data.frame(Value = z, row.names = znames)
+ }
+ else {
+ nextcolumn = data.frame(Value = z, row.names = znames)
+ resultingtable = cbind(resultingtable, nextcolumn)
+ }
+ }
+ colnames(resultingtable) = columnnames
+ ans = base::round(resultingtable, digits)
+ ans
+
+
+ }
+
+###############################################################################
+# R (http://r-project.org/)
+#
+# Copyright (c) 2004-2013
+#
+# This R package is distributed under the terms of the GNU Public License (GPL)
+# for full details see the file COPYING
+#
+# $Id: table.ComparitiveReturn.GLM
+#
+###############################################################################
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/table.UnsmoothReturn.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/table.UnsmoothReturn.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Code/table.UnsmoothReturn.R 2013-07-29 10:37:59 UTC (rev 2661)
@@ -0,0 +1,79 @@
+#' Compenent Decomposition of Table of Unsmooth Returns
+#'
+#' Creates a table of estimates of moving averages for comparison across
+#' multiple instruments or funds as well as their standard error and
+#' smoothing index
+#'
+#' @param R an xts, vector, matrix, data frame, timeSeries or zoo object of
+#' asset returns
+#' @param ci confidence interval, defaults to 95\%
+#' @param n number of series lags
+#' @param p confidence level for calculation, default p=.99
+#' @param digits number of digits to round results to
+#' @author R
+#' @keywords ts smooth return models
+#'
+#' @export
+table.UnsmoothReturn <-
+ function (R, n = 3, p= 0.95, digits = 4)
+ {# @author
+
+ # DESCRIPTION:
+ # Downside Risk Summary: Statistics and Stylized Facts
+
+ # Inputs:
+ # R: a regular timeseries of returns (rather than prices)
+ # n : Number of lags
+ # p = Confifence Level
+ # Output:
+ # A table of estimates of Moving Average
+
+ y = checkData(R, method = "xts")
+ columns = ncol(y)
+ rows = nrow(y)
+ columnnames = colnames(y)
+ rownames = rownames(y)
+
+ # for each column, do the following:
+ for(column in 1:columns) {
+ x = y[,column]
+
+ z = c(arma(x,0,2)$theta[1],
+ arma(x,0,2)$se.theta[1],
+ arma(x,0,2)$theta[2],
+ arma(x,0,2)$se.theta[2],
+ arma(x,0,2)$se.theta[2])
+ znames = c(
+ "Moving Average(1)",
+ "Std Error of MA(1)",
+ "Moving Average(2)",
+ "Std Error of MA(2)",
+ "Smoothing Invest"
+
+ )
+ if(column == 1) {
+ resultingtable = data.frame(Value = z, row.names = znames)
+ }
+ else {
+ nextcolumn = data.frame(Value = z, row.names = znames)
+ resultingtable = cbind(resultingtable, nextcolumn)
+ }
+ }
+ colnames(resultingtable) = columnnames
+ ans = base::round(resultingtable, digits)
+ ans
+
+
+}
+
+###############################################################################
+# R (http://r-project.org/)
+#
+# Copyright (c) 2004-2013
+#
+# This R package is distributed under the terms of the GNU Public License (GPL)
+# for full details see the file COPYING
+#
+# $Id: table.UnSmoothReturn.R
+#
+###############################################################################
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV-Graph10.pdf
===================================================================
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Property changes on: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV-Graph10.pdf
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Added: svn:mime-type
+ application/octet-stream
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV-concordance.tex
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV-concordance.tex (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV-concordance.tex 2013-07-29 10:37:59 UTC (rev 2661)
@@ -0,0 +1,2 @@
+\Sconcordance{concordance:ACFSTDEV.tex:ACFSTDEV.rnw:%
+1 44 1 1 5 1 4 20 1 1 2 1 0 4 1 8 0 1 1 8 0 1 2 1 0 1 2 5 0 1 2 3 1}
Added: pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV.log
===================================================================
--- pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV.log (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/Shubhankit/Week2/Vignette/ACFSTDEV.log 2013-07-29 10:37:59 UTC (rev 2661)
@@ -0,0 +1,358 @@
+This is pdfTeX, Version 3.1415926-2.4-1.40.13 (MiKTeX 2.9) (preloaded format=pdflatex 2013.7.14) 28 JUL 2013 12:50
+entering extended mode
+**ACFSTDEV.tex
+
+("C:\Users\shubhankit\Desktop\New folder\pkg\PerformanceAnalytics\sandbox\Shubh
+ankit\Week2\Vignette\ACFSTDEV.tex"
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[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/returnanalytics -r 2661
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