[Returnanalytics-commits] r3560 - in pkg/PerformanceAnalytics/sandbox: . qwafafew2014 qwafafew2014/R qwafafew2014/data
noreply at r-forge.r-project.org
noreply at r-forge.r-project.org
Sun Nov 23 15:28:24 CET 2014
Author: peter_carl
Date: 2014-11-23 15:28:24 +0100 (Sun, 23 Nov 2014)
New Revision: 3560
Added:
pkg/PerformanceAnalytics/sandbox/qwafafew2014/
pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/
pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/Baily_LopezdePrado_stop-out.R
pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/mbb.R
pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/table.RiskStats.R
pkg/PerformanceAnalytics/sandbox/qwafafew2014/data/
pkg/PerformanceAnalytics/sandbox/qwafafew2014/data/Futures Trend 201409a.csv
pkg/PerformanceAnalytics/sandbox/qwafafew2014/qwafafew2014.R
pkg/PerformanceAnalytics/sandbox/qwafafew2014/qwafafew2014.Rpres
Log:
- presentation and code for Chicago chapter QWAFAFEW presentation
Added: pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/Baily_LopezdePrado_stop-out.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/Baily_LopezdePrado_stop-out.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/Baily_LopezdePrado_stop-out.R 2014-11-23 14:28:24 UTC (rev 3560)
@@ -0,0 +1,108 @@
+# --------------------------------------------------------------------
+# Max Quartile Loss at Confidence level MLdP
+# --------------------------------------------------------------------
+
+MaxQL<-function(R, confidence=0.95, type=c("ac","normal"), ...) {
+ # @TODO Handle multi-column output
+ if(!is.vector(R))
+ x = checkData(R[,1])
+ else
+ x = checkData(R)
+ x = na.omit(x)
+ if(type[1]=="ac"){
+ mu = mean(x, na.rm = TRUE)
+ sigma_infinity = StdDev(x)
+ phi = cov(x[-1],x[-length(x)])/(cov(x[-length(x)]))
+ phi=.5
+ sigma = sigma_infinity*((1-phi^2)^0.5)
+ dPi0 = 0
+ minQ = minQ(phi, mu, sigma, dPi0, confidence)
+ }
+ if(type[1]=="normal"){
+ minQ = minQ_norm(x, confidence)
+ }
+ MaxQL=min(0,minQ[[1]])
+# rownames(MaxQL) = paste0("MaxQL (", confidence*100, "%)") #,"t*")
+ return(MaxQL)
+}
+
+# --------------------------------------------------------------------
+# Max Quartile Loss at Confidence level - First-order AC
+# --------------------------------------------------------------------
+getQ <- function(bets, phi, mu, sigma, dPi0, confidence) {
+ # Compute analytical solution to quantile
+ #1) Mean (eq 15)
+ mean=(phi^(bets+1)-phi)/(1-phi)*(dPi0-mu)+mu*bets # wrong?
+ #2) Variance (eq 15)
+ var=sigma^2/(phi-1)^2
+ var=var*((phi^(2*(bets+1))-1)/(phi^2-1)-2*(phi^(bets+1)-1)/(phi-1)+bets+1)
+ #3) Quantile
+ q=mean+qnorm(1-confidence)*(var^0.5)
+ #print(sprintf("bets %g, mean %g, var %g, var1 %g, var2 %g, var3 %g, q %g", bets, mean, var, var1, var2, var3, q))
+ q
+}
+
+goldenSection<-function(a, b, FUN, minimum = TRUE, ...) {
+ FUN = match.fun(FUN)
+ tol = 10^-9
+ sign = 1
+
+ if(minimum) sign = -1
+ N = round(ceiling(-2.078087*log(tol/abs(b-a))))
+ r = 0.618033989
+ c = 1.0 - r
+ x1 = r*a + c*b
+ x2 = c*a + r*b
+ f1 = sign * FUN(x1,...=...)
+ f2 = sign * FUN(x2,...=...)
+ #print(f1); print(f2)
+ for(i in 1:N){
+ if(f1>f2){
+ a = x1
+ x1 = x2
+ f1 = f2
+ x2 = c*a+r*b
+ f2 = sign*FUN(x2,...=...)
+ } else {
+ b = x2
+ x2 = x1
+ f2 = f1
+ x1 = r*a + c*b
+ f1 = sign*FUN(x1,...=...)
+ }
+ }
+ if(f1 < f2){
+ return(list(minQ=sign*f1, t=x1))
+ } else {
+ return(list(minQ=sign*f2, t=x2))
+ }
+}
+
+minQ <- function(phi, mu, sigma, dPi0, confidence) {
+ q = 0
+ bets = 0
+ while (q <= 0) {
+ bets = bets + 1
+ q = getQ(bets, phi, mu, sigma, dPi0, confidence)
+ }
+ #print(sprintf("bets %g, q %g", bets, q))
+ goldenSection(0,bets,getQ,FALSE,phi=phi,mu=mu,sigma=sigma,dPi0=dPi0,confidence=confidence)
+}
+
+# minQ(0.5, 1, 2, 1, 0.95)
+# MinQ = -9.15585580378
+# Time at MinQ = 12.4832517718
+
+# --------------------------------------------------------------------
+# Max Quartile Loss at Confidence level - Assuming IID (eq. 5)
+# --------------------------------------------------------------------
+
+minQ_norm<-function(x, confidence){
+# Calculate the maximum drawdown for a normal distribution, assuming iid returns
+ x = na.omit(x)
+ sd = StdDev(x)
+ mu = mean(x, na.rm = TRUE)
+ minQ = -((qnorm(1-confidence)*sd)^2)/(4*mu)
+ t = ((qnorm(1-confidence)*sd)/(2*mu))^2
+ return(list(minQ=minQ,t=t))
+}
Added: pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/mbb.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/mbb.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/mbb.R 2014-11-23 14:28:24 UTC (rev 3560)
@@ -0,0 +1,27 @@
+mbb <- function(R, N=length(R), k=6, nrep=1000, verbose=TRUE, cores=6){
+#' Moving blocks bootstrap
+#'
+#' Follows Efron and Tibshirani (sec. 8.6).
+#' With a nod to http://stat.wharton.upenn.edu/~buja/STAT-541/time-series-bootstrap.R
+
+#' N length of the resulting time series
+#' k size of the moving blocks
+#' nrep number of bootstrap replications
+ require(foreach)
+ require(doMC)
+ registerDoMC(cores)
+ .mdd <- function(R, N, k){
+ result.mbb <- rep(NA, N) # local vector for a bootstrap replication
+ for(j in 1:ceiling(N/k)) { # fill the vector with random blocks
+ endpoint <- sample(k:N, size=1) # by randomly sampling endpoints
+ result.mbb[(j-1)*k+1:k] <- R[endpoint-(k:1)+1] # and copying blocks to the local vector
+ }
+ result <- as.matrix(result.mbb[1:N])# trim overflow when k doesn't divide N
+ return(result)
+ }
+
+ result <- foreach(i=1:nrep, .combine=cbind, .inorder=TRUE) %dopar% {
+ .mdd(R=R, N=N, k=k)
+ }
+ return(result)
+}
\ No newline at end of file
Added: pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/table.RiskStats.R
===================================================================
--- pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/table.RiskStats.R (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/qwafafew2014/R/table.RiskStats.R 2014-11-23 14:28:24 UTC (rev 3560)
@@ -0,0 +1,229 @@
+# Additional and re-organized tables for WB presentations
+
+table.RiskStats <-
+function (R, ci = 0.95, scale = NA, Rf = 0, MAR = .1/12, p= 0.95, digits = 4)
+{# @author Peter Carl
+ # Risk Statistics: Statistics and Stylized Facts
+
+ y = checkData(R, method = "zoo")
+ if(!is.null(dim(Rf)))
+ Rf = checkData(Rf, method = "zoo")
+ # Set up dimensions and labels
+ columns = ncol(y)
+ rows = nrow(y)
+ columnnames = colnames(y)
+ rownames = rownames(y)
+
+ if(is.na(scale)) {
+ freq = periodicity(y)
+ 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}
+ )
+ }
+
+ # for each column, do the following:
+ for(column in 1:columns) {
+ x = na.omit(y[,column,drop=FALSE])
+ # for each column, make sure that R and Rf are for the same dates
+ if(!is.null(dim(Rf))){ # if Rf is a column
+ z = merge(x,Rf)
+ zz = na.omit(z)
+ x = zz[,1,drop=FALSE]
+ Rf.subset = zz[,2,drop=FALSE]
+ }
+ else { # unless Rf is a single number
+ Rf.subset = Rf
+ }
+
+ z = c(
+ Return.annualized(x, scale = scale),
+ StdDev.annualized(x, scale = scale),
+ SharpeRatio.annualized(x, scale = scale, Rf = Rf),
+ DownsideDeviation(x,MAR=0)*sqrt(scale),# Add annualization to this function
+ SortinoRatio(x)*sqrt(scale), # New function adds annualization
+ PerformanceAnalytics:::AverageDrawdown(x),
+ maxDrawdown(x),
+ SterlingRatio(x),
+ VaR(x, p=p,method="historical"),
+ ES(x, p=p,method="historical"),
+ skewness(x),
+ kurtosis(x),
+ VaR(x, p=p),
+ ES(x, p=p),
+ SharpeRatio(x, p=p, Rf=Rf, FUN="ES", annualize=TRUE),
+ length(x)
+ )
+ znames = c(
+ "Annualized Return",
+ "Annualized Std Dev",
+ "Annualized Sharpe Ratio",
+ "Annualized Downside Deviation",
+ "Annualized Sortino Ratio",
+ "Average Drawdown",
+ "Maximum Drawdown",
+ "Sterling Ratio (10%)",
+ paste("Historical VaR (",base::round(p*100,1),"%)",sep=""),
+ paste("Historical ETL (",base::round(p*100,1),"%)",sep=""),
+ "Skewness",
+ "Excess Kurtosis",
+ paste("Modified VaR (",base::round(p*100,1),"%)",sep=""),
+ paste("Modified ETL (",base::round(p*100,1),"%)",sep=""),
+ paste("Annualized Modified Sharpe Ratio (ETL ", base::round(p*100,1),"%)",sep=""),
+ "# Obs"
+ )
+ 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
+}
+
+table.PerfStats <-
+function (R, scale = NA, Rf = 0, digits = 4)
+{# @author Peter Carl
+ # Performance Statistics: Statistics and Stylized Facts
+
+ y = checkData(R)
+ if(!is.null(dim(Rf)))
+ Rf = checkData(Rf)
+ # Set up dimensions and labels
+ columns = ncol(y)
+ rows = nrow(y)
+ columnnames = colnames(y)
+ rownames = rownames(y)
+
+ if(is.na(scale)) {
+ freq = periodicity(y)
+ 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}
+ )
+ }
+
+ # for each column, do the following:
+ for(column in 1:columns) {
+ x = na.omit(y[,column,drop=FALSE])
+ # for each column, make sure that R and Rf are for the same dates
+ if(!is.null(dim(Rf))){ # if Rf is a column
+ z = merge(x,Rf)
+ zz = na.omit(z)
+ x = zz[,1,drop=FALSE]
+ Rf.subset = zz[,2,drop=FALSE]
+ }
+ else { # unless Rf is a single number
+ Rf.subset = Rf
+ }
+
+ z = c(
+ Return.cumulative(x),
+ Return.annualized(x, scale = scale),
+ StdDev.annualized(x, scale = scale),
+ length(subset(x, x>0)),
+ length(subset(x, x<=0)),
+ length(subset(x, x>0))/length(x),
+ mean(subset(x, x>0)),
+ mean(subset(x, x<=0)),
+ mean(x),
+ AverageDrawdown(x),
+ AverageRecovery(x)
+ )
+ znames = c(
+ "Cumulative Return",
+ "Annualized Return",
+ "Annualized Std Dev",
+ "# Positive Months",
+ "# Negative Months",
+ "% Positive Months",
+ "Average Positive Month",
+ "Average Negative Month",
+ "Average Month",
+ "Average Drawdown",
+ "Average Months to Recovery"
+ )
+ 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
+}
+
+table.RiskContribution <- function(R, p, ..., weights=NULL, scale=NA, geometric = TRUE) {
+
+ R = na.omit(R)
+ if(is.null(weights)) {
+ message("no weights passed in, assuming equal weighted portfolio")
+ weights = rep(1/dim(R)[[2]], dim(R)[[2]])
+ }
+ if (is.na(scale)) {
+ freq = periodicity(R)
+ switch(freq$scale, minute = {
+ stop("Data periodicity too high")
+ }, hourly = {
+ stop("Data periodicity too high")
+ }, daily = {
+ scale = 252
+ }, weekly = {
+ scale = 52
+ }, monthly = {
+ scale = 12
+ }, quarterly = {
+ scale = 4
+ }, yearly = {
+ scale = 1
+ })
+ }
+
+ # Returns
+ # ret.col = colMeans(R)*weights
+ ret.col = Return.annualized(R, geometric=geometric)*weights
+ percret.col = ret.col/sum(ret.col)
+ result = cbind(t(ret.col), t(percret.col))
+ # Standard Deviation
+ sd.cols = StdDev(R, weights=weights, invert=TRUE, portfolio_method="component", p=(1-1/12))
+ result = cbind(sd.cols$contribution*sqrt(scale), sd.cols$pct_contrib_StdDev, result)
+ # VaR?
+ var.cols = VaR(R, weights=weights, method="gaussian", portfolio_method="component", p=(1-1/12))
+ result = cbind(var.cols$contribution, var.cols$pct_contrib_VaR, result)
+
+ mvar.cols = VaR(R, weights=weights, method="gaussian", portfolio_method="component", p=(1-1/12))
+ result = cbind(mvar.cols$contribution, mvar.cols$pct_contrib_VaR, result)
+
+ # ES
+ es.cols = ES(R, weights=weights, method="gaussian", portfolio_method="component", p=(1-1/12))
+ result = cbind(es.cols$contribution, es.cols$pct_contrib_ES, result)
+
+ mes.cols = ES(R, weights=weights, method="modified", portfolio_method="component", p=(1-1/12))
+ result = cbind(weights, mes.cols$contribution, mes.cols$pct_contrib_MES, result)
+ total = colSums(result)
+
+ result = rbind(result, colSums(result))
+ rownames(result) = c(colnames(R),"Total")
+# colnames(result) = c("Weights", "Contribution to mETL", "Percentage Contribution to mETL", "Contribution to gETL", "Percentage Contribution to gETL", "Contribution to Annualized StdDev", "Percentage Contribution to StdDev", "Contribution to Annualized E(R)", "Percentage Contribution to E(R)")
+
+ colnames(result) = c("Weights", "Contribution to mETL", "%Contribution to mETL", "Contribution to gETL", "%Contribution to gETL", "Contribution to mVaR", "%Contribution to mVaR", "Contribution to gVaR", "%Contribution to gVaR", "Contribution to Annualized StdDev", "%Contribution to StdDev", "Contribution to Annualized E(R)", "%Contribution to E(R)")
+ return(result)
+
+}
Added: pkg/PerformanceAnalytics/sandbox/qwafafew2014/data/Futures Trend 201409a.csv
===================================================================
--- pkg/PerformanceAnalytics/sandbox/qwafafew2014/data/Futures Trend 201409a.csv (rev 0)
+++ pkg/PerformanceAnalytics/sandbox/qwafafew2014/data/Futures Trend 201409a.csv 2014-11-23 14:28:24 UTC (rev 3560)
@@ -0,0 +1,298 @@
+,Trend1,Trend2,Trend3,Trend4,Trend5,Trend6,Trend7,Trend8,Trend9,Trend10,Trend11,Trend12,Trend13,Trend14,Trend15,Trend16,Trend17,Trend18,Trend19,Trend20,Trend21,Trend22,EDHEC CTA,NewEdge CTA,NewEdge Trend Index,Barclay BTOP50
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+2002-09-30,0.0763,0.0344,0.06,0.1049737,0.0344,,0.0473,0.0692,,0.0513,0.045,0.0143,,,0.0866,0.082615307,0.086873878,0.069,0.0329,0.0334,0.0335,0.0653,0.0284,0.0218,0.0584,0.0339
+2002-10-31,-0.0796,-0.0272,-0.0692,-0.0447151,-0.0456,,-0.09,-0.0711,,-0.0773,-0.136,-0.0442,,,-0.0609,-0.095973783,-0.046019836,0.0099,-0.1019,-0.0141,-0.0456,-0.0588,-0.0376,-0.0327,-0.0678,-0.0406
+2002-11-30,-0.0069,-0.0171,-0.0352,0.0242593,-0.0114,,-0.0364,-0.0549,,-0.0508,0.053,-0.0043,,,-0.0188,-0.046090109,-0.021584735,-0.035,-0.018,-0.014,-0.0285,-0.0554,-0.0164,-0.0225,-0.0541,-0.0257
+2002-12-31,0.1416,0.0658,0.0694,0.0810749,0.0328,,0.069,0.075,,0.078,0.201,0.0111,,,0.0837,0.104234528,0.103974663,0.1692,0.1841,0.0552,0.0473,0.148,0.0489,0.0472,0.0935,0.0576
+2003-01-31,0.0595,0.0518,0.0399,0.1011867,0.072,,0.0779,0.0418,,0.132,-0.006,0.0716,,,0.0802,0.069321534,0.051773364,-0.0184,0.2418,-0.005,0.0299,0.0978,0.0441,0.0495,0.0764,0.0522
+2003-02-28,0.1195,0.0403,0.0637,0.0779875,0.0746,,0.0516,0.0444,,0.0722,-0.022,0.0521,,,0.0921,0.08091954,0.086873445,0.0616,0.1318,0.0091,0.0399,0.0556,0.0402,0.0472,0.0741,0.0561
+2003-03-31,-0.108,-0.0504,-0.0824,-0.0950606,-0.0434,,-0.0363,-0.0297,,-0.1283,-0.031,-0.0748,,,-0.0619,-0.082730753,-0.083752593,0.0093,-0.0473,0.0082,-0.0452,-0.0556,-0.0445,-0.0552,-0.0912,-0.058
+2003-04-30,0.0245,0.0377,0.0011,0.053471,0.0269,,0.0127,-0.016,,0.0145,-0.032,0.0505,,,0.0208,-0.009738001,-0.000186325,-0.079,0.0202,-0.0362,0.0418,0.0014,0.0065,0.0165,0.0076,0.0161
[TRUNCATED]
To get the complete diff run:
svnlook diff /svnroot/returnanalytics -r 3560
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