###########NOT INVESTMENT ADVICE###################### | |
#extend the trend following factors into a system for trading S&P 500 | |
#Hsieh, David A. and Fung, William, | |
#The Risk in Hedge Fund Strategies: Theory and Evidence from Trend Followers. | |
#The Review of Financial Studies, Vol. 14, No. 2, Summer 2001 . | |
#Available at SSRN: http://ssrn.com/abstract=250542 | |
#http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls | |
require(gdata) | |
require(quantmod) | |
require(PerformanceAnalytics) | |
require(FactorAnalytics) | |
URL <- "http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls" | |
#get xls sheet TF-Fac starting at the row with yyyymm | |
hsieh_factor <- read.xls(URL,sheet="TF-Fac",pattern="yyyymm",stringsAsFactors=FALSE) | |
hsieh_factor.clean <- hsieh_factor | |
#clean up date to get to yyyy-mm-dd | |
hsieh_factor.clean[,1] <- as.Date(paste(substr(hsieh_factor[,1],1,4), | |
substr(hsieh_factor[,1],5,6), | |
"01",sep="-")) | |
#remove percent sign and make numeric | |
hsieh_factor.clean[,2:6] <- apply( | |
apply(hsieh_factor[,2:6], | |
MARGIN=2, | |
FUN=function(x) {gsub("%", "", x)}), | |
MARGIN=2, | |
as.numeric)/100 | |
#get rid of NAs | |
hsieh_factor.clean <- hsieh_factor.clean[,1:6] | |
hsieh_factor.xts <- as.xts(hsieh_factor.clean[,2:6],order.by=hsieh_factor.clean[,1]) | |
chart.CumReturns(hsieh_factor.xts, | |
main="Hsieh and Fung Trend Following Factors", | |
xlab=NA, | |
legend.loc="topleft") | |
mtext(text="Source: http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls", | |
side=3,adj=0.10,outer=TRUE, col="purple",cex=0.75,line=-4) | |
chart.Correlation(hsieh_factor.xts,main="Hsieh and Fung Trend Following Factors") | |
mtext(text="Source: http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls", | |
side=1,adj=0.10,outer=TRUE, col="purple",cex=0.75,line=-1.5) | |
#get edhec data for sample factor analysis | |
data(edhec) | |
cta <- edhec[,1] | |
index(cta)=as.Date(format(index(cta),"%Y-%m-01")) | |
cta.factors <- na.omit(merge(cta,hsieh_factor.xts)) | |
chart.RollingStyle(cta.factors[,1],cta.factors[,2:NCOL(cta.factors)], | |
width=36, | |
colorset=c("darkseagreen1","darkseagreen3","darkseagreen4","slateblue1","slateblue3","slateblue4"), | |
main="Edhec CTA by Trend Following Factors Rolling 36 Months") | |
mtext(text="Source: http://faculty.fuqua.duke.edu/~dah7/DataLibrary/TF-Fac.xls", | |
side=1,adj=0.10,outer=TRUE, col="purple",cex=0.75,line=-5) | |
#in one line get SP500 data, convert to monthly, and get 1-month rate of change | |
GSPC.roc <- ROC(to.monthly(get(getSymbols("^GSPC",from="1900-01-01")))[,4],n=1,type="discrete") | |
colnames(GSPC.roc) <- "SP500" | |
#convert date to yyyy-mm-01 so we can merge properly | |
index(GSPC.roc) <- as.Date(index(GSPC.roc)) | |
#merge factor data with ROC data | |
roc.factors <- na.omit(merge(GSPC.roc,hsieh_factor.xts)) | |
#graph 6 month rolling correlation | |
chart.RollingCorrelation(roc.factors[,2:NCOL(roc.factors)],roc.factors[,1],n=6, | |
legend.loc="topleft",main="Correlation (Rolling 6-month)") | |
chart.RollingCorrelation(roc.factors[,6],roc.factors[,1],n=6, | |
legend.loc="topleft",main="PTFSSTK (Stock) Correlation (Rolling 6-month)") | |
abline(h=-0.6,col="red") | |
abline(h=0.5,col="green") | |
#get rolling 6 month correlation versus all the factors | |
correl <- as.xts( | |
apply(roc.factors[,2:NCOL(roc.factors)],MARGIN=2,runCor,y=roc.factors[,1],n=6), | |
order.by=index(roc.factors)) | |
#do simple system where long if correlation with stock trend following factor is low | |
#as defined by a band of -0.6 and 0.5 | |
system <- lag(ifelse(correl[,5] > -0.6 & correl[,5] < 0.5,1,0)) * GSPC.roc | |
#see how it works | |
charts.PerformanceSummary(merge(system,GSPC.roc)) |
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