Investment

fanplot_example.R

#--------------------------------------------------------------------------------------#
# 
# FANPLOT EXAMPLE
# 
# Made famous by the BoE inflation report, fanplots are good for showing distributions where 
# there is a time series. In this script I have created a toy dataset that looks like a dispersing time series
# to illustrate the fan syntax and .
# In future posts I'll show how to build a time-series using arbitrary data for an equity share 
#
# D Menezes 20161111
#--------------------------------------------------------------------------------------#

#Step 1: Input constants that will be used below
pkg.list<-c("fanplot","RColorBrewer")    

#Step 2: Install packages and load:

for(i in seq_along(pkg.list)) {
  if(!require(pkg.list[i], character.only=TRUE)) {
    install.packages(pkg.list[i])
    library(pkg.list[i], character.only=TRUE)
  }
}

#Step 3: Simulate some toy data
t<-1:20 #20 time steps
trend<-0.1 #increasing return
vol<-0.2 #volatility at t = 0
mu<-trend*t #on average the investment return will increase linearly
stddev<-vol*sqrt(t) #on average, the volatility parameter will increase at a slower rate - cf http://www.macroption.com/why-is-volatility-proportional-to-square-root-of-time/
n.sims<-10000 #no of different realisations of the future

initial.data<-c(0.06,0.08,0.04,0.08,0.1) #data prior to the fan - will need this later

#create an empty matrix for results
output.sims<-matrix(NA,nrow=n.sims,ncol=length(t))

#fill the matrix
for(i in seq_along(t)) { 
  output.sims[,i]<- rnorm(n=n.sims, mean=mu[i], sd = stddev[i])
}
head(output.sims)

#check - a vs e so that we're comfortable our simulation is doing what we want:
mean.check<-cbind(mu,
                  apply(output.sims,2,mean) 
)

vol.check<-cbind(stddev,
                 apply(output.sims,2,sd) 
)

colnames(mean.check)<-c("expected/input","actual/output")
colnames(vol.check)<-c("expected/input","actual/output")

mean.check
vol.check

#results are ok.

#plot - box plot to check - only used when testing the code
#boxplot(output.sims)

# plot initial values

plot(initial.data, type = "l", lwd = 2, 
     xlim = c(0,25), ylim = range(output.sims)*0.85, 
     main="Range of investment returns",xlab="Years", ylab="%")

# add fan (note - you need an existing plot window open, this can even be an empty plot, but the syntax won't instantiate the plot window, slightly odd
fan(data=output.sims,start=6,rlab=c(0.2,0.5,0.8),anchor = initial.data[length(initial.data)]) 

#6 to only tag on the distribution from that point
#anchor so that things fan out from a point, rather than immediately showing the 1st yr spread
#rlab are the selected percentiles


#Other color options avaialble - see here and examples below:
#http://www.datavis.ca/sasmac/brewerpal.html

#example 1
fan(data=output.sims,start=6,rlab=c(0.2,0.5,0.8),anchor = initial.data[length(initial.data)], 
    fan.col = colorRampPalette(colors = brewer.pal(11,"Spectral"))) 

#example 2
fan(data=output.sims,start=6,rlab=c(0.2,0.5,0.8),anchor = initial.data[length(initial.data)], 
    fan.col = colorRampPalette(colors = brewer.pal(9,"RdYlBu"))) 
#example 3
fan(data=output.sims,start=6,rlab=c(0.2,0.5,0.8),anchor = initial.data[length(initial.data)], 
    fan.col = colorRampPalette(colors = brewer.pal(9,"OrYlBu"))) #this one doesn't work.  the no 9, is how many colors in the palette