#From Session you can choose Set Working Directory
#Alternatively you can use setwd to set the working directory
#Pay close attention to the direction of the slash "/"
#The first time through you will need to install.packages(" ") listed in the library statements
#R-code for Assignment
#setwd("~/Desktop/FINA")
library(timeSeries)
library(fPortfolio)
library(quantmod)
library(caTools)
library(dplyr)
library(PerformanceAnalytics)
library(ggplot2)
library(tidyverse)
library(tidyquant)
library(timetk)
library(lubridate)
#If your data is in long format (i.e., a panel), the code below reads in the returns
#I use the "ymd" function from the lubridate package to convert the date stored as an integer to
#a date that R will recognize. I then use the SPREAD function
#to reshape the data so that each firm's returns are in a column
#You can directly reference a variable in a file as filename$VariableName
returns<-read.csv("1589309519_StockReturns.csv")
returns$Date <- ymd(returns$Date)
#this code will convert a panel to a set of columns
#the %>% is handy and tells R to take the file retuns and passes it to the SPREAD function
#the %>% is called a "pipe" is comes with the package "dplyr" and "tidyverse". Tidyverse gives
#us a whole array of functions for data manipulation
#returns <- returns %>% spread(TICKER, RET )
#If the returns are in a column you can use the below code to read in the .csv fle
returns<-read.csv("1589309519_StockReturns.csv")
returns$Date <- ymd(returns$Date)
returns = subset(returns, Date < "2015-01-01")
#The returns[,-1] reads all rows and skips the first column, and then we tell it which variable is the Date
returns<-timeSeries(returns[,-1],charvec=returns$Date)
#portfolioFrontier is a package thqt will generate the entire efficient set
#The output of this package is stored in an object call effFrontier
#Set Portfolio specifications - in this case the risk-free rate
tgSpec<-portfolioSpec()
setRiskFreeRate(tgSpec)<- 0.02/12
effFrontier <- portfolioFrontier(returns, spec= tgSpec, constraints = "LongOnly")
# plot frontier - simple and interactive
#'Options
#'1: Plot Efficient Frontier
#'2: Plot Minimum Variance Portfolio
#'3: Plot Tangency Portfolio
#'4: Plot Risk Returns of Each Asset
#'5: Plot Equal Weights Portfolio
#'6: Plot Two Asset Frontiers (Long)
#'7: Plot Monte Carlo Portfolios
#'8: Plot Sharpe Ratio
plot(effFrontier,c(1,2,3,4,6))
#tailored Plot much more flexible version of plot
tailoredFrontierPlot(object=effFrontier,sharpeRatio = FALSE)
#Plot Frontier Weights (Can Adjust Number of Points)
frontierWeights <- getWeights(effFrontier) # get allocations for each instrument for each point on the efficient frontier
tickers <- colnames(frontierWeights)
risk_return <- frontierPoints(effFrontier)
write.csv(risk_return, "risk_return.csv")
write.csv(frontierWeights,"frontier_weights.csv")
#Output Correlation
cor_matrix <- cor(returns)
cov_matrix <- cov(returns)
write.csv(cov_matrix, "covmatrix.csv")
write.csv(cor_matrix,"corr_matrix.csv")
cov_matrix
#Annualize Data
riskReturnPoints <- frontierPoints(effFrontier) # get risk and return values for points on the efficient frontier
annualizedPoints <- data.frame(targetRisk=riskReturnPoints[, "targetRisk"] * sqrt(12),
targetReturn=riskReturnPoints[,"targetReturn"] * 12)
plot(annualizedPoints)
# plot Sharpe ratios for each point on the efficient frontier
riskFreeRate <- 0.02
plot((annualizedPoints[,"targetReturn"]-riskFreeRate) / annualizedPoints[,"targetRisk"], xlab="point on efficient frontier", ylab="Sharpe ratio")
#Plot Frontier Weights (Need to transpose matrix first)
barplot(t(frontierWeights), main="Frontier Weights", col=cm.colors(ncol(frontierWeights)+2), legend=colnames(frontierWeights))
#Get Minimum Variance Port, Tangency Port, etc.
mvp <- minvariancePortfolio(returns, spec=portfolioSpec(), constraints="LongOnly")
mvp
tangencyPort <- tangencyPortfolio(returns, spec=portfolioSpec(), constraints="LongOnly")
tangencyPort
mvpweights <- getWeights(mvp)
tangencyweights <- getWeights(tangencyPort)
#Extract value at risk
covRisk(returns, mvpweights)
varRisk(returns, mvpweights, alpha = 0.05)
cvarRisk(returns, mvpweights, alpha = 0.05)
#Plot MVP Weights: Basic Graphs
barplot(mvpweights, main="Minimum Variance Portfolio Weights", xlab="Assset", ylab="Weight In Portfolio (%)", col=cm.colors(ncol(frontierWeights)+2), legend=colnames(weights))
pie(mvpweights, col=cm.colors(ncol(frontierWeights)+2))
#ggplot MVP Weights
df <- data.frame(mvpweights)
assets <- colnames(frontierWeights)
ggplot(data=df, aes(x=assets, y=mvpweights, fill=assets)) +
geom_bar(stat="identity", position=position_dodge(),colour="black") +
geom_text(aes(label=sprintf("%.02f %%",mvpweights*100)),
position=position_dodge(width=0.9), vjust=-0.25, check_overlap = TRUE) +
ggtitle("Minimum Variance Portfolio Optimal Weights")+ theme(plot.title = element_text(hjust = 0.5)) +
labs(x= "Assets", y = "Weight (%)")
dft <- data.frame(tangencyweights)
assets <- colnames(frontierWeights)
ggplot(data=dft, aes(x=assets, y=tangencyweights, fill=assets)) +
geom_bar(stat="identity", position=position_dodge(),colour="black") +
geom_text(aes(label=sprintf("%.02f %%",tangencyweights*100)),
position=position_dodge(width=0.9), vjust=-0.25, check_overlap = TRUE) +
ggtitle("Tangency Portfolio Weights")+ theme(plot.title = element_text(hjust = 0.5)) +
labs(x= "Assets", y = "Weight (%)")
#ggplot Pie
bar <- ggplot(df, aes(x = "", y = mvpweights, fill=assets)) + geom_bar(width= 1, stat="identity") + ggtitle("Minimum Variance Portfolio Weights")+ theme(plot.title = element_text(hjust = 0.5))
pie <- bar + coord_polar("y", start=0)
pie + scale_fill_brewer(palette="Blues")+
theme_minimal()
bar <- ggplot(dft, aes(x = "", y = tangencyweights, fill=assets)) + geom_bar(width= 1, stat="identity") + ggtitle("Tangency Portfolio Weights")+ theme(plot.title = element_text(hjust = 0.5))
pie <- bar + coord_polar("y", start=0)
pie + scale_fill_brewer(palette="Blues")
