我正在尝试运行下面的脚本并且它一直在失败。
###### rm(list=ls())
library(stockPortfolio) # Base package for retrieving returns
library(ggplot2) # Used to graph efficient frontier
library(reshape2) # Used to melt the data
library(quadprog) #Needed for solve.QP
# Create the portfolio using ETFs, incl. hypothetical non-efficient allocation
stocks <- c ("VCRA",
"AFFX",
"SGI",
"SAFM",
"SCAI",
"LINC",
"OGS",
"HIIQ",
"FLIR",
"MGEE",
"INTT",
"IPCI",
"DMRC",
"SBGL",
"UNTY",
"AEP",
"NAME",
"ED",
"WEC",
"MMYT",
"AWK",
"DRD",
"ISRG",
"CHUY",
"EDE",
"CHT",
"BGFV",
"VPCOU",
"NJR",
"FC",
"ROVI",
"SO",
"BXLT",
"NATH",
"VRNS",
"XEL",
"MBTF",
"MJCO",
"CMS",
"DLR",
"O",
"ADTN",
"SSS",
"SLP",
"PBY",
"NI",
"ORBC",
"CPB",
"OCLR",
"TLP",
"PROV",
"NWN",
"LNT",
"NTLS",
"PPC",
"NTT",
"WBMD",
"PLCE",
"NEE",
"EE",
"PMBC",
"PACB",
"AVA",
"IESC",
"HOFT",
"QSII",
"LPTH",
"INFY",
"DYAX",
"CPK",
"MMAC",
"CBNJ",
"IDSY",
"ONE",
"ITC",
"HLI",
"VHC",
"CTWS",
"SMBC",
"EQIX",
"LOCO",
"LEI",
"PNM",
"CYBE",
"PSA",
"YOKU",
"BDBD",
"ADMS",
"GMCR",
"DWA",
"LBMH",
"SCG",
"KMB",
"POR",
"ARG",
"ETR",
"WGL",
"CRAY",
"ES")
# Retrieve returns, from earliest start date possible (where all stocks have
# data) through most recent date
returns <- getReturns(stocks, freq="day", start = "2015-02-01", end = "2015-07-30") #Currently, drop index
#### Efficient Frontier function ####
eff.frontier <- function (returns, short="yes", max.allocation=NULL,
risk.premium.up=2.95, risk.increment=.1){
# return argument should be a m x n matrix with one column per security
# short argument is whether short-selling is allowed; default is no (short
# selling prohibited)max.allocation is the maximum % allowed for any one
# security (reduces concentration) risk.premium.up is the upper limit of the
# risk premium modeled (see for loop below) and risk.increment is the
# increment (by) value used in the for loop
covariance <- cov(returns)
print(covariance)
n <- ncol(covariance)
# Create initial Amat and bvec assuming only equality constraint
# (short-selling is allowed, no allocation constraints)
Amat <- matrix (1, nrow=n)
bvec <- 1
meq <- 1
# Then modify the Amat and bvec if short-selling is prohibited
if(short=="no"){
Amat <- cbind(1, diag(n))
bvec <- c(bvec, rep(0, n))
}
# And modify Amat and bvec if a max allocation (concentration) is specified
if(!is.null(max.allocation)){
if(max.allocation > 1 | max.allocation <0){
stop("max.allocation must be greater than 0 and less than 1")
}
if(max.allocation * n < 1){
stop("Need to set max.allocation higher; not enough assets to add to 1")
}
Amat <- cbind(Amat, -diag(n))
bvec <- c(bvec, rep(-max.allocation, n))
}
# Calculate the number of loops
loops <- risk.premium.up / risk.increment + 1
loop <- 1
# Initialize a matrix to contain allocation and statistics
# This is not necessary, but speeds up processing and uses less memory
eff <- matrix(nrow=loops, ncol=n+3)
# Now I need to give the matrix column names
colnames(eff) <- c(colnames(returns), "Std.Dev", "Exp.Return", "sharpe")
# Loop through the quadratic program solver
for (i in seq(from=0, to=risk.premium.up, by=risk.increment)){
dvec <- colMeans(returns) * i # This moves the solution along the EF
sol <- solve.QP(covariance, dvec=dvec, Amat=Amat, bvec=bvec, meq=meq)
eff[loop,"Std.Dev"] <- sqrt(sum(sol$solution*colSums((covariance*sol$solution))))
eff[loop,"Exp.Return"] <- as.numeric(sol$solution %*% colMeans(returns))
eff[loop,"sharpe"] <- eff[loop,"Exp.Return"] / eff[loop,"Std.Dev"]
eff[loop,1:n] <- sol$solution
loop <- loop+1
}
return(as.data.frame(eff))
}
# Run the eff.frontier function based on no short and 50% alloc. restrictions
eff <- eff.frontier(returns=returns$R, short="no", max.allocation=.50,
risk.premium.up=2.95, risk.increment=.1)
# Find the optimal portfolio
eff.optimal.point <- eff[eff$sharpe==max(eff$sharpe),]
# graph efficient frontier
# Start with color scheme
ealred <- "#7D110C"
ealtan <- "#CDC4B6"
eallighttan <- "#F7F6F0"
ealdark <- "#423C30"
ggplot(eff, aes(x=Std.Dev, y=Exp.Return)) + geom_point(alpha=.1, color=ealdark) +
geom_point(data=eff.optimal.point, aes(x=Std.Dev, y=Exp.Return, label=sharpe),
color=ealred, size=5) +
annotate(geom="text", x=eff.optimal.point$Std.Dev,
y=eff.optimal.point$Exp.Return,
label=paste("Risk: ",
round(eff.optimal.point$Std.Dev*100, digits=3),"\nReturn: ",
round(eff.optimal.point$Exp.Return*100, digits=4),"%\nSharpe: ",
round(eff.optimal.point$sharpe*100, digits=2), "%", sep=""),
hjust=0, vjust=1.2) +
ggtitle("Efficient Frontier\nand Optimal Portfolio") +
labs(x="Risk (standard deviation of portfolio)", y="Return") +
theme(panel.background=element_rect(fill=eallighttan),
text=element_text(color=ealdark),
plot.title=element_text(size=24, color=ealred))
ggsave("Efficient Frontier.png")
transposed_object<-as.data.frame(t(eff.optimal.point))
colnames(transposed_object)<- c("stat")
subset(transposed_object, transposed_object $stat>0.05)
问题是，这些股票中的一个或多个在我看的期间没有价格，这会引发错误，因为没有价格=没有回报。如何修改脚本以打印任何没有价格的股票代码，所以我可以从列表中删除任何/所有，然后重新运行脚本并让它第二次运行？
感谢所有人。
答案 0 :(得分：0)
returns <- if( prices = returns) { getReturns(stocks, freq="day", start = "2015-02-01", end = "2015-07-30") } else (no prices = no returns) {add current date};
returns<- returns[-currentdate, ];