#Read data for 10 stocks:
a1 <- read.table("AMZN.csv", sep=",", header=TRUE)
a2 <- read.table("COST.csv", sep=",", header=TRUE)
a3 <- read.table("CVX.csv", sep=",", header=TRUE)
a4 <- read.table("DIS.csv", sep=",", header=TRUE)
a5 <- read.table("GILD.csv", sep=",", header=TRUE)
a6 <- read.table("GS.csv", sep=",", header=TRUE)
a7 <- read.table("LMT.csv", sep=",", header=TRUE)
a8 <- read.table("MMM.csv", sep=",", header=TRUE)
a9 <- read.table("PG.csv", sep=",", header=TRUE)
a10 <- read.table("TSLA.csv", sep=",", header=TRUE)

a11 <- read.table("^GSPC.csv", sep=",", header=TRUE)

cov_matrix <- cov(a)

#Convert adjusted close proces into returns:
r1 <- (a1$Adj.Close[-1]-a1$Adj.Close[-nrow(a1)])/a1$Adj.Close[-nrow(a1)]

r2 <- (a2$Adj.Close[-1]-a2$Adj.Close[-nrow(a2)])/a2$Adj.Close[-nrow(a2)]

r3 <- (a3$Adj.Close[-1]-a3$Adj.Close[-nrow(a3)])/a3$Adj.Close[-nrow(a3)]

r4 <- (a4$Adj.Close[-1]-a4$Adj.Close[-nrow(a4)])/a4$Adj.Close[-nrow(a4)]

r5 <- (a5$Adj.Close[-1]-a5$Adj.Close[-nrow(a5)])/a5$Adj.Close[-nrow(a5)]

r6 <- (a6$Adj.Close[-1]-a6$Adj.Close[-nrow(a6)])/a6$Adj.Close[-nrow(a6)]

r7 <- (a7$Adj.Close[-1]-a7$Adj.Close[-nrow(a7)])/a7$Adj.Close[-nrow(a7)]

r8 <- (a8$Adj.Close[-1]-a8$Adj.Close[-nrow(a8)])/a8$Adj.Close[-nrow(a8)]

r9 <- (a9$Adj.Close[-1]-a9$Adj.Close[-nrow(a9)])/a9$Adj.Close[-nrow(a9)]

r10 <- (a10$Adj.Close[-1]-a10$Adj.Close[-nrow(a10)])/a10$Adj.Close[-nrow(a10)]


r <- (a11$Adj.Close[-1]-a11$Adj.Close[-nrow(a11)])/a11$Adj.Close[-nrow(a11)]

rr <- as.data.frame(cbind(r1,r2,r3,r4,r5,r6,r7,r8,r9,r10))

#Compute the variance covariance matrixL:
cov_matrix <- cov(rr)

#Risk free asset:
rf <- 0.001

#Compute the mean returns for each stock:
means <- colMeans(rr)

#Compute R vector:
R <- means - rf

#Compute z vector:
z <- solve(cov_matrix) %*% R

#Finally compute the portfolio weights (point of tangency):
x <- z/sum(z)