sample_script.R

# Install and activate these 4 packages in RStudio
lapply(c("quantmod",
         "PortfolioAnalytics",
         "timeSeries",
         "fBasics"
         ),
       require,
       character.only = TRUE
       )

# Then, select tickers of stocks you want to get data of
tickers <- c("1801.HK",
             "6618.HK",
             "1093.HK",
             "1177.HK",
             "2269.HK"
             )
             
# Set up any start date you need  
start_date <- "2017-10-17"

# Here you can set up time period that you would like to analyse
portfolioPrices <- NULL
for (Ticker in tickers) 
  portfolioPrices <- cbind(portfolioPrices,
                           getSymbols(Ticker,
                                      src = "yahoo",
                                      auto.assign=FALSE
                                      )[,4]
                           )
portfolioPrices <- portfolioPrices[apply(portfolioPrices,
                                         1,
                                         function(x) all(!is.na(x))
                                         ),
                                   ]
colnames(portfolioPrices) <- tickers
portfolioReturns <- ROC(portfolioPrices,
                        type = "discrete")
portfolioReturns <-as.timeSeries(portfolioPrices)

# Get first values of stock prices
head(portfolioReturns)

# Of course you want to get returns
lrtn=diff(log(portfolioReturns)
          )
lrtn <- lrtn[-1,]

# Check values
head(lrtn)

# To calculate basic stats in one matrix
stats1 <- t(apply(lrtn,
                  2,
                  function(lrtn) c(mean(lrtn),
                                   quantile(lrtn,
                                            na.rm = T,
                                            probs = c(0.25)
                                            ),
                                   median(lrtn),
                                   quantile(lrtn,
                                            na.rm = T,
                                            probs = c(0.75)
                                            ),
                                            sd(lrtn),
                                            skewness(lrtn),
                                            kurtosis(lrtn)
                                   )
                  )
            )
colnames(stats1) <- c("Mean",
                      "25%",
                      "50%",
                      "75%",
                      "SD",
                      "Skewness",
                      "Kurtosis")
stats1   

# To calculate Sharpe Ratio
t(SharpeRatio(lrtn,
              Rf = 0.05,
              p = 0.95
              )
  )

# If you would like to know correlation between stocks
cor(lrtn)

# Boxplot
boxPlot(lrtn,
        main = "Hong Kong Stocks",
        title = FALSE,
        xlab = "Source: Yahoo Finance",
        ylab = "Returns"
        )

# For those who need returns' histogram with normal distribution 
pdf("myfile.pdf")
histPlot(lrtn)
dev.off()