#################################
### R commands used in Lecture 1:
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"<==" denotes explanation of the command.

## Install R (or RStudio) on your computer ##
## double click on R-icon to start R ##

### Make sure that you have internet connection before using the package "quantmod".

library(quantmod) or require(quantmod)  <=== load the package "quantmod"
getSymbols("AAPL") <=== get daily Apple stock data from Yahoo Finance. The default starting date is 2007-01-03.
dim(AAPL)          <=== find the size of the data downloaded
head(AAPL)         <=== show the first 6 rows of data
tail(AAPL)         <=== show the last 6 rows of data
chartSeries(AAPL)  <=== plot Apple daily closing stock prices with trading volume
       <== Daily closing prices do not adjusted for stock split. You can use adjusted closing price.
chartSeries(AAPL[,6]) <== Column 6 of the object "AAPL" in R.
chartSeries(AAPL[,6],theme="white")  <== Same as the previous command, but use "white" background for the plot.

getSymbols("^VIX") <== load daily VIX index
getSymbols("^TNX") <== load 10-year interest rate
getSymbols("^GSPC") <== load S&P 500 index
getSymbols("^IXIC") <== load NASDAQ index

getSymbols("AAPL",from="2005-01-03",to="2013-12-31")  <== specify the data span

getSymbols("UNRATE",src="FRED") <== Load U.S. monthly unemplyment rate from Federal Reserve Bank of St Louis.
                     <== src stands for "source", FRED stands for Federal Reserve Economic Data.
chartSeries(UNRATE) <== plot the U.S. monthly unemployment rate
getSymbols("DEXUSEU",src="FRED") <== Load Dollar verus Euro daily exchange rates from FRED.
chartSeries(DEXUSEU) <== plot the daily dollar-euro exchange rates.
getSymbols("DEXJPUS", src="FRED") <== Load Japanese Yen and US Dollar exchange rates from FRED.


### You can also use the following commands to load data from FRED.
getSymbols.FRED("UNRATE",env=globalenv())

### You should use your working directory ###
library(fBasics)  or require(fBasics) <== Load the package "fBasics"
da=read.table("m-ibm6708.txt",header=T)  <== Load data with header into R
    <== header=T means the data file contains "names" for each column.
    <== use header=F, if the data file contains no column names.
dim(da)  <== Check dimension of the data (row = sample size, col = variables)
head(da)   <== Print out the first 6 rows of the data object "da".
tail(da) <== Print out the last 6 rows of the data object "da".
ibm=da[,2]  or ibm=da$ibm <== Select the simple returns of IBM stock stored in Column 2.

plot(ibm,type='l')  <== Plot the simple returns. Note that type is "ell" not 1.
basicStats(ibm)     <== Compute the descriptive statistics of simple returns.
libm=log(ibm+1)  <== Compute the IBM log returns
basicStats(libm)  <== Compute descriptive statistics of log returns.

t.test(ibm)   <== Perform t-test for mean being zero.
t.test(ibm,alternative=c("greater"))  <== Perform one-sided test (Not shown in class)

hist(ibm,nclass=40) <== Obtain histogram of IBM simple returns.
d1=density(libm)  <== Compute density function of ibm log returns
names(d1)   <== Find out the output variables of the command "density".
plot(d1$x,d1$y,type='l')   <== Plot the sample density of log returns
mu=mean(libm); s1 = sd(libm) <== compute the sample mean and standard deviation of IBM log returns.
x=seq(-0.4,0.4,0.01) <=== create a sequence of real numbers from -0.4 to 0.4 with increment 0.01.
y=dnorm(x,mean=mu,sd=s1) <=== obtain normal density with mean mu and standard deviation s1.
lines(x,y,lty=2) <== impose a dashed line on the density plot for comparison with the normal density.
  <== you can also use different colors in the plot. For example,
  lines(x,y,col="red") will plot a red curve.

da <- read.csv("d-vix0411.csv",header=T) ## read *.csv file

tt <- skewness(libm)/sqrt(6/length(libm))  ## Test H_0: m3 =0 versus H_a: m3 not equal = 0.
tt
pv <- 2*(1-pnorm(tt))  ### Compute p-value (assuming tt is positive)
pv

tt <- kurtosis(libm)/sqrt(24/length(libm)) ## Testing excess kurtosis being zero
tt

normalTest(libm,method="jb")   <== Perform normality test.


### Pearson correlation, Kendall's tau and Spearman's rho

x = rnorm(1000)  <== Generate 1000 N(0,1) random numbers
cor(x,x)   ### default, traditional Pearson correlation coefficient

cor(x,exp(x))
cor(x,exp(x),method="kendall")
cor(x,exp(x),method="spearman")

cor(x,exp(20*x))
cor(x,exp(20*x),method="kendall")
cor(x,exp(20*x),method="spearman")