advanced-algorithmic-trading

172
set.seed(123)
## SIMULATED DATA
## Create a simulated random walk
z <- rep(0, 1000)
for (i in 2:1000) z[i] <- z[i-1] + rnorm(1)
## Create two non-stationary series based on the
## simulated random walk
p <- q <- rep(0, 1000)
p <- 0.3*z + rnorm(1000)
q <- 0.6*z + rnorm(1000)
## Perform a linear regression against the two
## simulated series in order to assess the hedge ratio
comb <- lm(p~q)
## FINANCIAL DATA - EWA/EWC
## Obtain EWA and EWC for dates corresponding to Chan (2013)
getSymbols("EWA", from="2006-04-26", to="2012-04-09")
getSymbols("EWC", from="2006-04-26", to="2012-04-09")
## Utilise the backwards-adjusted closing prices
ewaAdj = unclass(EWA$EWA.Adjusted)
ewcAdj = unclass(EWC$EWC.Adjusted)
## Plot the ETF backward-adjusted closing prices
plot(ewaAdj, type="l", xlim=c(0, 1500), ylim=c(5.0, 35.0),
xlab="April 26th 2006 to April 9th 2012",
ylab="ETF Backward-Adjusted Price in USD", col="blue")
par(new=T)
plot(ewcAdj, type="l", xlim=c(0, 1500), ylim=c(5.0, 35.0),
axes=F, xlab="", ylab="", col="red")
par(new=F)
## Plot a scatter graph of the ETF adjusted prices
plot(ewaAdj, ewcAdj, xlab="EWA Backward-Adjusted Prices",
ylab="EWC Backward-Adjusted Prices")
## Carry out linear regressions twice, swapping the dependent
## and independent variables each time, with zero drift
comb1 = lm(ewcAdj~ewaAdj)
comb2 = lm(ewaAdj~ewcAdj)