advanced-algorithmic-trading

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## Plot the residuals of the first linear combination
plot(comb1$residuals, type="l",
xlab="April 26th 2006 to April 9th 2012",
ylab="Residuals of EWA and EWC regression")
## Now we perform the ADF test on the residuals,
## or "spread" of each model, using a single lag order
adf.test(comb1$residuals, k=1)
adf.test(comb2$residuals, k=1)
## FINANCIAL DATA - RDS-A/RDS-B
## Obtain RDS equities prices for a recent ten year period
getSymbols("RDS-A", from="2006-01-01", to="2015-12-31")
getSymbols("RDS-B", from="2006-01-01", to="2015-12-31")
## Avoid the hyphen in the name of each variable
RDSA <- get("RDS-A")
RDSB <- get("RDS-B")
## Utilise the backwards-adjusted closing prices
rdsaAdj = unclass(RDSA$"RDS-A.Adjusted")
rdsbAdj = unclass(RDSB$"RDS-B.Adjusted")
## Plot the ETF backward-adjusted closing prices
plot(rdsaAdj, type="l", xlim=c(0, 2517), ylim=c(25.0, 80.0),
xlab="January 1st 2006 to December 31st 2015",
ylab="RDS-A and RDS-B Backward-Adjusted Closing Price in GBP", col="blue")
par(new=T)
plot(rdsbAdj, type="l", xlim=c(0, 2517), ylim=c(25.0, 80.0),
axes=F, xlab="", ylab="", col="red")
par(new=F)
## Plot a scatter graph of the
## Royal Dutch Shell adjusted prices
plot(rdsaAdj, rdsbAdj, xlab="RDS-A Backward-Adjusted Prices",
ylab="RDS-B Backward-Adjusted Prices")
## Carry out linear regressions twice, swapping the dependent
## and independent variables each time, with zero drift
comb1 = lm(rdsaAdj~rdsbAdj)
comb2 = lm(rdsbAdj~rdsaAdj)
## Plot the residuals of the first linear combination