advanced-algorithmic-trading

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Once quantmod is installed we can use it to obtain the historical price of MSFT stock:
> require(’quantmod’)
> getSymbols(’MSFT’, src=’yahoo’)
> MSFT
..
..
2015-07-15 45.68 45.89 45.43 45.76 26482000 45.76000
2015-07-16 46.01 46.69 45.97 46.66 25894400 46.66000
2015-07-17 46.55 46.78 46.26 46.62 29262900 46.62000
This will create an object called MSFT (case sensitive) into the R namespace, which contains
the pricing and volume history of MSFT. We are interested in the corporate-action adjusted
closing price. We can use the following commands to (respectively) obtain the Open, High,
Low, Close, Volume and Adjusted Close prices for the Microsoft stock: Op(MSFT), Hi(MSFT),
Lo(MSFT), Cl(MSFT), Vo(MSFT), Ad(MSFT).
Our process will be to take the difference of the Adjusted Close values, omit any missing
values, and then run them through the autocorrelation function. When we plot the correlogram
we are looking for evidence of discrete white noise, that is, a residuals series that is serially
uncorrelated. To carry this out in R, we run the following command:
> acf(diff(Ad(MSFT)), na.action = na.omit)
The latter part (na.action = na.omit) tells the acf function to ignore missing values by
omitting them. The output of the acf function is given in Figure 9.5.
Figure 9.5: Correlogram of the Difference Series from MSFT Adjusted Close.
We notice that the majority of the lag peaks do not differ from zero at the 5% level. However