advanced-algorithmic-trading

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created the difference series, we wish to plot the correlogram and then assess how close this is to
discrete white noise, see Figure 9.4.
> acf(diff(x))
Figure 9.4: Correlogram of the Difference Series from a Simulated Random Walk.
What can we notice from this plot? There is a statistically significant peak at k = 10, but only
marginally. Remember, that we expect to see at least 5% of the peaks be statistically significant,
simply due to sampling variation.
Hence we can reasonably state that the the correlogram looks like that of discrete white noise.
It implies that the random walk model is a good fit for our simulated data. This is exactly what
we should expect, since we simulated a random walk in the first place!
Fitting to Financial Data
Let us now apply our random walk model to some actual financial data. As with the Python
library Pandas we can use the R package quantmod to easily extract financial data from Yahoo
Finance.
We are going to see if a random walk model is a good fit for some equities data. In particular,
I am going to choose Microsoft (MSFT), but you can experiment with your favourite ticker
symbol.
Before we are able to download any of the data we must install quantmod since it is not part
of the default R installation. Run the following command and select the R package mirror server
that is closest to your location:
> install.packages(’quantmod’)