advanced-algorithmic-trading

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> set.seed(1)
> x <- seq(1,100) + 20.0*rnorm(1:100)
> set.seed(2)
> y <- seq(1,100) + 20.0*rnorm(1:100)
> plot(x,y)
The plot is given in Figure 8.1.
Figure 8.1: Scatter plot of two linearly increasing variables with normally distributed noise.
There is a relatively clear association between the two variables. We can now calculate the
sample covariance:
> cov(x,y)
[1] 681.6859
The sample covariance is given as 681.6859.
One drawback of using the covariance to estimate linear association between two random
variables is that it is a dimensional measure. That is, it isn’t normalised by the spread of the
data and thus it is hard to draw comparisons between datasets with large differences in spread.
This motivates another concept, namely correlation.