> The output of this test is a test statistic (t-statistic) and an associated p-value. The t-statistic, also known as the score, is the result of the unit-root test on the residuals. A more negative t-statistic suggests that the residuals are more likely to be stationary. The p-value provides a measure of the probability that the null hypothesis of the test (no cointegration) is true. The results of your test yielded a p-value of approximately 0.0149 and a t-statistic of -3.7684.
I think they used an LLM to write this bit.
It's also a really weird example. They look at correlation of once-a-day close prices over five years, and then write code to calculate the spread with 65 microsecond latency. That doesn't actually make any sense as something to do. And you wouldn't be calculating statistics on the spread in your inner loop. And 65 microseconds is far too slow for an inner loop. I suppose the point is just to exercise some optimisation techniques - but this is a rather unrepresentative thing to optimise!
> The output of this test is a test statistic (t-statistic) and an associated p-value. The t-statistic, also known as the score, is the result of the unit-root test on the residuals. A more negative t-statistic suggests that the residuals are more likely to be stationary. The p-value provides a measure of the probability that the null hypothesis of the test (no cointegration) is true. The results of your test yielded a p-value of approximately 0.0149 and a t-statistic of -3.7684.
I think they used an LLM to write this bit.
It's also a really weird example. They look at correlation of once-a-day close prices over five years, and then write code to calculate the spread with 65 microsecond latency. That doesn't actually make any sense as something to do. And you wouldn't be calculating statistics on the spread in your inner loop. And 65 microseconds is far too slow for an inner loop. I suppose the point is just to exercise some optimisation techniques - but this is a rather unrepresentative thing to optimise!