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Statistical confidence increases with the number of observations of the variable under observation, which is statistically relevant data, to which I am referring. I am not advocating adding in data re. hemlines etc.

So let us get a little less abstract and more specific.

Take Mr Skate's various systems: these have been designed with the 'exit' as the (or one of the prime) variables. The premise being: money is made on the exit.

Now, return to last year when the markets crashed. Did the exits save Mr Skate? To a point, yes, he still exited the market holding profits. But essentially the profits were decimated.

How did other mechanical systems hold up last year? They didn't.

Now they were backtested, and all gave the illusion of having a max drawdown of 'X'. They were all wrong. They were all wrong because of the very issue highlighted.

If, they had 'recognised' the signs as they were building, they would have exited much earlier. That is the value of studying, backtesting, whatever, the outliers that we have had far more frequently than the relevant statistical data has forecast in terms of probabilities.

jog on

duc