> Eugene, your comment goes to the need to have sufficiently large backtest
> database relative to the number of adjustable parameters, so that the results
> are statistically significant. How does that relate to potential
> non-stationarity of parameters? 

The non-stationarity of parameters is a problem, indeed. However, some
things are more or less absolute. Think of the supply/demand
relationship. If you can capture its essence in the strategy, that
should work today, tomorrow, and 10 years in the future. Now, I do
acknowledge that a better model would be the one which not only
accounts for the supply/demand, but also for its changing elasticity
over time. However, such model would be more complex, more difficult
to understand, and more time-consuming to test. Perhaps more
importantly, while the supply/demand law by itself is quite intuitive,
the manner in which its parameters change overtime is not intuitive at
all. The best we can hope for in our walk-forward optimization is that
whatever parameters were the "optimal" in a recent period would still
be the optimal in the next period. For the sake of this hope, we would
be required to significantly shorten our optimization periods, thus
incurring a penalty of standard error in our confidence bands.

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