About forecast scalars:
I am using artificial random data to estimate the scalars and correlations between rules.
For example, I created 1000 different random walks, each 100000 bars('days') long and applied trading rules to them:
Code:count mean std min 25% 50% 75% max scalar_ewmac256 1000 1.628219 0.070967 1.420118 1.581897 1.626521 1.678534 1.827633 scalar_ewmac128 1000 2.301430 0.071873 2.092129 2.253581 2.300945 2.349642 2.524300 scalar_ewmac64 1000 3.262073 0.073323 3.039892 3.211492 3.261055 3.313205 3.511254 scalar_ewmac32 1000 4.639525 0.075807 4.417496 4.589838 4.636334 4.694308 4.875523
The 50% value is the median along the 1000 tries (data is from the pandas describe() function).
ewmac256 means the EWMA 256/64 rule and so on.
The medians are lower than the values that you give in your book (1.87, 2.65, 3.75, 5.3), probably reflecting the fact that the artificial data has no trends.
But even with 1000 tries of 100000 days, i.e. about 400 years, you can see the error in the values is still significant.
I wonder if it is really possible to get stable values from real data where we only have maybe 40 futures and 50 years of data.
You're right - it isn't possible. Great experiment by the way.
I see a lot of people getting really tied up about the 'correct' value of these things. But it doesn't matter as much as people think. For example if you apply random noise to all your forecasts scalars, such that they're only right within an order of magnitude, what effect would it have on your Sharpe Ratio? Not as much as you might expect.
GAT
