> While using 10-level deep data will give different backtesting results > (obviously), it should *not* be too much different if we apply the > right transformation. If all is well, having more samples for the > depth should just reduce the variability of the our estimate for > supply--demand, which 'should' translate into a reduced variability of > a trading strategy (although some strategies are hyper-sensitive to > even tiny fluctuations, but these ones are probably not worth trading > anyway). > > Right now, we track a normalized difference between the bid and ask > depths. If we have a depth of N, all we need to do is find the best > scaling factor that keeps our estimate approximately the same as a > function of N. This could be done empirically for a few days to find > an appropriate scaling. This would be impossible to check on our old > dataset, but I believe it should work well enough for using our old > dataset. > > If someone has the full recorded data for a few 10-deep days, we could > simply try this out; that is, plot the distributions of our > 'normalized difference' estimate as a function of N. If this doesn't > work, I suspect that our assumption of collapsing 2N numbers (from the > bid and ask) to a single number is not well justified ...
You are assuming that the difference between the 5-level deep balances and the 10-level deep balances is just variability which can be compensated by a scaling factor. My guess is that there is more to it. However, I agree that it would be useful to plot both side by side. I'll modify my code to record both 5-level deep and 10-level deep balances for a day and will post the results. --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "JBookTrader" group. To post to this group, send email to [email protected] To unsubscribe from this group, send email to [email protected] For more options, visit this group at http://groups.google.com/group/jbooktrader?hl=en -~----------~----~----~----~------~----~------~--~---
