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Mark, thanks a huge amount for moving this forward so quickly, yeoman work.
that said, uh, sorry I'm thick, but I'm pretty lost here. are we supposed to
be able to do something like this ourselves without your forthcoming
example, which I assume would be a spreadsheet? this is where we use
simulations to estimate the probability of profit and estimate future
drawdowns, right? that I got from your earlier really-quick summary, but how
that translates into stuff to do I don't get yet.
some specific things I don't understand:
- "simply set up a structure in the worksheet that corresponds to the
criterion and run the simulation"
- "randomly select 10 trades with replacement"
- "set up a spreadsheet structure that will calculate max dd from a string
of x trades, run 1000 simulations and graph the output"
what criterion? run what simulation? what's "replacement"? run 1000
simulations how?
I assume this will all get clearer with example in hand, which you've said
is coming soon. so really, I'm just checking to make sure I'm not missing
the boat completely. am I?
dave
> Here's where it starts to get labor intensive. We need to go back and
> find the best behaved issues with good PFs, with best behaved here
> meaning fairly actively traded *and* consistent on *both* % profit per
> trade and % profit per bar graphs. So run the system on a group of
> stocks, sort by # trades and go down the list. There are obviously
> many mathematical ways to rank order the consistency but it's better
> to eyeball each and every one if you intend to perform the criteria 4
> and 5 simulations.
>
> We'll be using something called the nonparametric bootstrap which is a
> name for resampling with replacement. Although it's an old idea, it
> hasn't been very well researched until fairly recently and even today,
> it's still fertile ground for much additional research. There's not
> surprisingly then a lot of controversy over when it is and isn't
> appropriate to apply it. There are also very few diagnostics for it
> unlike most other statistical procedures but these, frankly, are
> advantages because it's an incredibly powerful procedure if you apply
> it properly and sometimes that means doing your own research.
>
> We'll use an Excel based simulation package (with its own random
> number generator). Copy and paste the % profit/trade column (from to
> a well behaved issue traded with a robust system) into a worksheet.
> To perform the simulations for criteria 4 & 5, simply set up a
> structure in the worksheet that corresponds to the criterion and run
> the simulation. To *estimate* the probability of profit after 10
> trades for criterion 4, randomly select 10 trades with replacement,
> sum them and note the answer (the software will do this
> automatically). Then repeat 999 or 4999 or however many more more
> times. Usually a total of 1000 is sufficient. Take the output and
> display as a histogram and a cumulative distribution. To *estimate*
> future max dd for criterion 5, set up a spreadsheet structure that
> will calculate max dd from a string of x trades, run 1000 simulations
> and graph the output. Standards for robustness on criteria # 4 and 5
> -- whatever you feel comfortable with. Mine are based on my
> benchmark system and are proprietary.
>
> Cautions: Your past data must be representative of how the system
> will perform in the future, however, simulating with output from
> robust systems traded on well behaved issues is a bet worth taking.
> But even if your data *are* representative, this still treats trades
> as if they're independent and they're not necessarily. This also
> cannot account for single extreme events that haven't happened yet
> (like price shocks). There are a few more esoteric concerns but all
> except price shocks are reasonably well mitigated by applying my
> proprietary adjustment. Actually, I have several and am willing to
> share two that work well *but offline*, just for the asking, to those
> interested enough to actually buy simulation software to explore this.
> Or to the core members of the team that pursues the robustness
> challenge if there ever is one.
>
> I consider this to be keeping my promise but will still try to post an
> example tonight, tomorrow morning at the latest.
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