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Here are criteria # 4 and 5.
"Opportunity is missed by most people because it is dressed in
overalls and looks like work." -Thomas Edison
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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