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I've been working on a new (to me) way of evaluating possible entry methods,
as distinct from exit methods. I've got code in the works, but I thought I'd
run the concept by folks here for some feedback while I finalize it. here's
the basic idea:
on the long side, profits come from selling at a higher price than you
bought. so, a good entry signal is one where the stock frequently rises
reasonably soon after a buy signal occurs. with the exception of a permanent
down-trend or individual stock fatalities, *everything* goes up eventually,
so the question really is, how soon after buys does price rise, and how far?
this exploration makes two assumptions, after Steve Karnish: 1) a relatively
tight stoploss is needed to limit risk and mental pain, and 2) we're looking
for short term signals, to generate a statistically meaningful number of
trades, and to limit the opportunity cost of continuing to hold a position
that's treading water. specifically, we'll sell anything that hits a 13%
stoploss or is still held after 15 days. these aren't the only possible
assumptions, but they're what's used here.
given those assumptions, the quality of an entry signal can be evaluated as
the peak percentage rise in price, after commissions, before one of those
two basic exit conditions occurs. to actually trade the signal profitably,
you might well need to exit before the loss or time stops took you out, and
the design and testing of an exit signal specific to a given entry model is
beyond the scope of this effort. however, unless price reaches a healthy
profit fairly often before those basic stop exits happen, the entry signal
itself isn't useful; you could do better with random entries.
peak price rise before those time or loss stops are hit can be evaluated for
any bar, not just when there's a buy signal. the efficiency of a buy signal
can then be expressed as the percentage ratio of the average peak rise on
buy bars to that of all bars. greater than 100% means it entered on bars
with higher than average gain before stopping out, less than 100%, lower
than average.
to design a system using this idea, you'd need to select a set of issues and
a date range to test, evaluate a variety of entry methods on this basis,
then test a variety of exit signals with the top entry methods and choose
the best combination.
make sense? is this a known concept that others have explored? other
thoughts?
dave
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