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<SPAN
class=274462918-22112003>thought of that, but I think the AFL approach gets you
some info that the built-in MFE/MAE stats don't.
<SPAN
class=274462918-22112003>
<SPAN
class=274462918-22112003>basically, you get stats for all bars, side by
side with stats for buys only. readouts for each bar cover the spec'd number of
bars into the future, or until the loss stop is hit, whichever comes
first.
<SPAN
class=274462918-22112003>
<SPAN
class=274462918-22112003>if you buy the time and loss stop idea, I think this is
exactly what you want to know, and I couldn't figure out how to get it out of
MFE/MAE (not that those stats aren't great to have
too).
<SPAN
class=274462918-22112003>
<SPAN
class=274462918-22112003>since you've been doing something like this already,
have you found it to be a useful way to evaluate entry signals? if so, would you
mind sharing which ones are looking good from this
perspective?
<SPAN
class=274462918-22112003>
<SPAN
class=274462918-22112003>dave
<SPAN
class=274462918-22112003>
<BLOCKQUOTE
>
An
excellent concept, but I may be able to give you an idea to make it
easier.
<FONT face=Arial color=#0000ff
size=2>
I do
what you propose, except I eliminate the "stop". Just leave an
exit after so many days.
<FONT face=Arial color=#0000ff
size=2>
<FONT face=Arial color=#0000ff
size=2>Then, using the portfolio backtester or other software, have a look at
the MAE and MFE reports.
<BLOCKQUOTE
>
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'drun the concept by folks here
for some feedback while I finalize it. here'sthe basic
idea:on the long side, profits come from selling at a higher
price than youbought. so, a good entry signal is one where the stock
frequently risesreasonably soon after a buy signal occurs. with the
exception of a permanentdown-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 relativelytight stoploss is
needed to limit risk and mental pain, and 2) we're lookingfor short term
signals, to generate a statistically meaningful number oftrades, and to
limit the opportunity cost of continuing to hold a positionthat'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
possibleassumptions, but they're what's used here.given those
assumptions, the quality of an entry signal can be evaluated asthe peak
percentage rise in price, after commissions, before one of thosetwo
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, andthe design and testing of an exit signal specific to a
given entry model isbeyond the scope of this effort. however, unless
price reaches a healthyprofit fairly often before those basic stop exits
happen, the entry signalitself isn't useful; you could do better with
random entries.peak price rise before those time or loss stops are
hit can be evaluated forany bar, not just when there's a buy signal. the
efficiency of a buy signalcan then be expressed as the percentage ratio
of the average peak rise onbuy bars to that of all bars. greater than
100% means it entered on barswith higher than average gain before
stopping out, less than 100%, lowerthan average.to design a
system using this idea, you'd need to select a set of issues anda 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 choosethe
best combination.make sense? is this a known concept that others
have explored?
otherthoughts?dave
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