hi,

i've been researching the formula and found some sites:

http://tinyurl.com/yaspu7b
http://tinyurl.com/ydymfcg

now the question is what do you use for the "target" or cut off point?  i
will have to run some examples and compare with using stdev and see what
happens to SQN.

i've coded a SQN calc in the backtester, so it's probably easy to just
replace the stdev calc with semi-dev calc once i'm confortable with the
formula...

nick

On Thu, Oct 8, 2009 at 11:33 AM, Thomas Ludwig <[email protected]> wrote:

> Nick,
>
> using semideviation seems to be a self-evident approach. Any idea how to
> code
> that in AB?
>
> Thanks
>
> Thomas
>
> On 08.10.2009, 18:01:36 NickW wrote:
> > Hi,
> >
> > This is a great thread. Thanks for all the valuable information.
> >
> > Using SQN is something I've struggled with aswell where is punishes the
> > outliers.  When designing trend following systems, 5% of the trades are
> the
> > big winners that makes trend following work, so you really don't want to
> > punish the system for having few great winners.  I am going to look more
> > into semideviation to see how that can solve that problem.
> >
> > Thanks
> > Nick
> >
> > On Thu, Oct 8, 2009 at 7:50 AM, Howard B <[email protected]> wrote:
> > > Hi Mike --
> > >
> > > CAR is compound annual rate of return.  Expectancy is the percentage
> (or
> > > dollar amount, but not for this discussion) gain on the average trade.
> > > The final value of the trading account, Terminal Relative Wealth in
> some
> > > descriptions, is: (1 + expectancy) raised to the power of the number of
> > > trades.  When there are a few large trades included, this relationship
> is
> > > slightly different, but not enough to worry this discussion.  CAR is:
> (1
> > > + annual gain) raised to the power of the number of years.  These are
> > > both based on geometric means.  Aren't they the same, or so close that
> > > they act the same when used as an objective function?
> > >
> > > Van Tharp defines System Quality Number on page 28 of "Definitive Guide
> > > to Position Sizing."  SQN = (expectancy / standard deviation) times
> > > squareroot of number of trades.  SQN decreases when a median trade is
> > > replaced by either a large win or a large loss.   It is possible to
> > > redefine the metric so that large wins are not penalized, such as by
> > > using the semi-deviation instead of the standard deviation as the
> > > denominator.  But without making that change, outliers, both good and
> > > bad, do affect SQN by reducing it.
> > >
> > > What happens with open (or potentially open) trades at the boundaries
> of
> > > walk forward periods is difficult to handle in both theory and
> practice.
> > > Tomasz' implementation is to close all open trades at the end of each
> WF
> > > period; and go into each WF period flat, not taking a new position
> until
> > > there is a new signal.  This creates a potentially serious distortion
> of
> > > results when trades are typically held a long time (a large proportion
> of
> > > a WF period) or when there are a few large trades that comprise the
> > > majority of a system's profit or loss.
> > >
> > > I think this leads to a conclusion that we both agree on -- high
> quality
> > > trading systems that can benefit from position sizing are based on high
> > > frequency trading with very careful control over losses, and even
> control
> > > over gains.
> > >
> > > Thanks for listening,
> > > Howard
> > >
> > > On Wed, Oct 7, 2009 at 2:36 PM, Mike <[email protected]> wrote:
> > >> Howard,
> > >>
> > >> Assuming that SQN is the t-test for expectancy, then optimizing on the
> > >> t-test of expectancy (i.e. SQN) is not the same as optimizing on CAR.
> > >>
> > >> The primary reason that CAR is a poor target for optimization is that
> > >> outliers can significantly improve the calculation. The exact opposite
> > >> is true for SQN.
> > >>
> > >> SQN rewards consistency and punishes outliers. Consistent winners with
> a
> > >> few large wins will improve CAR but hurt SQN, resulting in different
> > >> parameter combinations being selected during an optimization.
> > >>
> > >> As for writing a custom method, AmiBroker's stats are calculated based
> > >> on the assumption that all open trades are closed out at the backtest
> > >> boundary date. Many open trades, or even just a few large open trades,
> > >> can skew these values.
> > >>
> > >> For high frequency strategies or strategies using heavy position
> sizing,
> > >> creating a custom function is the only way to get reliable
> measurements.
> > >>
> > >> Mike
> > >>
> > >>
> > >> --- In [email protected] <amibroker%40yahoogroups.com>,
> Howard B
> > >>
> > >> <howardba...@...> wrote:
> > >> > Greetings all --
> > >> >
> > >> > There has been a lot of activity on this thread. I'll not respond to
> > >>
> > >> each
> > >>
> > >> > point individually, but will make a couple of general comments.
> > >> >
> > >> > I know David Aronson, speak with him regularly, and collaborate with
> > >> > him
> > >>
> > >> on
> > >>
> > >> > projects. I have a copy of his book, "Evidence-Based Technical
> > >>
> > >> Analysis."
> > >>
> > >> > His book is excellent and I highly recommend it. I think David and I
> > >> > are
> > >>
> > >> in
> > >>
> > >> > pretty close agreement on most of the modeling, simulation, testing,
> > >> > and validation issues.
> > >> >
> > >> > I have spoken with Robert Pardo and have exchanged several emails
> and
> > >>
> > >> forum
> > >>
> > >> > postings with him. I think his earlier book was very good,
> > >> > particularly
> > >>
> > >> at
> > >>
> > >> > the time it was published. And his more recent book is not quite up
> to
> > >> > those standards. There are several important areas he did not cover
> > >> > and several areas where I see things considerably differently than
> > >> > Robert.
> > >> >
> > >> > I have spoken with and exchanged emails with Van Tharp, and I have
> > >>
> > >> copies of
> > >>
> > >> > his books "Trade Your Way to Financial Freedom" and "Definitive
> Guide
> > >> > to Position Sizing." Both are excellent, and I recommend them both
> > >> > highly.
> > >>
> > >> Be
> > >>
> > >> > sure to get the second edition of Trade Your Way to Financial
> Freedom
> > >> > --
> > >>
> > >> it
> > >>
> > >> > has some important corrections and clarifications.
> > >> >
> > >> > Permit me a short rant on my soapbox. I really dislike it when
> people
> > >>
> > >> claim
> > >>
> > >> > ownership of common terms. Tom DeMark, Robert Pardo, Van Tharp, and
> > >>
> > >> others
> > >>
> > >> > put Service Mark symbols on terms that they think are unique to
> them,
> > >>
> > >> but
> > >>
> > >> > are not. I appreciate Tharp's enthusiasm over what he calls System
> > >>
> > >> Quality
> > >>
> > >> > Number, but I wish he would not put the Service Mark symbol next to
> > >>
> > >> every
> > >>
> > >> > occurrence of it. And trying to Service Mark the term Position
> Sizing
> > >> > is like a dietician service marking "calorie counting." Robert Pardo
> > >> > claims "Walk Forward." I used exactly that term describing exactly
> > >> > that process
> > >>
> > >> in
> > >>
> > >> > research papers I delivered at conferences in the late 1960s. The
> mark
> > >>
> > >> has
> > >>
> > >> > been registered, not by Robert, but by a company I used to work for
> > >> > and
> > >>
> > >> with
> > >>
> > >> > which Robert was not associated, over my strong objection. End of
> > >> > rant.
> > >> >
> > >> > System quality number is equivalent to t-test. Systems with SQNs
> above
> > >> > 2 work well for exactly the same reasons that systems with t-test
> > >> > scores
> > >>
> > >> above
> > >>
> > >> > 2 work well. In fact, it is possible to create a custom objective
> > >>
> > >> function
> > >>
> > >> > that Is the t-test and use it for optimization. Attendees at my
> > >>
> > >> workshops
> > >>
> > >> > in Melbourne later this month will see that demonstrated. Optimizing
> > >> > for the t-test of expectancy is equivalent to optimizing for CAR, so
> > >> > don't bother creating the custom function unless you have a better
> > >> > candidate
> > >>
> > >> for
> > >>
> > >> > your objective function than CAR.
> > >> >
> > >> > Back to the topic at hand -----
> > >> >
> > >> > There is No rule of thumb to determine how long the in-sample period
> > >>
> > >> should
> > >>
> > >> > be. The Only way to determine that is by testing the model and the
> > >> > data together. And be prepared for that length to change over time.
> > >> > Some writers suggest a relationship between the number of free
> > >> > parameters and
> > >>
> > >> the
> > >>
> > >> > number of data points, or some proportional division of the
> available
> > >>
> > >> data.
> > >>
> > >> > Those techniques do work on industrial time-series data which is
> > >> > usually stationary, but they do not work on financial time-series
> data
> > >> > which is non-stationary and changes as trading systems become better
> > >> > at
> > >>
> > >> extracting
> > >>
> > >> > inefficiencies from it.
> > >> >
> > >> > No matter how good the in-sample results look, no matter how high
> the
> > >>
> > >> t-test
> > >>
> > >> > score is, no matter how many closed trades are represented --
> > >> > in-sample results have no value in estimating the future performance
> > >> > of the
> > >>
> > >> system.
> > >>
> > >> > None. The only information you have that gives any indication of
> > >> > future performance are the out-of-sample results from testing on
> data
> > >> > that was never used at all -- not even once -- during system
> > >> > development.
> > >> >
> > >> > Tomorrow is out-of-sample. The only way to prepare for real-money
> > >>
> > >> trading
> > >>
> > >> > tomorrow is to be rigorous during the system testing and validation
> > >> > process. Anything less will overestimate the probability of success.
> > >> >
> > >> > Thanks for listening,
> > >> > Howard
> >
>
>
> ------------------------------------
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