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All -
I've discussed the following with Howard and am posting mainly for
others on the board. He might want to comment on the trade-offs. But,
there is an easy way in practice to deal with one of the drawbacks that
he mentioned when he said -
"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 anew
position until there is a new signal. "
Closing all trades at the end of a WF period is a compromise of sort to
avoid some messy analysis at the boundary. BUT, it is fairly easy to
handle going into each WF period and not taking a new position until
there is a signal.
If the signals are in impulse form (generated with Cross()), then the
system will be flat in the WF period until the first signal. But, if
the signals are put in state form, the trade in progress will be taken
at the beginning of the period. To do this, all that is necessary is
to place the following code at the end of the program -
Buy = Flip( Buy, Sell);
Sell = Not Buy;
Finally, a brief example to illustrate. Say you have a simple timing
system and the walk-forward analysis is yearly. Assume the walk-forward
period for 2005 has a Buy on Nov. 1. In processing that year, AB sill
generate a Buy on Nov. 1 and a Sell on Dec.31. Now if the walk-forward
for the year 2006 has a Sell on Feb. 1, you would like the system to be
on a Buy starting Jan. 2. - even though the individual trade metrics
will be skewed, the equity curve and metrics will be almost correct (in
certain cases there is one boundary effect flat day).
If the signals were left in impulse form, there would not be a Buy for
that first month of 2006. If you put the signals in state form, there
will be.
-- BruceR
--- In amibroker@xxxxxxxxxxxxxxx, Howard B <howardbandy@xxx> 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 sfclimbers@xxx 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 amibroker@xxxxxxxxxxxxxxx <amibroker%40yahoogroups.com>,
Howard B
> > howardbandy@ 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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