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Replying to your message of Friday, February 28, 2003, 12:53:13 PM,
My feeling is that the equity curve is telling us only second-hand
that our system is not working as well as it did in the past. Rather
than focusing on the equity curve itself, I think it is more
profitable to focus on what it is about the market and system being
traded that leads to the poorer results; i.e., identify additional
tests or filters on trading based on patterns, your favorite
indicators, etc. Of course, you need to be sure you aren't curve
fitting. I agree that self adapting systems that make trade decisions
based on changing market characteristics is a better way to go than
basing trading decisions on the equity curve.
L> This thread has stimulated a lot of thinking for me. A few years ago I
L> experimented with various moving averages of the equity
L> curve and also looked into analyzing the equity curve as a data stream
L> in order to come up with some advance idea as to the
L> performance of the system. However, I have concluded that the most
L> important issue to understand is why systems stop
L> working. Markets constantly change and it becomes clear that the reason
L> a system fails to perform during any given time
L> period is because the decision rules of the system no longer fit the
L> market conditions at the time. Of course this seems
L> elementary, yet it leads to the whole question of self adaptivity and
L> optimization. In analyzing many different compressions of
L> the same security for the same period, ranging from tick to daily by 1
L> minute increments, it is possible to determine some
L> relationships of noise to identifiable trending behavior as that ratio
L> moves through the various compressions. On a simple level,
L> it is clear that the smaller the compression, the more noise, however it
L> does not follow that every compression yields a logical
L> progression of that ratio. It is also instructive to analyze different
L> time frames in each of the compressions to understand how
L> this ratio evolves.
L> We know that markets go through periods of congestion and periods of
L> direction. We also understand that there are
L> relationships between securities, and various indices. Systems work
L> when whatever paradigm of market conditions that were
L> designed into the system exist. When the market has evolved into an new
L> circumstance, the system stops working and we
L> begin lose money. Confidence in the system is really based on the idea
L> the we believe that the market will return to the former
L> iteration that produced results in the past. Depending on the level of
L> refinement of the systems decision rules, that may or not
L> happen. For example, we all know that over enough time and in the right
L> compressions, moving average systems can produce
L> good results. But we also know that these systems go through periods of
L> deep draw downs. Perhaps the reason the simple
L> systems produce better results over time, is because they contain a more
L> common market condition and the system is designed
L> to, by it's very nature, ignore more of the intermediate market
L> perturbations.
L> What is clear is we can organize the data that is created by a security
L> in many different ways and that the act of organizing the
L> data is significant to the ultimate potential to be systematized.
L> Perhaps we bring order to the chaos of a data stream by that
L> very act. We can also change the ratio of noise to trend for any given
L> time period by altering the compression, however what
L> we cannot do is predict that ratio in the future. It constantly
L> changes. This is the challenge when we try to create evolutionary
L> systems that can adapt to market conditions.
L> Thanks to all who have contributed to this thread,
L> Lawrence Price
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