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As it mentions, a Heuristic aprroach is required (compounding with more data).
* Now this is a LOT of data to work with. ie Lots of Weighted variables.
(More than AB can handle on it's own. Or even Excel etc etc).
The easiest method I can think of - Would be to utilize some sort of Neural
net to try and Learn the patterns for that stock. (It would be very stock
specific).
And re-run (optimize) each day as new data comes about (The more it LEARNS,
the better chance it has of getting it right).
<MSG>
At 06:08 AM 23/10/2003 -0700, you wrote:
>Hello all,
>
>Since we have been throwing around methods of optimizing and selecting
>objective measures, I was wondering if some of the more skilled in our
>group could translate the following passage, as my Sergeant would say back
>in my Army days " in terms a 10-year old could understand". The passage
>is from from Bob Pelletier in the latest CSI Technical Journal.
>
>Some pre-emptive questions belfore the passage:
>
>1. Fred, is Pelletier's comments the same as what you are referring to in
>terms of making system adjustments on the fly? Optimizing on only OS data
>on a go-forward basis? If you are willing to break it down into a process
>it would be much appreciated by myself and probably a lot of others.
>
>2. If he is going only one pass through the data how is he coming up with
>paramaters?
>
>I'm typing out his comments so please forgive typos.
>
>PELLETIER'S COMMENTS
>OCT 03 CSI TECHNICAL JOURNAL
>
>"... This is a novel approach that depends upon large amounts of data to
>arrive at a solution. Theoretically, the more data supplied the better
>should be the result. It views all markets and variables forward in
>time. There is no hindsight optimization testing. Analysts correctly
>believe that basing future decisions on results optimized in the past
>tends to produce unrepeatable performance in real-time trading. Such
>complaints boil down to the derogatory label of "curve fitting". None of
>that is done here. This model learns by studying only future (or forward)
>facts in time. Only one pass through all of the supplied data defines the
>trading model that will be forecasting tomorrow's prices.
>
>The model-building stage of the process includes a feedback mechanism that
>measures the daily causal effects of each independent variable upon the
>movement of the dependent variable. The feedback logic tends to
>emphasize the positive and negative effects of each variable upon the
>dependent variable. The decision-making process process therefore becomes
>increasingly heuristic over time as more and more price history is
>reviewed. The model is encouraged by predictive success and discouraged
>by the apparent failures of all independent time series supplied. The
>basic intent of the process is to teach the computer to profitably trade a
>dependent variable basded upon the predictive characteristics of all
>independent variables...."
>
>COMMENTS???
>
>
>
>
>
>
>
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