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Y Global information infrastructure and Internet protocol aspects Z Languages and general software aspects for telecommunication systems Within each letter designator can be found specific, numbered recommendations For example, recommendation number 25 in the X book contains the specifications for transporting packet-based data across a public network operating in packet mode This, of course, is the now famous X25 packet-switching standard Similarly, Q931 provides the standard for signaling in integrated services digital networks (ISDN) networks, and so on The documents are remarkably easy to read and contain vast amounts of information I am always surprised to discover how many people who work in telecommunications have never read the ITU standards Take this, then, as my recommendation: Find some of them and flip through them They can be very useful I remember very well an experience I had years ago with the ITU standards.



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My family was visiting friends in California, but because of work commitments I was in Vermont preparing to travel It was a dark, snowy January night and I was sitting in front of a roaring fire, drinking a warm brandy and reading Q931 The worst part was that I was really into it, enjoying the fact that I was understanding the technojargon And then it hit me: I had become one of them I was a propellerhead I spent some time writing about the ITU and its standards activities simply to explain the vagaries of the process (one of my favorite telecomm jokes goes like this: There are two things you never want to watch being made One of them is sausage; the other is standards ) and the role of these bodies.





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not be representative of the population defined by the later period because it was drawn from a different population! This is, of course, an extreme case. More often, structural market variations are due to subtle influences that are sometimes impossible to identify, especially before the fact. In some cases, the market may still be fundamentally the same, but it may be going through different phases; each sample drawn might inadvertently be taken from a different phase and be representative of that phase alone, not of the market as a whole. How can it be determined that the population from which a sample is drawn for the purpose of system development is the same as the population on which the system will be traded Short of hopping into a time machine and sampling the future, there is no reliable way to tell if tomorrow will be the day the market undergoes a systemkilling metamorphosis! Multiple out-of-sample tests, conducted over a long period of time, may provide some assurance that a system will hold up, since they may show that the market has not changed substantially across several sampling periods. Given a representative sample, statistics can help make accurate inferences about the population from which the sample was drawn. Statistics cannot, however, reveal whether tomorrow s market will have changed in some fundamental manner.

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The ITU is representative of the manner in which all standards are developed, although the frequency of update, the cycle time, the relative levels of involvement of the various players, and the breadth of coverage of the documents vary dramatically..

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Another issue found in trading system development is optimization, i.e., improving the performance of a system by adjusting its parameters until the system performs its best on what the developer hopes is a representative sample. When the system fails to hold up in the future (or on out-of-sample data), the optimization process is pejoratively called curve-fitting. However, there is good curve-fitting and bad curve-fitting. Good curve-fitting is when a model can be fit to the entire relevant population (or, at least, to a sufficiently large sample thereof), suggesting that valid characteristics of the entire population have been captured in the model. Bad curve-@zing occurs when the system only fits chance characteristics, those that are not necessarily representative of the population from which the sample was drawn. Developers are correct to fear bad curve-fitting, i.e., the situation in which parameter values are adapted to the particular sample on which the system was optimized, not to the population as a whole. If the sample was small or was not representative of the population from which it was drawn, it is likely that the system will look good on that one sample but fail miserably on another, or worse, lose money in real-time trading. However, as the sample gets larger, the chance of this happening becomes smaller: Bad curve-fitting declines and good curve-fitting increases. All the statistics discussed reflect this, even the ones that specifically concern optimization. It is true that the more combinations of things optimized, the greater the likelihood good performance may be obtained by chance alone.

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