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Parts of your solution may end up getting more data than they need. If that data is in Extensible Markup Language (XML), that may mean that more time is spent parsing and rendering the XML than is truly necessary. If the data is in a JMS, CORBA, or RMI message, the parsing and rendering time is hidden by the protocol but the time is still there, and still wasted on unused data. Big, unexpected changes in performance from the test environment to the deployment. This can indicate that you misunderstood the data, most likely indicating that the size changed noticeably. If you overestimate data requirements you will get the inverse effect. You may have way more available bandwidth or processing power than you need. This can lead to a higher project cost than was actually necessary.

17: Using Advanced EJB Techniques Now you need to provide variables that represent each of the fields. The simplest implementation is to just use a public variable per method that you had in your previous bean instance (this will horrify the OO purist who would insist on having setter and getter methods!). This is how the class would now look with the addition of the public variables:

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Simple Moving Averages and Popular Alternatives In 1 we examined two indicator-driven triggers that are also complete mechanical trading systems: the single moving average and the two moving average crossover The variations on moving average indicators are so numerous that a book could be devoted exclusively to their various flavors; however, in the interest of completeness, I address what I believe are some of the most significant alternatives to the simple moving average As discussed in 1, simple moving averages are the most widely used and the easiest to calculate because they give equal weighting to each data point within the data set This issue of equal weighting to each data point leads technicians to seek alternatives to the simple moving average.

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The problem with using a moving average that gives equal weight to each data point is that with longer-term moving averages such as the 200day moving average the lagging aspect of indicator means it will be slower to respond to changes in trend Obviously slower response times to trend changes could mean less reward and greater risk One solution to the problem of the lagging nature of the simple moving average is to give greater weight to the most recent price action Linearly weighted and exponentially smoothed moving averages both attempt to address the equal weighting issue by giving a larger weighting factor to more recent data2 An alternative to the moving average weighting paradigm is found through the use of a volume-adjusted moving average.

Initially we considered that the estimates of gross flows based on the response sample likelihood might explain the nonignorable nonresponse. The similarity of the results of the weighted and response likelihood methods is not surprising, since the calibrated weights used in both methods are only a function of auxiliary variables and do not depend on the labour force status. The interesting result is that if we have sample data that contain the response variable and the sampling weights and for nonresponse the calibrated adjusted weights, then basing inference using the classical weighted method and the proposed method based on the response likelihood may give similar results.

The volume-adjusted moving average suggests that directional movement accompanied by strong or weak volume is often a better measure of trend strength than any of the time-driven weighting models Another problem with moving averages is choosing between shorter and longer time parameters The smaller the data set, such as a 7-day moving average, the quicker the indicator s ability to generate signals and the greater its reduction of lag time But smaller data sets also result in more false trend-following signals during sideways, consolidation environments As discussed, larger data sets, such as a 200-day moving average, will generate fewer, higher-quality entry signals, but those remaining signals will entail less reward and greater risk Perry Kaufman, author of many books on.

Before diving into the details of JMS programming, there are several things that you should be familiar with. These include some basic messaging terminology, an overview of the interfaces involved, and a high level operational view of JMS systems.

One of the pitfalls of RSI extremes was that the average duration of a trade was the same as that of our intermediate-term trend-following systems. This problem can be eliminated by changing its exit condition #1 criteria, but to showcase another trend-following mean reversion system, I have chosen instead to introduce the fading of our trend-following Bollinger band system with the addition of a 200-day moving average criteria to filter out countertrend trades. Using CQG, the programming code for this trend-following mean reversion system is written in this way: Long Entry:

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