Preparing for a Career in Architecture in Software

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82 | Baby Signs
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Solution. The five equally spaced datapointscan be generated in as
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APPENDIX: Answers to Quiz, Test, and Final Exam Questions
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Although percentage bands are useful and many trading systems have been created using them, they have a number of problems. Bollinger explains: By addressing those problems, I was led to create Bollinger Bands. The first problem was that you needed different width bands from stock to stock. Some stocks need bands that are 2 to 3 percent wide, and some stocks need bands that are 7 to 10 percent wide. Because the bands varied, depending on the stock and time period, Bollinger said using them was very time consuming, primarily because you had to draw them by hand. Perhaps the worst problem with percentage bands, he says, was they allowed your emotions to enter the trading process, which is a disaster, as we all know. He says that percentage bands were designed based on data the trader wanted to include, a very subjective process. I was looking for a system that could set the bands automatically, Bollinger says. At the time, he was an options trader and was very concerned with how options were priced, which was based on calculating volatility: In those days, we believed that volatility was a fixed number. We thought it was part of the property of a security. In other words, once volatility was calculated for a security, it wouldn t be revised. No one bothered to calculate it more than once a year, because it was believed that volatility didn t change, he says. Fortunately, Bollinger had access to an early version of a spreadsheet called SuperCalc using the old CP/M [Control Program for Microcomputers] operating system. One day I copied the volatility formula on the spreadsheet and saw for the first time that volatility was actually a dynamic number, he explains. It was changing all the time. And I said, That s interesting! That s when he realized he could use the volatility calculation to make the percentage bands more adaptive to real-life market conditions. Over the course of a few months, with trial and error and different forms of volatility calculations, I came up with Bollinger Bands, notes Bollinger. At the time, he knew he had a valuable tool, but he didn t think others would recognize it. The way it was introduced to the public was through a television interview I did on the Financial News Network in the early 1980s, he says. I showed a chart that had my indicator on it. I said we are projecting that the market will rally from the lower band to the upper band in a relatively short
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Figure 3.11 The Exchange Task Wizard.
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The first step is to find a color scheme. You can go with something simple or elaborate, but be careful, especially if you re a developer. The colors in this example were extrapolated from a book on color combinations, The Designer s Guide to Color Combinations: 500+ Historic and Modern Color Formulas in CMYK by Leslie Cabarga (North Light Books, 1999). First, the CMYK values were changed to RGB values and then to hexadecimal. The following palette makes up the color schemes: Hex Value
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Percent of NYSE Stocks Above Their 200-Day Moving Averages.
5. Preacher curls: The bench used for this
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For our next FGA example, we ll leverage more session context information to narrow the allowed parameters of the audit condition a bit. This time, we will use MODULE to check that the query is coming from a specific APEX application. We can also use the SYS_CONTEXT function to determine the IP address of the client that issued the query. In this case, the expected client is our HTTP server; any other IP address indicates the query might be coming from another client such as a SQL*Plus connection from an unauthorized workstation.
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