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We develop many systems using genetic algorithms. A popular$fimessfunction (criterion used to determine whether a model is producing the desired outcome) is the total net profit of the system. However, net profit is not the best measure of system quality! A system that only trades the major crashes on the S&P 500 will yield a very high total net profit with a very high percentage of winning trades. But who knows if such a system would hold up Intuitively, if the system only took two or three trades in 10 years, the probability seems very low that it would continue to perform well in the future or even take any more trades. Part of the problem is that net profit does not consider the number of trades taken or their variability. An alternative fitness function that avoids some of the problems associated with net profit is the t-statistic or its associated probability. When using the t-statistic as a fitness function, instead of merely trying to evolve the most profitable systems, the intention is to genetically evolve systems that have the greatest likelihood of being profitable in the future or, equivalently, that have the least likelihood of being profitable merely due to chance or curve-fitting. This approach works fairly well. The t-statistic factors in profitability, sample size, and number of trades taken. All things being equal, the greater the number of trades a system takes, the greater the t-statistic and the more likely it will hold up in the future. Likewise, systems that produce more consistently profitable trades with less variation are more desirable than systems that produce wildly varying trades and will



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They are mostly manufactured by Western Electric, based on a set of internal manufacturing standards that, were there other manufacturers in the industry, would be considered proprietary Because AT&T was the only game in town (in the United States at least) prior to divestiture, AT&T created the standard for transmission interfaces Fast forward now to January 1, 1984, and put yourself into the mind of MCI s Bill McGowan, whose company s very survival depended upon the successful implementation of Equal Access Unfortunately, Equal Access had one very serious flaw Keep in mind that, because the post1984 network was emerging from the darkness of monopoly control, all of the equipment that made up the network infrastructure was bought at the proverbial company store and was, by the way, proprietary Consider the newly recreated postdivestiture network model shown in Figure 3-7.





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Now our string resources are ready to bind to XAML controls to make the user interface localizable based on the language selection. For that, revisit the DragnDropDemo.xaml page. Here we will data-bind Content/Text properties of XAML controls to resources in Strings.resx, to set up the default as the English language. Also we need to add a ComboBox control to let the user select English, French, or Hebrew culture. The following is the updated XAML code with changes shown in the highlighted fonts. <UserControl x:Class="chapter7.DragnDropDemo" xmlns="http://schemas.microsoft.com/winfx/2006/xaml/presentation" xmlns:x="http://schemas.microsoft.com/winfx/2006/xaml" xmlns:d="http://schemas.microsoft.com/expression/blend/2008" xmlns:mc="http://schemas.openxmlformats.org/ markup-compatibility/2006" mc:Ignorable="d" d:DesignHeight="300" d:DesignWidth="400" xmlns:toolkit="http://schemas.microsoft.com/winfx/2006/ xaml/presentation/toolkit" FlowDirection="{Binding Source= {StaticResource LocalizedStrings}, Path=Resource.FD}"> <Grid x:Name="LayoutRoot" Background="White"> <Grid.RowDefinitions> <RowDefinition Height="150"/> <RowDefinition Height="30"/> <RowDefinition Height="*"/> </Grid.RowDefinitions> <StackPanel Grid.Row="0"> <StackPanel Orientation="Horizontal"> <TextBlock Text="Select UI Culture" Margin="10,10,0,0" FontWeight="Bold"/> <ComboBox Height="23" x:Name="CultureList" Width="150" SelectionChanged="CultureList_SelectionChanged" Margin="5,10,0,5"> <ComboBoxItem Content="en-US" /> <ComboBoxItem Content="fr-fr" /> <ComboBoxItem Content="he" /> </ComboBox> </StackPanel> <Border BorderThickness="3" BorderBrush="DarkBlue" > <Canvas x:Name="DropZoneCanvas" AllowDrop="True" Drop="DropZoneCanvas_Drop" DragOver="DropZoneCanvas_DragOver" DragLeave="DropZoneCanvas_DragLeave"

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At the local switch level, precious little has changed at this point in time there is still only a single local service provider At the longdistance level, however, there is a significant change Instead of a single long-distance service provider called AT&T, there are now three AT&T, MCI, and Sprint The competitive mandate of Equal Access was designed.

yield higher t-statistic values. The t-statistic incorporates many of the features that define the quality of a trading model into one number that can be maximized by a genetic algorithm.

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Another statistical technique frequently used is multiple regression. Consider intermarket analysis: The purpose of intermarket analysis is to find measures of behaviors in other markets that are predictive of the future behavior of the market being studied. Running various regressions is an appropriate technique for analyzing such potential relationships; moreover, there are excellent statistics to use for testing and setting confidence intervals on the correlations and regression (beta) weights generated by the analyses. Due to lack of space and the limited scope of this chapter, no examples are presented, but the reader is referred to Myers (1986), a good basic text on multiple regression. A problem with most textbooks on multiple regression analysis (including the one just mentioned) is that they do not deal with the issue of serial correlation in time series data, and its effect on the statistical inferences that can be made from regression analyses using such data. The reader will need to take the effects of serial correlation into account: Serial correlation in a data sample has the effect of reducing the effective sample size, and statistics can be adjusted (at least in a rough-and-ready manner) based on this effect. Another trick that can be used in some cases is to perform some transformations on the original data series to make the time series more stationary and to remove the unwanted serial correlations.

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Still-image applications have been in widespread use for quite some time Initially, there were photocopy machines They did not provide storage of documents nor did they offer the ability to electronically transport them from one place to another That capability arrived on a limited basis with the fax machine While a fax transmission allows a document to be moved from one location to another, what actually moves is not document content but rather an image of the document s content This is important, because there is no element of flexibility inherent in this system that allows the receiver to make immediate and easy changes to the document The image must be converted into machine-readable data, a capability that is just now becoming possible with optical character recognition (OCR) software As imaging technology advanced and networks grew more capable, other technological variations emerged.

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