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Test 12: Volatility Breakout with Limit Entry and Trend Filter. The same model from Tests 10 and 11 is used; instead of restriction to long positions or currencies, the signals are filtered for trending conditions using the Average Directional Movement index (or ADX; Wilder, 1978). By not entering trendless markets, whipsaws and languishing trades, and the resultant erosion of capital, can hopefully be reduced. The ADX was implemented to filter breakouts as suggested by White (1993). Trending conditions exist as long as the 18-bar ADX makes a new 6-bar high, and entries are taken only when trends exist.



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Much like .NET assemblies, Silverlight assemblies can be compiled in a debug mode configuration or a release mode configuration. The main differences between debug and release mode are which conditional symbols are defined and whether symbols are generated along with the assembly. For debug mode, the preprocessor symbol DEBUG and TRACE are automatically defined, and for release mode, TRACE is defined. Sometimes implementing code only for purposes of debugging can be extremely useful. For example, an application might write a significant amount of information to a log file for debugging only. This code can t run in production applications due to performance reasons, and optimally we want to get rid of this code completely. This can be achieved with conditional compilation. The best approach to conditional compilation is to use #if...#endif to isolate blocks of code that must only appear in certain configurations. Generally, these are used to only put debug code in debug builds for example, writing to a debug trace log. private void login() { #if DEBUG traceLog.WriteLine("entered login method");

Pulse Code Modulation (PCM)

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As in previous tests, a genetic algorithm optimized the parameters. All 100 parameter combinations except one produced positive returns in-sample; 88 returned greater than 20%, demonstrating the model s tolerance of parameter variation. The best parameters were: bandwidth, 2.6; moving average length, 8; and average true range period, 34. With these parameters the in-sample return was 68.3%; the probability that such a high return would result from chance was less than one in two-thousand, or about one in twenty-nine instances when corrected for optimization. There were 872 trades and 47% wins. The average trade generated about $4,500 in profit. Out-of-sample the average trade lost $2,415 and only 36% of all trades taken (373) were winners. The return was -20.9%, one of the worst out-of-sample performances. The ADX appears to have helped more in the past than in current times.

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Thanks to the work performed by Harry Nyquist at Bell Laboratories in the 1920s, we know that to optimally represent an analog signal as a digitally encoded bitstream, the analog signal must be sampled at a rate that is equal to twice the bandwidth of the channel over which the signal is to be transmitted. Since each analog voice channel is allocated 4 KHz of bandwidth, it follows that each voice signal must be sampled at twice that rate, or 8,000 samples per second. In fact, that is precisely what happens in T-Carrier systems, which we now use to illustrate our example. The standard T-Carrier multiplexer accepts inputs from 24 analog channels, as shown in Figure 3-66. Each channel is sampled, in turn, every one eight-thousandth of a second in round-robin fashion, resulting in the generation of 8,000 pulse amplitude samples from each channel every second. The sampling rate is important. If the sampling rate is too high, too much information is transmitted and bandwidth is wasted; if the sampling rate is too low, then we run the risk of aliasing. Aliasing is the interpretation of the sample points as a false waveform, due to the paucity of samples. This PAM process represents the first stage of PCM, the process by which an analog baseband signal is converted to a digital signal for transmission across the T-Carrier network. This first step is shown in Figure 3-67. The second stage of PCM, shown in Figure 3-68, is called quantization. In quantization, we assign values to each sample within a constrained range. For illustration purposes, imagine what we now have before us. We have replaced the continuous analog waveform of the signal with a series of amplitude samples which are close enough together that we can

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