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Tom DeMark has been involved in the investment business for over 40 years Upon graduation with a BA and MBA and after law school, DeMark went to work for one of the fastest-growing investment counseling firms He became president of a consulting subsidiary that sold market-timing services to many of the largest and most successful funds and trusts in the country For the past 17 years DeMark has been special advisor to Steve Cohen, principal of SAC Capital DeMark is also president of Market Studies LLC, a supplier of market-timing services to Bloomberg, Thomson Reuters, CQG, and, recently, DeMark Prime DeMark has written three bestselling books and is a speaker at many worldwide investment forums and seminars Additionally, he regularly appears on financial networks WHAT S NEXT

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More from SautinSoft Team
More from SautinSoft Team

Therefore, the steady-state bias error covariance for b would be 600m2 Covariance analysis can be used to determine how a lter which ignores the measurement bias would actually perform In this case, 2 2 1 T T2 0 1 T T2 0 1 T 0 = 0 1 T (625) = 0 0 1 0 0 0 1 0 0 0 where = e T For both of the covariance analysis simulations described below, T = 10 s and P(0) is a diagonal matrix with elements 1000, 10, 0001, and 625 Figure 61 displays the actual performance (bold dotted line) and internal lter prediction (narrow solid line) of performance for the case where R = 10 m2 Therefore, except for the neglected measurement bias, the model of the implemented lter is correct Based on the design model, the lter expects an accurate, unbiased measurement of position Therefore, the lter gains are relatively high The design model covariance sequence of eqn (68) predicts that the position error standard deviation in steady-state will be less than 15m, while the covariance analysis predicts a steady-state position error standard deviation great than 24m The design neglecting the bias is overly optimistic Figure 62 displays the actual performance (bold dotted line) and internal lter prediction (narrow solid line) of performance for the case where R = 625m2 For this simulation, the implemented lter does not model the bias; instead, the design model increases R to a value meant to indicate that the error in each measurement has a standard deviation of 25m Based on this design model, the lter expects a measurement of position with a relatively large amount of white noise; however, if the noise was white, as modeled, then suitable ltering of the measurement should produce an accurate estimate of position Therefore, the lter gains are lower than the.

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In the next chapter, I ll provide you with lots of useful resources including where to get help, an indicator summary, charts, books, and stock discussion groups

K1 10 0 15 50 100 150

1 05 0 K2

student Read books, study, and analyze Don t rely on others to do all of the homework for you Understanding the stock market can be a lifelong pursuit, and to succeed you need a continuous education This book is one small step The next step is yours For now, here are some useful resources and ideas for further study

01 005 0 2 x 10

As mentioned earlier, if you have questions about market indicators while reading this book, call the Help Desk at your brokerage firm As long as you re a customer, the professionals at your brokerage firm should answer your questions (You can also send me an e-mail, if you want) Finally, if you d like to study indicators on your own, the following is a list of some very helpful resources

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Figure 61: Results of the covariance analysis of Example 61 for R = 10 m2 The standard deviation of each component of the estimated state is plotted versus time in the left column Actual performance is indicated by the bold dotted curve The performance predicted by the implemented lter is the narrow solid curve The right column plots the Kalman lter gains versus time previous case The lter trusts the lter state, which represents a time average of all past measurements more than the measurement The performance prediction by the lter is still optimistic, p = 7m predicted in steady-state while p = 25m is achieved, because the model does not properly account for the time correlation of the position measurement error The lter is not averaging white noise corrupted measurements, but measurements with a highly correlated error The actual (correlated error) will not average out over any reasonable time span A better method for addressing the sensor bias of this example without including it as an augmented state is discussed in Section 5102 In the special case where the state de nitions are the same and it is reasonable to assume that = and H = H with V = I, the error state

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