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market indexes should be followed every day, because your raise cash indication could evolve at any time or point A count of the number of distribution days that have occurred day by day is displayed in the Big Picture column of Investor s Business Daily These concepts serve as a helpful guideline to learning how to interpret the major indexes for yourself, O Neil suggests People who spent the time to learn this system were able to sell and retreat into cash before major bear markets and protect a significant part of their prior period s earnings, he says And they avoided taking the excessive losses that many investors experienced in these difficult periods According to O Neil s research, which contains a computer database covering 27 market cycles going back to 1880, the highly successful market patterns have not changed The main reason is because the law of supply and demand doesn t change, and neither does human nature O Neil is not a big fan of most traditional technical indicators: The concept of looking at a lot of indicators is a relatively mediocre method because nothing is as accurate as the market itself The many indicators people use are derivatives, and many of them are based on theories and personal opinions about the basic items that you re trying to understand Opinions are frequently wrong; markets seldom are

h x x(tk )= (tk ) x 1 Kk = P Hk Rk + Hk P Hk k k + Pk = [I Kk Hk ]Pk P = k P+ k + Qdk k+1 k

0 x = x0 P = var( x ) 0 0 zk = yk yk + = + Kk zk Hk xk xk xk = k + xk+1 xk

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Nevertheless, O Neil is a strong believer in using charts for both the market and individual stocks When choosing individual stocks, he says that stocks go up based on 80 percent fundamentals such as which stock has an outstanding product that s gaining market share in its field You re looking for a company that s the current leader fundamentally as well as technically, he explains This can only be determined by daily, weekly, and monthly charts of the stock s price and volume action To be successful at picking stocks, O Neil suggests that you develop some ability at separating the very best companies fundamentally from all the other ones that don t measure up He says, You want to be able to see that the marketplace and the chart action are confirming what the fundamentals are showing

Table 57: Linearized Kalman lter equations Computation of the matrices k and Qdk is discussed in Section 471 su ciently accurate, then 1) the state estimate should be near the actual state; 2) the linearization should be accurate; and, 3) the performance should be good If however, the estimate is far from the actual state, the linearization will be inaccurate and the estimate may diverge rapidly The possibility of using a poor state estimate in the lter gain calculations makes the Extended Kalman Filter (EKF) riskier than the linearized Kalman lter Alternatively, the fact that the estimated state is typically (under good observability conditions) closer to the actual state than a prede ned nominal trajectory usually allows the extended Kalman lter to produce better performance than the linearized Kalman lter The implementation equations for the EKF are summarized in Table 58 The algorithm can be manipulated to correctly work with either total states (ie, x(t)) or error states (ie, (t)), as discussed further in Section x 51053 51053 Complementary Filtering and the EKF

O Neil has often told investors to create a set of rules before entering the market It s not enough to recognize which stocks to buy and when to buy them, he cautions You must also have a set of sell rules to nail down worthwhile gains once you have them, and even more important, a set of sell rules to cut short all mistakes In fact, O Neil is famously known for telling investors to cut losses at 8 percent from the price they paid for the stock That s the only way to avoid 30 to 50 percent losses that can easily occur to any new or experienced investor, he says You can t sit with mistakes; you have to learn to read the market and listen to it, and not fight the reality of the market, which can be costly

In the implementation of the EKF, the solution to k x x (t) = f ( (t), u(t), t), for t [tk , tk+1 ) with x (tk ) = x+ is required between sampling instants This book will primarily focus on navigation systems implemented using a complementary lter structure,

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