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The next level, he says, are indicators: With indicators, you formulate conclusions based on history, time periods, and ideal price points The third level is the systematic approach, DeMark explains, taking the indicators and systemizing them DeMark says he operates on the second level, creating proprietary indicators Although he could systemize his indicators, he s chosen not to, primarily because it doesn t allow him as much creativity One of the things that DeMark does with indicators is to view time periods uniquely We don t use terminology such as long, short, and intermediate the way most people do, he explains For most people, they will say that short-term is two weeks We use it in context of return We might say that short-term is a 10 percent move An intermediate term is a 25 percent move, and anything over 25 percent is a long-term move We could accomplish what some people perceive as a two-week return in an hour and then be out of our position

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Kalman gains or the steady-state Kalman gains Although this will lighten the on-line computational load, performance may su er For example, when steady-state gains are used, performance will su er during transient conditions such as start-up Second, the covariance equations allow prediction of the state estimation accuracy Therefore, it is often useful to perform covariance analysis in simulation prior to hardware construction to analyze implementation tradeo s or ensure satisfaction of system speci cations To summarize, assuming that P+ (0) is known, starting with k = 0, the steps of the covariance analysis are as follows for t [tk , tk+1 ) and k 0: 1 Specify the truth model: F, G, and Q 2 Specify the design model: F, G, and Q 3 Compute the discrete-time equivalent design model parameters: k and Qdk 4 Use eqn (66) to propagate the error covariance of the design model from tk to tk+1 5 At time tk+1 , increment k by one and compute H and H 6 Compute and store the Kalman lter gain sequence Kk using eqn (67) and compute the posterior design model error covariance using eqn (68) 7 Use the matrix parameters from the previous steps to process eqns (615 622) Iterate the above process over the time duration of interest or until steadystate is achieved Example 61 demonstrates the covariance analysis process Example 61 Consider the one dimension inertial frame INS aided by a biased position measurement The error equations for the INS are p 0 1 0 0 p 0 v = 0 0 1 0 v + v (623) a 0 0 0 0 a a b 0 0 0 b b p v 1 0 0 1 y = (624) a + b where var( ) = R = 10 m2 , P SD v = 25 10 3 m3 and P SD a = s 2 10 10 6 m5 The navigation mechanization equations are integrated in s.

Unlike some, DeMark has no qualms about his goal: using markettiming indicators to predict the market There are times when we ve been so precise it will boggle your mind, he says But there are also times when we are wrong DeMark freely admits his life s ambition is to find the market s Holy Grail, which means the elusive but perfect indicator that can predict where the market will go next We re looking for the Holy Grail, and eventually I m going to find it We ve found some things that are very accurate, but it s about getting everything into a computer and making it automatic DeMark s closing advice: While a trading strategy is critical, more important are trading discipline and money management skills Without these two important elements, a trader is destined to failure

discrete-time using the small time step determined by the IMU to compute the navigation state The error state dynamics are modeled in continuoustime The position aiding measurement denoted by y is available at a 10Hz rate The measurement bias b is a scalar Gauss-Markov process with = 2 1 1 and P SD b = Qb = 4 m For a scalar Gauss-Markov process 300 sec s with these parameters, Pb Pb (t) Pb ( ) = 2 Pb + Qb = e 2 t Pb (0) + 1 e 2 t = Qb 2 Qb 2

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