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and automatically widen when volatility increases RSI and Moving Averages were nearly tied for second place with huge profits I considered publishing the results of the backtests but didn t think it was fair to include a first-run test Now that the data has been entered into the backtest program, we can continue experimenting Although past results cannot guarantee future profits, testing helps to determine which indicators are best suited for the current, and future, market environment

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The discrete-time noise samples 2 (k) are zero mean and Gaussian with 2 variance 2 = (10m)2 The sensor y contains a bias by that is modeled as by = y by + 2 (76)

MACD (Moving Average Convergence Divergence)

1 where y 60 , E by (0) = by , var(by (0)) = Pby (0) = 32 m2 and 2 is white, Gaussian and uncorrelated with p, v, a, 1 , 2 , and 1 The PSD of 3 2 2 is by = 30 (m/s) Hz Assuming that the above information is accurate, the objective is to design a system that accurately estimates p, v, and a at the frequency 2

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f1 The navigation system should maintain the position accuracy to better than 50 m while position aiding information is available and maintain an accuracy of better than 10 m for at least 50 seconds after loss of the position sensor information

140 130 120 110 100 90 80 70 5 0 5 10 15 A M J J A S O N D 2009 6 19 week MACD 6-week signal line Rising Double Bottom M A M J J A S O N D 2010 M A M Falling Double Top Positive divergence at 2009 market bottom: Rising double bottom in MACD with falling double bottom in price

The system kinematics are described as p v = = v a (77) (78)

SQN (System Quality Number)

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To these equations, we must augment the accelerometer and position sensor calibration parameters The augmented system kinematics have the form x = f (x, a, ) where x = [p, v, bu , by , ] , = [ 1 , 2 , 1 ] and x2 v a a = 1 1 f (x, a, ) = y by + 2 y x4 + 2 0 0 The initial value of the augmented state is x0 = [E p(0) , E v(0) , bu , by , 0] with covariance Px (0) = diag ([ Pp (0), Pv (0), (001)2 , 32 , (001)2 (710) (79)

The navigation mechanization equations are (711)

050

where x = [ , v , u , y , ] , acceleration is estimated as a = p b b f ( , a, 0) = x v a 0 y y b 0 = x2

(712)

100

(f1-1)

150

Figure 71: Timeline illustration of the relationship between the fast timeindex j used for state integration and the slow time-index k used for Kalman lter position aiding for the case that f2 = 1Hz

In the general mathematical formulation given above, the mechanization equations may not look quite as familiar as they do when written out in the component-wise discrete-time equivalent form pj+1 vj+1 u b

5/20/09 6/20/09 7/20/09 8/20/09 9/20/09 10/20/09 11/20/09 12/20/09 1/20/10 2/20/10 3/20/10 4/20/10

= = = = =

Comment: The SQN for the S&P 500 through May 17, 2010 Notice that while the reading is still neutral, it looks quite ominous in a chart form

(713)

where = exp( y dt) and dt = f11 At least the rst two equations should look very familiar from elementary physics These equations are iterated at the f1 rate The (fast) time index j = [0, f1 1] is used instead of the (slow) time index k = 0, 1, 2, to make clear the distinction The relation between the fast and slow time indices is illustrated in Figure 71 At the time of each position measurement update: prior to the update j should be (f1 1), after the measurement update j is reset to zero, and the result of the measurement update serves as the initial condition (ie, at j = 0) for the next interval of integration

ES-067: 09/09/1997 08/17/2010 (Daily bars) E-Mini S&P 500 Cad Liq Template: Default Fri 07/02/2010 (ES-201009): O = 102245 H = 103250 L = 101075 C = 101425 TdofM = 2 TdofY = 130

For this sample problem, all the sensor models are stated in the problem statement Given the estimated state x, the aiding sensor output is predicted at discrete-time instants as yk = pk + y (k) b (714)

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