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If you liked RSI, you ll love Stochastics, although it can be rowdy at times Stochastics is a favorite with experienced traders, who like its precise signals You should know that RSI and Stochastics can give almost identical signals, so you may not want to use both Meanwhile, if RSI seemed too tame for you, then step right up: you re going on a Stochastics roller-coaster ride

Assume that the output y of a gyro attached to a non-rotating platform is modeled as x1 x2 y = = 0 0 1 0 x1 x2 + 1 0 (4138) (4139)

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2 2 The initial conditions are x1 (0) N (0, B0 ) and x2 (0) N (0, R0 ) The stochastic process noise (t) and measurement noise are independent, white, and Gaussian with R ( ) = N 2 ( ) and R ( ) = K 2 ( ) The nomenclature and units related to the various symbols are shown in Table 43

Name: Stochastics Where to find: wwwstockchartscom or any chart program Default settings: 14 (%K), 3 (%D) Time period: Daily or weekly Favorite market: Choppy The Lighter Side: Stochastics, originally nicknamed The Drama Queen by author and trader Toni Turner, is even more excitable than RSI You ll need a firm and experienced hand to keep this duckling under control

Interpretation Random bias, bias, xed drift Rate random walk Angular random walk Rate ramp, Random ramp

SY (j )

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The Stochastics indicator, which moves between 0 to 100, helps determine if the market or individual stock is overbought or oversold, or if a trend is ending or beginning

K2 2

1 Type wwwstockchartscom in your Web address line (or open any chart program) 2 On the right side of the screen, type $SPX, the S&P 500 index 3 When the chart appears, click on one of the drop-down menus under Indicators Select Slow Stochastics from the list, and press the Update button (Hint: If RSI is still listed under Indicators, you can remove it by changing it to None It s not necessary to have both RSI and Stochastics on a chart)

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Table 43: Parameter de nitions for the gyro error model of eqns (4138 4139) The PSD SY (j ) is de ned in eqn (4140) Prior to estimating the model parameters K and N , the e ect of the initial conditions B0 and R0 must rst be removed For each sequence of available data Y = {y(tk )}M , the rst step is to remove the trend de ned k=1 by yk = b0 + r0 tk This can be performed by least squares curve tting as discussed in Section 532 Representing the data in Y as a column vector, we have that Y = A where 1 t1 b0 A = and = r0 1 tM Because the sampling times are unique, the matrix A has full row rank and b A A is nonsingular Therefore, the least squares estimate = [ 0 , r0 ] is computed as (see eqn (526) on p 176) = A A

Slow STO %K (14) %D (3) 7833, 8816 %K Line %D Line $SPX (Daily) 109531

Given a large set of instruments, with estimated for each, the distribution 2 2 of b0 and r0 could be estimated and the variance parameters B0 and R0 can be determined

8816 7833 50 20

S (w)

Figure 46: Power spectral density of eqn (4140) Given and the data set Y = {y(tk )}M , the sequence of residual k=1 measurements Y = {y(tk ) ( 0 + r0 tk )}M can be formed This process b k=1 is referred to as detrending and Y is the detrended data The covariance sequence RY ( ) is computed from Y under the ergodic assumption, and is computed as the Fourier transform of R ( ) Using the the PSD of Y Y results of Section 45, for B0 = R0 = 0, the transfer function model for the system of eqns (4138 4139) is 1 Y (s) = s 1 (s) (s)

4 Slow Stochastics will be located above or below the chart It should look something like Figure 35

Given that (t) and (t) are independent, the PSD of y is SY (j ) = N 2 + K2 2 (4140)

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