On second look, the coefficient may be quite important. As alpha ->1, the 
difference between the slow and the fast EMAs begins to shrink. The coefficient 
seems to counteract that trend. So, the derivative estimate is a little less 
dependent on the choice of alpha and more on the actual local variation.
From: Astor <[email protected]>
To: "[email protected]" <[email protected]>
Sent: Sunday, May 22, 2011 7:18 AM
Subject: Re: [JBookTrader] Re: Book trading discussion


There is one more difference in LaViola's apporach to that of JBT. The scaling 
coefficient alpha/(1- alpha) in front of the difference in EMAs in Eqn. 3. 
Would be interesing to see if that coefficient makes any difference in 
forecasting. 
From: nonlinear <[email protected]>
To: [email protected]
Cc: Astor <[email protected]>
Sent: Saturday, May 21, 2011 9:16 PM
Subject: Re: [JBookTrader] Re: Book trading discussion



On Saturday, May 21, 2011 4:24:32 PM UTC-4, Alexana wrote: 
The whole concept of LaViola's paper is estimation of local linear regression 
parameters. Of course the time series is not linear, so linear regression can 
only be used within some local region, where some linearity can be assumed. The 
smoothing constant alpha defines the size of the "local" region. Larger alpha 
assumes rapidly changing function and, therefore, very small "local" region. 
Equation 1 defines the fast EMA and Equation 2 the slow one. Equation 3 
computes the difference (derivative). Equation 5 is classic linear regression 
forecasting.
>
>

Indeed, LaViola's equation (3) is calculating the difference between a "fast" 
and "slow" smoothers. That's what JBT does in a number of its indicators, 
including Tension. There is a difference, however:

LaViola: derivative = EMA(N) - (EMA(N) of (EMA(N)), i.e. the difference between 
the EMA and the double-EMA
JBT: derivative = EMA(N) - EMA(M), where N < M

I'm going to try LaViola's approach to see if it improves my backtesting 
results. Will report here.


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