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. -- You received this message because you are subscribed to the Google Groups "JBookTrader" group. To post to this group, send email to [email protected]. To unsubscribe from this group, send email to [email protected]. For more options, visit this group at http://groups.google.com/group/jbooktrader?hl=en.
