HPotter

T3 Average

This indicator plots the moving average described in the January, 1998 issue
of S&C, p.57, "Smoothing Techniques for More Accurate Signals", by Tim Tillson.
This indicator plots T3 moving average presented in Figure 4 in the article.
T3 indicator is a moving average which is calculated according to formula:
T3(n) = GD ( GD ( GD (n))),
where GD - generalized DEMA ( Double EMA ) and calculating according to this:
GD (n,v) = EMA (n) * (1+v)-EMA( EMA (n)) * v,
where "v" is volume factor, which determines how hot the moving average’s response
to linear trends will be. The author advises to use v=0.7.
When v = 0, GD = EMA , and when v = 1, GD = DEMA . In between, GD is a less aggressive
version of DEMA . By using a value for v less than1, trader cure the multiple DEMA
overshoot problem but at the cost of accepting some additional phase delay.
In filter theory terminology, T3 is a six-pole nonlinear Kalman filter. Kalman
filters are ones that use the error — in this case, (time series - EMA (n)) —
to correct themselves. In the realm of technical analysis , these are called adaptive
moving averages; they track the time series more aggres-sively when it is making large
moves. Tim Tillson is a software project manager at Hewlett-Packard, with degrees in
mathematics and computer science. He has privately traded options and equities for 15 years.

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오픈 소스 스크립트

이 스크립트의 오써는 참된 트레이딩뷰의 스피릿으로 이 스크립트를 오픈소스로 퍼블리쉬하여 트레이더들로 하여금 이해 및 검증할 수 있도록 하였습니다. 오써를 응원합니다! 스크립트를 무료로 쓸 수 있지만, 다른 퍼블리케이션에서 이 코드를 재사용하는 것은 하우스룰을 따릅니다. 님은 즐겨찾기로 이 스크립트를 차트에서 쓸 수 있습니다.

면책사항

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차트에 이 스크립트를 사용하시겠습니까?
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 21/05/2014
// This indicator plots the moving average described in the January, 1998 issue
// of S&C, p.57, "Smoothing Techniques for More Accurate Signals", by Tim Tillson.
// This indicator plots T3 moving average presented in Figure 4 in the article.
// T3 indicator is a moving average which is calculated according to formula:
//     T3(n) = GD(GD(GD(n))),
// where GD - generalized DEMA (Double EMA) and calculating according to this:
//     GD(n,v) = EMA(n) * (1+v)-EMA(EMA(n)) * v,
// where "v" is volume factor, which determines how hot the moving average’s response
// to linear trends will be. The author advises to use v=0.7.
// When v = 0, GD = EMA, and when v = 1, GD = DEMA. In between, GD is a less aggressive
// version of DEMA. By using a value for v less than1, trader cure the multiple DEMA
// overshoot problem but at the cost of accepting some additional phase delay.
// In filter theory terminology, T3 is a six-pole nonlinear Kalman filter. Kalman
// filters are ones that use the error — in this case, (time series - EMA(n)) — 
// to correct themselves. In the realm of technical analysis, these are called adaptive
// moving averages; they track the time series more aggres-sively when it is making large
// moves. Tim Tillson is a software project manager at Hewlett-Packard, with degrees in
// mathematics and computer science. He has privately traded options and equities for 15 years.   
////////////////////////////////////////////////////////////
study(title="T3 Averages", shorttitle="T3", overlay = true)
Length = input(5, minval=1)
xPrice = close
xe1 = ema(xPrice, Length)
xe2 = ema(xe1, Length)
xe3 = ema(xe2, Length)
xe4 = ema(xe3, Length)
xe5 = ema(xe4, Length)
xe6 = ema(xe5, Length)
b = 0.7
c1 = -b*b*b
c2 = 3*b*b+3*b*b*b
c3 = -6*b*b-3*b-3*b*b*b
c4 = 1+3*b+b*b*b+3*b*b
nT3Average = c1 * xe6 + c2 * xe5 + c3 * xe4 + c4 * xe3
plot(nT3Average, color=blue, title="T3")

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