LazyBear

[REPOST] Indicators: 3 Different Adaptive Moving Averages

*** NOTE: This is a repost with updated scripts to workaround the recent script engine changes ****

As the volatility rises, all Adaptive Moving Averages (AMA) become more sensitive and adapt faster to the price changes. As the volatility decreases, they slow down significantly compared to normal EMA. This makes it an excellent choice for detecting ranging markets (look for horizontal lines).

I have included 3 AMAs here:
- Kaufman's AMA. This makes use of Kaufman's Efficiency Ratio as the smoothing constant.
- Adaptive RSI. This adapts standard RSI to a smoothing constant.
- Tushar Chande's Variable Index Dynamic Average (VIDYA). This uses a pivotal smoothing constant, which is fixed, and varies the speed by using a factor based on the relative volatility to increase or decrease the value of SC.

For reference, I have plotted an EMA(10). This uses a fixed smoothing constant.

This is my 25th indicators post (Yayy!), so decided to include a bunch of AMAs. Enjoy :)

Feel free to "Make mine" and use these in your charts. Appreciate any comments / feedback.

List of my free indicators: bit.ly/1LQaPK8
List of my indicators at Appstore: blog.tradingview.com/?p=970
Script de código abierto

Siguiendo el verdadero espíritu de TradingView, el autor de este script lo ha publicado en código abierto, para que los traders puedan entenderlo y verificarlo. ¡Un hurra por el autor! Puede utilizarlo de forma gratuita, aunque si vuelve a utilizar este código en una publicación, debe cumplir con lo establecido en las Normas internas. Puede añadir este script a sus favoritos y usarlo en un gráfico.

Exención de responsabilidad

La información y las publicaciones que ofrecemos, no implican ni constituyen un asesoramiento financiero, ni de inversión, trading o cualquier otro tipo de consejo o recomendación emitida o respaldada por TradingView. Puede obtener información adicional en las Condiciones de uso.

¿Quiere utilizar este script en un gráfico?
//
// @author LazyBear
//
// v2 - updated the scripts to workaround function array indexing issues in the latest TV engine. 
// v1 - initial
//
study(title = "Kaufman Adaptive Moving Average [LazyBear]", shorttitle="KAMA2_LB", overlay=true)
amaLength = input(10, title="Length")
fastend=input(0.666)
slowend=input(0.0645)

diff=abs(close[0]-close[1])
signal=abs(close-close[amaLength])
noise=sum(diff, amaLength)
efratio=noise!=0 ? signal/noise : 1

smooth=pow(efratio*(fastend-slowend)+slowend,2)
kama=nz(kama[1], close)+smooth*(close-nz(kama[1], close))
plot( kama, color=green, linewidth=3)