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
開源腳本

本著真正的TradingView精神,該腳本的作者將其開源發布,以便交易者可以理解和驗證它。為作者喝彩吧!您可以免費使用它,但在出版物中重複使用此代碼受網站規則的約束。 您可以收藏它以在圖表上使用。

免責聲明

這些資訊和出版物並不意味著也不構成TradingView提供或認可的金融、投資、交易或其他類型的意見或建議。請在使用條款閱讀更多資訊。

想在圖表上使用此腳本?
//
// @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)