QuantitativeExhaustion

Wave Channel 3D

Wave Channel 3D
Built by Ricardo idea from JR & Aloakdutt from indieTrades Jan. 2010

This indicator is very easy to build. We utilize Moving Averages with a set multiplier and an offset. Specially we try to use Fibonacci sequence series numbers (1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144...) as time space and multiplier (default 89, 8). Also included is Donchian Channel to locate strong trends and possible future support - resistance.


Examples of support/resistance on chart.


Dominant Price Trends


Future Support Resistance


Comparing Fibonacci Series Time Space - Multiplier


When Comparing make note of confluence support/resistance showing up with Fibonacci Series
Example uses DC

When Comparing make note of confluence support/resistance showing up with Fibonacci Series
Example without DC / Smooth MA
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?
study(title="3D-Wave Channel", shorttitle="3D-WC", overlay=true)
timespace = input(1)
smooth = input(89)
offsetMultiplier=input(8)
useDonchianAverage = input(false)
src = input(ohlc4)
ma = useDonchianAverage ? avg(highest(smooth),lowest(smooth)) : ema(src, smooth)
plot(ma[0], color=black, offset=offsetMultiplier*01)
plot(ma[timespace*01], color=silver, offset=offsetMultiplier*02)
plot(ma[timespace*02], color=silver, offset=offsetMultiplier*03)
plot(ma[timespace*03], color=gray, offset=offsetMultiplier*04)
plot(ma[timespace*04], color=gray, offset=offsetMultiplier*05)
plot(ma[timespace*05], color=gray, offset=offsetMultiplier*06)
plot(ma[timespace*06], color=silver, offset=offsetMultiplier*07)
plot(ma[timespace*07], color=silver, offset=offsetMultiplier*08)
plot(ma[timespace*08], color=gray, offset=offsetMultiplier*09)
plot(ma[timespace*09], color=gray, offset=offsetMultiplier*10)
plot(ma[timespace*10], color=black, offset=offsetMultiplier*11)

max_ma()=>max(ma[timespace*10],max(ma[timespace*9],max(ma[timespace*8],max(ma[timespace*7],max(ma[timespace*6],max(ma[timespace*5],max(ma[timespace*4],max(ma[timespace*3],max(ma[timespace*2],max(ma[timespace*1], ma))))))))))
min_ma()=>min(ma[timespace*10],min(ma[timespace*9],min(ma[timespace*8],min(ma[timespace*7],min(ma[timespace*6],min(ma[timespace*5],min(ma[timespace*4],min(ma[timespace*3],min(ma[timespace*2],min(ma[timespace*1], ma))))))))))

top = highest(max_ma(), smooth)
bot = lowest(min_ma(), smooth)

plot(top, color=black, offset=offsetMultiplier)
plot(bot, color=black, offset=offsetMultiplier)