OPEN-SOURCE SCRIPT
已更新 Falling-Rising Filter

Introduction
This is a modification of an old indicator i made. This filter aim to adapt to market trend by creating a smoothing constant using highest and lowest functions. This filter is visually similar to the edge-preserving filter, this similarity can make this filter quite good for MA cross strategies.
On The Filter Code
a = nz(a[1]) + alpha*nz(error[1]) + beta*nz(error[1])
The first 3 terms describe a simple exponential filter where error = price - a, beta introduce the adaptive part. beta is equal to 1 when the price is greater or lower than any past price over length period, else beta is equal to alpha, someone could ask why we use two smoothing variable (alpha, beta) instead of only beta thus having :
a = nz(a[1]) + beta*nz(error[1])
well alpha make the filter converge faster to the price thus having a better estimation.

In blue the filter using only beta and in red the filter using alpha and beta with both length = 200, the red filter converge faster to the price, if you need smoother results but less precise estimation only use beta.
Conclusion
I have presented a simple indicator using rising/falling functions to calculate an adaptive filter, this also show that when you create an exponential filter you can use more terms instead of only a = a[1] + alpha*(price - a[1]). I hope you find this indicator useful.
Thanks for reading !
This is a modification of an old indicator i made. This filter aim to adapt to market trend by creating a smoothing constant using highest and lowest functions. This filter is visually similar to the edge-preserving filter, this similarity can make this filter quite good for MA cross strategies.
On The Filter Code
a = nz(a[1]) + alpha*nz(error[1]) + beta*nz(error[1])
The first 3 terms describe a simple exponential filter where error = price - a, beta introduce the adaptive part. beta is equal to 1 when the price is greater or lower than any past price over length period, else beta is equal to alpha, someone could ask why we use two smoothing variable (alpha, beta) instead of only beta thus having :
a = nz(a[1]) + beta*nz(error[1])
well alpha make the filter converge faster to the price thus having a better estimation.
In blue the filter using only beta and in red the filter using alpha and beta with both length = 200, the red filter converge faster to the price, if you need smoother results but less precise estimation only use beta.
Conclusion
I have presented a simple indicator using rising/falling functions to calculate an adaptive filter, this also show that when you create an exponential filter you can use more terms instead of only a = a[1] + alpha*(price - a[1]). I hope you find this indicator useful.
Thanks for reading !
版本注释
Fixed a redundant error, thanks to aaahopper for pointing it out.开源脚本
本着TradingView的真正精神,此脚本的创建者将其开源,以便交易者可以查看和验证其功能。向作者致敬!虽然您可以免费使用它,但请记住,重新发布代码必须遵守我们的网站规则。
Check out the indicators we are making at luxalgo: tradingview.com/u/LuxAlgo/
"My heart is so loud that I can't hear the fireworks"
"My heart is so loud that I can't hear the fireworks"
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。
开源脚本
本着TradingView的真正精神,此脚本的创建者将其开源,以便交易者可以查看和验证其功能。向作者致敬!虽然您可以免费使用它,但请记住,重新发布代码必须遵守我们的网站规则。
Check out the indicators we are making at luxalgo: tradingview.com/u/LuxAlgo/
"My heart is so loud that I can't hear the fireworks"
"My heart is so loud that I can't hear the fireworks"
免责声明
这些信息和出版物并不意味着也不构成TradingView提供或认可的金融、投资、交易或其它类型的建议或背书。请在使用条款阅读更多信息。