alexgrover

Kaufman Adaptive Least Squares Moving Average

Introduction

It is possible to use a wide variety of filters for the estimation of a least squares moving average , one of the them being the Kaufman adaptive moving average ( KAMA ) which adapt to the market trend strength, by using KAMA in an lsma we therefore allow for an adaptive low lag filter which might provide a smarter way to remove noise while preserving reactivity.

The Indicator

The lsma aim to minimize the sum of the squared residuals, paired with KAMA we obtain a great adaptive solution for smoothing while conserving reactivity. Length control the period of the efficiency ratio used in KAMA , higher values of length allow for overall smoother results. The pre-filtering option allow for even smoother results by using KAMA as input instead of the raw price.


The proposed indicator without pre-filtering in green, a simple moving average in orange, and a lsma with all of them length = 200. The proposed filter allow for fast and precise crosses with the moving average while eliminating major whipsaws.


Same setup with the pre-filtering option, the result are overall smoother.

Conclusion

The provided code allow for the implementation of any filter instead of KAMA , try using your own filters. Thanks for reading :)
从常用的脚本中删除 添加到常用的脚本
You can check my indicator papers here : https://figshare.com/authors/Alex_Pierrefeu/7339466
This is great! Thanks.
+1 回复
@stluvin, Thanks you a lot for the support, send me a pm if you have suggestions :)
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stluvin alexgrover
@alexgrover, It is perfect. Unfiltered/ filtered crossover is very good!
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@alexgrover sir superb.
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alexgrover shreyasimijo
@shreyasimijo, Thx for your comment :)
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@alexgrover , Impressive script !! Thanks for sharing.
+1 回复
@jsmehra, You are welcome :)
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Hi Alex,

Thanks for sharing!!!! It is Great!!!!
+1 回复
alexgrover sudhir.mehta
@sudhir.mehta, Thank you :)
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Great Indicator!!
+1 回复
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