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 :)
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Comments

This is great! Thanks.
+1 Reply
alexgrover stluvin
@stluvin, Thanks you a lot for the support, send me a pm if you have suggestions :)
Reply
stluvin alexgrover
@alexgrover, It is perfect. Unfiltered/ filtered crossover is very good!
Reply
@alexgrover sir superb.
+1 Reply
alexgrover BavarianMan
@BavarianMan, Thx for your comment :)
Reply
@alexgrover , Impressive script !! Thanks for sharing.
+1 Reply
alexgrover jsmehra
@jsmehra, You are welcome :)
Reply
Hi Alex,

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