I already mentioned various problems associated with the , one of them being overshoots, so here i propose to use an using a developed and adaptive form of 1st order polynomial to provide several improvements to the . This indicator will adapt to various coefficient of determinations while also using various recursions.
More In Depth
A 1st order polynomial is in the form : y = ax + b , our indicator however will use : y = a*x + a1*x1 + (1 - (a + a1))*y, where a is the coefficient of determination of a simple and a1 the coefficient of determination of an who try to best fit y to the price.
In some cases the coefficient of determination or is simply the squared correlation between the input and the . The can tell you if something is trending or not because its the correlation between the rough price containing noise and an estimate of the trend ( ). Therefore the filter give more weight to x or x1 based on their respective , when both is low the filter give more weight to its precedent output value.
lsma and R2 with both length = 100
The result of the R2 is rougher, faster, have less overshoot than the and also adapt to market conditions.
Longer/Shorter terms period can increase the error compared to the because of the R2 trying to adapt to the . The R2 can also provide good fits when there is an edge, this is due to the part where the fit the filter output to the input (y2)
I presented a new kind of that adapt itself to various coefficient of determination. The indicator can reduce the sum of squares because of its ability to reduce overshoot as well as remaining stationary when price is not trending. It can be interesting to apply exponential averaging with various smoothing constant as long as you use : (1- (alpha+alpha1)) at the end.
Thanks for reading
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You can also check out some of the indicators I made for luxalgo : https://www.tradingview.com/u/LuxAlgo/#published-scripts