One of the main reason I changed it is because, Slope calculation on transition period was not being computed properly. Because the Version 1, looks back the length assigned, and compute the slope based on two candle readings, could be 10 days apart or 50. That was misleading.
Therefore, I changed it to plot daily slope and Smooth it with an .
List of All my Indicators - https://www.tradingview.com/p/stocks/?so...
// Created by UCSgears -- Version 2 // Simple linear regression slope - Good way see if the trend is accelarating or decelarating study(title="UCSGEARS - Linear Regression Slope", shorttitle="UCS-LRS", overlay=false) src = close //Input clen = input (defval = 50, minval = 1, title = "Curve Length") slen = input(defval=5, minval=1, title="Slope Length") glen = input(defval=13, minval=1, title="Signal Length") //Linear Regression Curve lrc = linreg(src, clen, 0) //Linear Regression Slope lrs = (lrc-lrc)/1 //Smooth Linear Regression Slope slrs = ema(lrs, slen) //Signal Linear Regression Slope alrs = sma(slrs, glen) //loalrs = sma(slrs, (glen*5)) uacce = lrs > alrs and lrs > 0 dacce = lrs < alrs and lrs < 0 scolor = uacce ? green : dacce ? red : blue plot(0, title = "Zero Line", color = gray) plot(slrs, color = scolor, title = "Linear Regression Slope", style = histogram, linewidth = 4) plot(alrs, color = gray, title = "Average Slope")
It also depends on the price on the price action. (Steady vs Parabolic)
If you use 20, the LRC (linear regression curve) is more sensitive to tick reading. Creating quicker reaction on the SLOPE.
Remember the equation for linear slope is (y=mx+b) y = y axis, x = x axis, b is just a constant, m is the slope.
Where as for parabolic movers the equation changes by applying integration to this equation to smooth out the Parabolic mover. Its too much of math for those handful of parabolic movers out there. Simple solution would be expand the time frame (reduce it to lower timeframe) and look out for loss of momentum there.
Two highlighted region - The Parabolic Shoot up is not smooth, while the Drop was more linear.