# [NM]Improved Linear Regression Bull and Bear Power v02

Hi guys, I'm back with a little improvement on the Bull and Bear Signal I published just last week thanks to some feedback I received from a couple of users, which is of course highly appreciated.

Here are the changes that have been implemented compared to v01 :
(version 1 is the top indicator, version 2 is the bottom one) in the chart above

• Formula adapted to calculate the signal if no data is available for either bull or bear
• Added the possibility to smoothen the signal using Arnaud Legroux Moving Average (the benefit of this is that it does not add any lag to the signal)

If you have any further ideas on how to improve the indicator or if you are happy with it and want to share your settings or rules of engagement, please feel free to share them below.

Oh, and don't forget to click that like button ! :)

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Open-source script

In true TradingView spirit, the author of this script has published it open-source, so traders can understand and verify it. Cheers to the author! You may use it for free, but reuse of this code in a publication is governed by House Rules. You can favorite it to use it on a chart.

Disclaimer

Want to use this script on a chart?
```//@version=2
// this code uses the Linear Regression Bull and Bear Power indicator created by RicardoSantos
// and adds a signal line
// Use : if signal line is changes color, you have your signal, green = buy, red = sell
// Advice : best used with a zero lag indicator like ZeroLagEMA_LB from LazyBear
// if price is above ZLEMA and signal = green => buy, price below ZLEMA and signal = red => sell
// ***** Changelog compared to v01 ******
// Adapted formula to calculate the signal in case there is no information for either bear or bull
// Added the possibility to smoothen the signal (this is done by a simple SMA)
study(title='[RS][NM]Improved Linear Regression Bull and Bear Power v02', shorttitle='BBP_NM_v02', overlay=false)
window = input(title='Lookback Window:', type=integer, defval=10)
smooth = input(title='Smooth ?', type=bool, defval=true)
smap = input(title='Smooth factor', type=integer, defval=5, minval=2, maxval=10)
sigma = input(title='Sigma', type=integer, defval=6)

f_exp_lr(_height, _length)=>
_ret = _height + (_height/_length)

h_value = highest(close, window)
l_value = lowest(close, window)

h_bar = n-highestbars(close, window)
l_bar = n-lowestbars(close, window)

bear = 0-(f_exp_lr(h_value-close, n-h_bar) > 0 ? f_exp_lr(h_value-close, n-h_bar) : 0)
bull = 0+(f_exp_lr(close-l_value, n-l_bar) > 0 ? f_exp_lr(close-l_value, n-l_bar) : 0)
direction = smooth ? alma(bull + bear, smap, 0.9, sigma) : bull*3 + bear*3
dcolor = smooth ? direction[0] > direction[1] ? green : direction[0] < direction[1] ? red : yellow : direction > bull ? green : direction < bear ? red : yellow

plot(title='Bear', series=bear, style=columns, color=maroon, transp=92)
plot(title='Bull', series=bull, style=columns, color=green, transp=92)
plot(title='Direction', series=direction, style=line, linewidth=3, color= dcolor)
plot(0,title='zero line', color=black, linewidth=2)

```