Polynomial Regression Channel (PRC) / Quadratic Regression
- Thread starter Sean_C
- Start date
Solution
Does anyone have the TOS code to plot this? It would be a great first sort for prices near one of the extremes, then use the Ultimate Breakout indicator on a lower time frame and only take the buys when prices near bottom deviation and slope is positive and sells when at top deviation and negative slope.
@codydog- thanks for pointing me in a direction, I just couldn't find the direction you were pointing. I tried searching "growex" and "polynomial" on the onenote site I was familiar with with no luck. Can you point me a bit more specifically. Thanks in advance for your help.
Also, @RobertPayne- any chance of a discount to usethinkscript members for your indicators for sale on your site?
Also, @RobertPayne- any chance of a discount to usethinkscript members for your indicators for sale on your site?
codydog- I hate to seem stupid, but I searched for growex, xxx_VM, polynomial and still nothing shows. Am I looking in the right OneNote? I am using this one- ThinkScript Cloud
@hockeycoachdoug
See if this is what you're looking for. I found it in the Thinkscript Cloud.
If it works, please post a picture here!
See if this is what you're looking for. I found it in the Thinkscript Cloud.
Code:
# Quadratic Regression
# Robert Payne
# funwiththinkscript.com
input length = 20;
def n = length;
def bn = BarNumber();
def lastBar = HighestAll(if IsNaN(close) then 0 else bn);
def x = bn;
def y = close;
# calculate summation values
def startBar = lastBar - (n - 1);
def sumX = if bn < startBar then 0 else x + sumX[1];
def sumY = if bn < startBar then 0 else y + sumY[1];
def sumX2 = if bn < startBar then 0 else Power(x, 2) + sumX2[1];
def sumX3 = if bn < startBar then 0 else Power(x, 3) + sumX3[1];
def sumX4 = if bn < startBar then 0 else Power(x, 4) + sumX4[1];
def sumXY = if bn < startBar then 0 else x * y + sumXY[1];
def sumX2Y = if bn < startBar then 0 else Power(x, 2) * y + sumX2Y[1];
# intermediary calculations
def xx = sumX2 - Power(sumX, 2) / n;
def xy = sumXY - (sumX * sumY / n);
def xx2 = sumX3 - (sumX2 * sumX / n);
def x2y = sumX2Y - (sumX2 * sumY / n);
def x2x2 = sumX4 - (Power(sumX2, 2) / n);
# calculate coefficients for the quadratic equation
def a0 = (x2y * xx - xy * xx2) / (xx * x2x2 - Power(xx2, 2));
def b0 = (xy * x2x2 - x2y * xx2) / (xx * x2x2 - Power(xx2, 2));
def c0 = sumY / n - b0 * sumX / n - a0 * sumX2 / n;
# for a, b, and c use the final value on the last bar of the chart for all calculations
def a = GetValue(a0, bn - lastBar);
def b = GetValue(b0, bn - lastBar);
def c = GetValue(c0, bn - lastBar);
# calculate and plot the regression curve
plot theCurve = if bn < startBar then Double.NaN else a * Power(x, 2) + b * x + c;
#####If it works, please post a picture here!
Function Polynomial Regression For TradingView by RicardoSantos
https://www.tradingview.com/script/12M8Jqu6-Function-Polynomial-Regression/
This is the script from TV on PRC, would anyone be interested in port it to TOS?
https://www.tradingview.com/script/12M8Jqu6-Function-Polynomial-Regression/
This is the script from TV on PRC, would anyone be interested in port it to TOS?
Ruby:
/ This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © RicardoSantos
//@version=4
study(title="Function Polynomial Regression", overlay=true)
f_array_polyreg(_X, _Y)=>
//{
//| Returns a polynomial regression channel using (X,Y) vector points.
//| Resources:
//| language D: https://rosettacode.org/wiki/Polynomial_regression
int _sizeY = array.size(id=_Y)
int _sizeX = array.size(id=_X)
//
float _meanX = array.sum(id=_X) / _sizeX
float _meanY = array.sum(id=_Y) / _sizeX
float _meanXY = 0.0
float _meanY2 = 0.0
float _meanX2 = 0.0
float _meanX3 = 0.0
float _meanX4 = 0.0
float _meanX2Y = 0.0
if _sizeY == _sizeX
for _i = 0 to _sizeY - 1
float _Xi = array.get(id=_X, index=_i)
float _Yi = array.get(id=_Y, index=_i)
_meanXY := _meanXY + (_Xi * _Yi)
_meanY2 := _meanY2 + pow(_Yi, 2)
_meanX2 := _meanX2 + pow(_Xi, 2)
_meanX3 := _meanX3 + pow(_Xi, 3)
_meanX4 := _meanX4 + pow(_Xi, 4)
_meanX2Y := _meanX2Y + pow(_Xi, 2) * _Yi
_meanXY := _meanXY / _sizeX
_meanY2 := _meanY2 / _sizeX
_meanX2 := _meanX2 / _sizeX
_meanX3 := _meanX3 / _sizeX
_meanX4 := _meanX4 / _sizeX
_meanX2Y := _meanX2Y / _sizeX
//-----------|covs
float _sXX = _meanX2 - _meanX * _meanX
float _sXY = _meanXY - _meanX * _meanY
float _sXX2 = _meanX3 - _meanX * _meanX2
float _sX2X2 = _meanX4 - _meanX2 * _meanX2
float _sX2Y = _meanX2Y - _meanX2 * _meanY
//-----------|
float _b = (_sXY * _sX2X2 - _sX2Y * _sXX2) / (_sXX * _sX2X2 - _sXX2 * _sXX2)
float _c = (_sX2Y * _sXX - _sXY * _sXX2) / (_sXX * _sX2X2 - _sXX2 * _sXX2)
float _a = _meanY - _b * _meanX - _c * _meanX2
//-----------|
float[] _predictions = array.new_float(size=0, initial_value=0.0)
float _max_dev = 0.0
float _min_dev = 0.0
float _stdev = 0.0
for _i = 0 to _sizeX - 1
float _Xi = array.get(id=_X, index=_i)
float _vector = _a + _b * _Xi + _c * _Xi * _Xi
array.push(id=_predictions, value=_vector)
//
float _Yi = array.get(id=_Y, index=_i)
float _diff = _Yi - _vector
if _diff > _max_dev
_max_dev := _diff
if _diff < _min_dev
_min_dev := _diff
_stdev := _stdev + abs(_diff)
//| Output:
//| _predictions: Array with adjusted _Y values.
//| _max_dev: Max deviation from the mean.
//| _min_dev: Min deviation from the mean.
//| _stdev/_sizeX: Average deviation from the mean.
//}
[_predictions, _max_dev, _min_dev, _stdev/_sizeX]
int length = input(10)
var float[] prices = array.new_float(size=length, initial_value=open)
var int[] indices = array.new_int(size=length, initial_value=0)
if pivothigh(2, 2)
e = array.pop(id=prices)
i = array.pop(id=indices)
array.insert(id=prices, index=0, value=high[2])
array.insert(id=indices, index=0, value=bar_index[2])
if pivotlow(2, 2)
e = array.pop(id=prices)
i = array.pop(id=indices)
array.insert(id=prices, index=0, value=low[2])
array.insert(id=indices, index=0, value=bar_index[2])
[P, Pmax, Pmin, Pstdev] = f_array_polyreg(indices, prices)
//|----------------------------------------------------------------------------||
//|----------------------------------------------------------------------------||
//|----------------------------------------------------------------------------||
//|----------------------------------------------------------------------------||
//|----------------------------------------------------------------------------||
//|----------------------------------------------------------------------------||
color _pr_mid_col = input(color.blue)
color _pr_std_col = input(color.aqua)
color _pr_max_col = input(color.purple)
f_init_mid()=>line.new(x1=bar_index, y1=0.0, x2=bar_index, y2=0.0, color=_pr_mid_col, style=line.style_solid, width=2)
f_init_std()=>line.new(x1=bar_index, y1=0.0, x2=bar_index, y2=0.0, color=_pr_std_col, style=line.style_dashed, width=1)
f_init_max()=>line.new(x1=bar_index, y1=0.0, x2=bar_index, y2=0.0, color=_pr_max_col, style=line.style_dotted, width=1)
var line pr_mid00 = f_init_mid(), var line pr_min00 = f_init_max(), var line pr_max00 = f_init_max()
var line pr_mid01 = f_init_mid(), var line pr_min01 = f_init_max(), var line pr_max01 = f_init_max()
var line pr_mid02 = f_init_mid(), var line pr_min02 = f_init_max(), var line pr_max02 = f_init_max()
var line pr_mid03 = f_init_mid(), var line pr_min03 = f_init_max(), var line pr_max03 = f_init_max()
var line pr_mid04 = f_init_mid(), var line pr_min04 = f_init_max(), var line pr_max04 = f_init_max()
var line pr_mid05 = f_init_mid(), var line pr_min05 = f_init_max(), var line pr_max05 = f_init_max()
var line pr_mid06 = f_init_mid(), var line pr_min06 = f_init_max(), var line pr_max06 = f_init_max()
var line pr_mid07 = f_init_mid(), var line pr_min07 = f_init_max(), var line pr_max07 = f_init_max()
var line pr_mid08 = f_init_mid(), var line pr_min08 = f_init_max(), var line pr_max08 = f_init_max()
var line pr_mid09 = f_init_mid(), var line pr_min09 = f_init_max(), var line pr_max09 = f_init_max()
var line pr_lower00 = f_init_std(), var line pr_upper00 = f_init_std()
var line pr_lower01 = f_init_std(), var line pr_upper01 = f_init_std()
var line pr_lower02 = f_init_std(), var line pr_upper02 = f_init_std()
var line pr_lower03 = f_init_std(), var line pr_upper03 = f_init_std()
var line pr_lower04 = f_init_std(), var line pr_upper04 = f_init_std()
var line pr_lower05 = f_init_std(), var line pr_upper05 = f_init_std()
var line pr_lower06 = f_init_std(), var line pr_upper06 = f_init_std()
var line pr_lower07 = f_init_std(), var line pr_upper07 = f_init_std()
var line pr_lower08 = f_init_std(), var line pr_upper08 = f_init_std()
var line pr_lower09 = f_init_std(), var line pr_upper09 = f_init_std()
f_pr_mid_line_selector(_i)=>(_i==0?pr_mid00:(_i==1?pr_mid01:(_i==2?pr_mid02:(_i==3?pr_mid03:(_i==4?pr_mid04:(_i==5?pr_mid05:(_i==6?pr_mid06:(_i==7?pr_mid07:(_i==8?pr_mid08:(_i==9?pr_mid09:pr_mid00))))))))))
f_pr_max_line_selector(_i)=>(_i==0?pr_max00:(_i==1?pr_max01:(_i==2?pr_max02:(_i==3?pr_max03:(_i==4?pr_max04:(_i==5?pr_max05:(_i==6?pr_max06:(_i==7?pr_max07:(_i==8?pr_max08:(_i==9?pr_max09:pr_max00))))))))))
f_pr_min_line_selector(_i)=>(_i==0?pr_min00:(_i==1?pr_min01:(_i==2?pr_min02:(_i==3?pr_min03:(_i==4?pr_min04:(_i==5?pr_min05:(_i==6?pr_min06:(_i==7?pr_min07:(_i==8?pr_min08:(_i==9?pr_min09:pr_min00))))))))))
f_pr_upper_line_selector(_i)=>(_i==0?pr_upper00:(_i==1?pr_upper01:(_i==2?pr_upper02:(_i==3?pr_upper03:(_i==4?pr_upper04:(_i==5?pr_upper05:(_i==6?pr_upper06:(_i==7?pr_upper07:(_i==8?pr_upper08:(_i==9?pr_upper09:pr_upper00))))))))))
f_pr_lower_line_selector(_i)=>(_i==0?pr_lower00:(_i==1?pr_lower01:(_i==2?pr_lower02:(_i==3?pr_lower03:(_i==4?pr_lower04:(_i==5?pr_lower05:(_i==6?pr_lower06:(_i==7?pr_lower07:(_i==8?pr_lower08:(_i==9?pr_lower09:pr_lower00))))))))))
int pr_fractions = 10
int pr_size = array.size(id=P)
int pr_step = max(pr_size / pr_fractions, 1)
for _i = 0 to pr_size - pr_step - 1 by pr_step
int _next_step_index = _i + pr_step
int _line = _i / pr_step
line.set_xy1(id=f_pr_mid_line_selector(_line), x=array.get(id=indices, index=_i), y=array.get(id=P, index=_i))
line.set_xy2(id=f_pr_mid_line_selector(_line), x=array.get(id=indices, index=_i + pr_step), y=array.get(id=P, index=_i + pr_step))
line.set_xy1(id=f_pr_max_line_selector(_line), x=array.get(id=indices, index=_i), y=array.get(id=P, index=_i) + Pmax)
line.set_xy2(id=f_pr_max_line_selector(_line), x=array.get(id=indices, index=_i + pr_step), y=array.get(id=P, index=_i + pr_step) + Pmax)
line.set_xy1(id=f_pr_min_line_selector(_line), x=array.get(id=indices, index=_i), y=array.get(id=P, index=_i) + Pmin)
line.set_xy2(id=f_pr_min_line_selector(_line), x=array.get(id=indices, index=_i + pr_step), y=array.get(id=P, index=_i + pr_step) + Pmin)
line.set_xy1(id=f_pr_upper_line_selector(_line), x=array.get(id=indices, index=_i), y=array.get(id=P, index=_i) + Pstdev)
line.set_xy2(id=f_pr_upper_line_selector(_line), x=array.get(id=indices, index=_i + pr_step), y=array.get(id=P, index=_i + pr_step) + Pstdev)
line.set_xy1(id=f_pr_lower_line_selector(_line), x=array.get(id=indices, index=_i), y=array.get(id=P, index=_i) - Pstdev)
line.set_xy2(id=f_pr_lower_line_selector(_line), x=array.get(id=indices, index=_i + pr_step), y=array.get(id=P, index=_i + pr_step) - Pstdev)
Last edited by a moderator:
I was looking for a PRC but not seeing one? Don Monkey made one but it's not shared. It's a fancy LRC that fits the price better. I am a mean reversion trader so I would use them for range estimates.
MountainKing333
Member
Alright so I suck at calculus and I'm even worse at coding. Can anyone find a way to expand this Quadratic Regression Line code into Cubic and or Quartic regression? I have absolutely no clue how to start, and I cant find any code online that can even be adapted to do this. Please if anyone can help, I think this would be a great option to have.
Code:
# Quadratic Regression
# Robert Payne
# funwiththinkscript.com
input length = 20;
def n = length;
def bn = BarNumber();
def lastBar = HighestAll(if IsNaN(close) then 0 else bn);
def x = bn;
def y = close;
# calculate summation values
def startBar = lastBar - (n - 1);
def sumX = if bn < startBar then 0 else x + sumX[1];
def sumY = if bn < startBar then 0 else y + sumY[1];
def sumX2 = if bn < startBar then 0 else Power(x, 2) + sumX2[1];
def sumX3 = if bn < startBar then 0 else Power(x, 3) + sumX3[1];
def sumX4 = if bn < startBar then 0 else Power(x, 4) + sumX4[1];
def sumXY = if bn < startBar then 0 else x * y + sumXY[1];
def sumX2Y = if bn < startBar then 0 else Power(x, 2) * y + sumX2Y[1];
# intermediary calculations
def xx = sumX2 - Power(sumX, 2) / n;
def xy = sumXY - (sumX * sumY / n);
def xx2 = sumX3 - (sumX2 * sumX / n);
def x2y = sumX2Y - (sumX2 * sumY / n);
def x2x2 = sumX4 - (Power(sumX2, 2) / n);
# calculate coefficients for the quadratic equation
def a0 = (x2y * xx - xy * xx2) / (xx * x2x2 - Power(xx2, 2));
def b0 = (xy * x2x2 - x2y * xx2) / (xx * x2x2 - Power(xx2, 2));
def c0 = sumY / n - b0 * sumX / n - a0 * sumX2 / n;
# for a, b, and c use the final value on the last bar of the chart for all calculations
def a = GetValue(a0, bn - lastBar);
def b = GetValue(b0, bn - lastBar);
def c = GetValue(c0, bn - lastBar);
# calculate and plot the regression curve
plot theCurve = if bn < startBar then Double.NaN else a * Power(x, 2) + b * x + c;
#####thinkorswimmer22
New member
Someone would have to code a design matrix from linear algebra. I don't know how to do that, but I tried.
MountainKing333
Member
I feel like it would just require one or two more lines of code per stage in the quadratic regression I posted above no?
Similar threads
-
How do I take One Point of Linear Regression Slope and Have it divide it by closing price of that candle
- Started by uunnamed
- Replies: 2
-
-
-
Linear Regression Fun -- Excursion from the Centerline For ThinkOrSwim
- Started by mashume
- Replies: 0
-