Trendline Regression TV -> PRT

• 10/30/2023 at 11:21 AM #223046
  ashehzi

  Hi PRC team, hope you guys are well. Thanks for whatever you have been doing wrt PRT/PRC.

  I’ve used Trend Line Regression indicator on TradingView (https://www.tradingview.com/script/tf2TTN6c/) and I think we PRT community will surely benefit from it. I appreciate is someone could convert it. As the source code is open, I am sharing here. It is combo of a shared library and the indicator code using that library.

  TV lib Code

  // This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/ // © dandrideng //@version=5 // @description least_squares_regression: Least squares regression algorithm library("least_squares_regression", overlay=true) // @function basic_lsr: Basic least squares regression algorithm // @param series int[] t: time scale value array corresponding to price // @param series float[] p: price scale value array corresponding to time // @param series int array_size: the length of regression array // @returns reg_slop, reg_intercept, reg_level, reg_stdev export basic_lsr(series int[] t, series float[] p, series int array_size) => p_t = array.new_float(array_size, 0) t_2 = array.new_float(array_size, 0) //init array for i = 0 to array_size - 1 array.set(p_t, i, array.get(p, i) * array.get(t, i)) array.set(t_2, i, array.get(t, i) * array.get(t, i)) //get slop and intercept sum_t = array.sum(t) sum_p = array.sum(p) sum_t_2 = array.sum(t_2) sum_p_t = array.sum(p_t) a = (array_size * sum_p_t - sum_p * sum_t) / (array_size * sum_t_2 - math.pow(sum_t, 2)) b = (sum_p - a * sum_t) / array_size //get regression prediction value _p = array.new_float(array_size, 0) for i = 0 to array_size - 1 array.set(_p, i, a * array.get(t, i) + b) //get regression level float r1 = 0 float r2 = 0 p_avg = array.avg(p) for i = 0 to array_size - 1 r1 += math.pow(array.get(_p, i) - p_avg, 2) r2 += math.pow(array.get(p, i) - p_avg, 2) r = -math.log10(1 - r1 / r2) //get regression standard devition _s = array.new_float(array_size, 0) for i = 0 to array_size - 1 array.set(_s, i, array.get(p, i) - array.get(_p, i)) s = array.stdev(_s) //return results [a, b, r, s] // @function top_trend_line_lsr: Trend line fitting based on least square algorithm // @param series int[] t: time scale value array corresponding to price // @param series float[] p: price scale value array corresponding to time // @param series int array_size: the length of regression array // @param string reg_type: regression type in 'top' and 'bottom' // @param series int max_iter: maximum fitting iterations // @param series int min_points: the threshold of regression point numbers // @returns reg_slop, reg_intercept, reg_level, reg_stdev, reg_point_num export trend_line_lsr(series int[] t, series float[] p, series int array_size, string reg_type, series int max_iter, series int min_points) => [a, b, r, s] = basic_lsr(t, p, array_size) x1 = array.get(t, 0) x2 = array.get(t, array_size - 1) n = array_size //Function arguments cannot be mutable, what the fuck! for i = 0 to max_iter - 1 //exit conditions if n < min_points break //init new tick and price array _t = array.new_int(n, 0) _p = array.new_float(n, 0) _n = 0 //fine the ground truth values that bigger than predictions for j = 0 to n - 1 if reg_type == 'top' if (a * array.get(t, j) + b) < array.get(p, j) array.set(_t, _n, array.get(t, j)) array.set(_p, _n, array.get(p, j)) _n += 1 else if reg_type == 'bottom' if (a * array.get(t, j) + b) > array.get(p, j) array.set(_t, _n, array.get(t, j)) array.set(_p, _n, array.get(p, j)) _n += 1 else break //exit if new array size is less than threshold if _n < min_points break //override result if r of new array is bigger than last array [_a, _b, _r, _s] = basic_lsr(_t, _p, _n) if _r > r x1 := array.get(_t, 0) x2 := array.get(_t, _n - 1) a := _a b := _b r := _r s := _s n := _n //after for loop [x1, x2, a, b, r, s, n]

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  // This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/ // © dandrideng   //@version=5   // @description least_squares_regression: Least squares regression algorithm library("least_squares_regression", overlay=true)   // @function basic_lsr: Basic least squares regression algorithm // @param series int[] t: time scale value array corresponding to price // @param series float[] p: price scale value array corresponding to time // @param series int array_size: the length of regression array // @returns reg_slop, reg_intercept, reg_level, reg_stdev export basic_lsr(series int[] t, series float[] p, series int array_size) => p_t = array.new_float(array_size, 0) t_2 = array.new_float(array_size, 0) //init array for i = 0 to array_size - 1     array.set(p_t, i, array.get(p, i) * array.get(t, i))     array.set(t_2, i, array.get(t, i) * array.get(t, i)) //get slop and intercept sum_t = array.sum(t) sum_p = array.sum(p) sum_t_2 = array.sum(t_2) sum_p_t = array.sum(p_t) a = (array_size * sum_p_t - sum_p * sum_t) / (array_size * sum_t_2 - math.pow(sum_t, 2)) b = (sum_p - a * sum_t) / array_size          //get regression prediction value     _p = array.new_float(array_size, 0)     for i = 0 to array_size - 1         array.set(_p, i, a * array.get(t, i) + b)          //get regression level     float r1 = 0     float r2 = 0     p_avg = array.avg(p)     for i = 0 to array_size - 1         r1 += math.pow(array.get(_p, i) - p_avg, 2)         r2 += math.pow(array.get(p, i) - p_avg, 2)     r = -math.log10(1 - r1 / r2)          //get regression standard devition     _s = array.new_float(array_size, 0)     for i = 0 to array_size - 1         array.set(_s, i, array.get(p, i) - array.get(_p, i))     s = array.stdev(_s) //return results [a, b, r, s]   // @function top_trend_line_lsr: Trend line fitting based on least square algorithm // @param series int[] t: time scale value array corresponding to price // @param series float[] p: price scale value array corresponding to time // @param series int array_size: the length of regression array // @param string reg_type: regression type in 'top' and 'bottom' // @param series int max_iter: maximum fitting iterations // @param series int min_points: the threshold of regression point numbers // @returns reg_slop, reg_intercept, reg_level, reg_stdev, reg_point_num export trend_line_lsr(series int[] t, series float[] p, series int array_size, string reg_type, series int max_iter, series int min_points) => [a, b, r, s] = basic_lsr(t, p, array_size) x1 = array.get(t, 0) x2 = array.get(t, array_size - 1) n = array_size //Function arguments cannot be mutable, what the fuck!   for i = 0 to max_iter - 1     //exit conditions      if n < min_points          break              //init new tick and price array         _t = array.new_int(n, 0)         _p = array.new_float(n, 0)         _n = 0                  //fine the ground truth values that bigger than predictions         for j = 0 to n - 1             if reg_type == 'top'                 if (a * array.get(t, j) + b) < array.get(p, j)                     array.set(_t, _n, array.get(t, j))                     array.set(_p, _n, array.get(p, j))                     _n += 1             else if reg_type == 'bottom'                 if (a * array.get(t, j) + b) > array.get(p, j)                     array.set(_t, _n, array.get(t, j))                     array.set(_p, _n, array.get(p, j))                     _n += 1             else                 break                  //exit if new array size is less than threshold         if _n < min_points             break                  //override result if r of new array is bigger than last array         [_a, _b, _r, _s] = basic_lsr(_t, _p, _n)         if _r > r             x1 := array.get(_t, 0)             x2 := array.get(_t, _n - 1)             a := _a             b := _b             r := _r             s := _s             n := _n          //after for loop     [x1, x2, a, b, r, s, n]

  Now below is indicator, above was using the lib.

  TV indi Code

  // This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/ // © dandrideng //@version=5 indicator(title="Trend Line Regression", shorttitle="TLR", overlay=true, max_bars_back=800, max_lines_count=400, max_labels_count=400) import dandrideng/least_squares_regression/1 as lsr //trend line regression lookback_left = input.int(defval=5, title="Pivot Lookback Left", group="Trend Line Regression") lookback_right = input.int(defval=5, title="Pivot Lookback Right", group="Trend Line Regression") reg_forward = input.int(defval=20, title="Max Lookback Forward", group="Trend Line Regression") min_length = input.int(defval=100, title="Min Regression Length", maxval=500, group="Trend Line Regression") max_length = input.int(defval=200, title="Max Regression Length", maxval=500, group="Trend Line Regression") length_step = input.int(defval=100, title="Regression Length Steps", group="Trend Line Regression") max_iteration = input.int(defval=4, title="Max Iterations of Regression", group="Trend Line Regression") max_points = input.int(defval=30, title="Max Point Numbers of Trend Line", group="Trend Line Regression") min_points = input.int(defval=3, title="Min Point Numbers of Trend Line", group="Trend Line Regression") min_reg_level = input.float(defval=4, title="Min Regression Level of Trend Line", group="Trend Line Regression") std_offset = input.float(defval=0.5, title="Multiply Regression Std", group="Trend Line Regression") top_line_color = input.color(defval=color.red, title="Color of Top Trend Line", group="Trend Line Regression") bottom_line_color = input.color(defval=color.green, title="Color of Bottom Trend Line", group="Trend Line Regression") extend_lines = input.bool(defval=true, title="Extend Lines", group="Trend Line Regression") ? extend.right : extend.none draw_pivot = input.bool(defval=false, title="Draw Piovt High/Low", group="Trend Line Regression") show_labels = input.bool(defval=false, title="Show Labels", group="Trend Line Regression") pivot_high = ta.pivothigh(high, lookback_left, lookback_right) pivot_low = ta.pivotlow(low, lookback_left, lookback_right) plot(draw_pivot ? pivot_high : na, offset=-lookback_left) plot(draw_pivot ? pivot_low : na, offset=-lookback_left) //check regression (functional) check_trend_line(start, end, type, a, b) => res = true for i = start + 1 to end - 1 yi = a * i + b yi_1 = a * (i - 1) + b if type == "top" and (close[i] > yi and close[i - 1] > yi_1) res := false break else if type == "bottom" and (close[i] < yi and close[i - 1] < yi_1) res := false break res //draw trend line regressions (functional) draw_trend_line(forward, length, type) => pivot_src = type == "top" ? pivot_high : pivot_low tick = array.new_int(max_points, 0) price = array.new_float(max_points, 0) num = 0 for i = 0 to length - 1 if not na(pivot_src[i + forward]) and num < max_points array.set(tick, num, i + lookback_left + forward) array.set(price, num, pivot_src[i + forward]) num := num + 1 if num > 0 [x2, x1, a, b, r, s, n] = lsr.trend_line_lsr(tick, price, num, type, max_iteration, min_points) y1 = x1 * a + b y2 = x2 * a + b o = s * std_offset if n > min_points and r > min_reg_level and check_trend_line(x1, x2, type, a, b) var line mid_line = na mid_line := line.new(x1=bar_index-x1, y1=y1, x2=bar_index-x2, y2=y2) line.set_width(mid_line, 1) line.set_color(mid_line, color.new(type == "top" ? top_line_color : bottom_line_color, 25)) line.set_extend(mid_line, extend_lines) line.set_style(mid_line, line.style_dashed) var line top_line = na top_line := line.new(x1=bar_index-x1, y1=y1+o, x2=bar_index-x2, y2=y2+o) line.set_width(top_line, 1) line.set_color(top_line, color.new(type == "top" ? top_line_color : bottom_line_color, 25)) line.set_extend(top_line, extend_lines) var line bottom_line = na bottom_line := line.new(x1=bar_index-x1, y1=y1-o, x2=bar_index-x2, y2=y2-o) line.set_width(bottom_line, 1) line.set_color(bottom_line, color.new(type == "top" ? top_line_color : bottom_line_color, 25)) line.set_extend(bottom_line, extend_lines) linefill.new(mid_line, top_line, color=color.new(type == "top" ? top_line_color : bottom_line_color, 90)) linefill.new(mid_line, bottom_line, color=color.new(type == "top" ? top_line_color : bottom_line_color, 90)) if show_labels var label reg_label = na reg_label := label.new(x=bar_index-x2, y=type == "top" ? y2+o : y2-o, textcolor=color.white) label.set_color(reg_label, color.new(type == "top" ? top_line_color : bottom_line_color, 30)) label.set_text(reg_label, "Level: " + str.tostring(r, "#.###") + "\nPoints: " + str.tostring(n, "#")) label.set_style(reg_label, type == "top" ? label.style_label_down : label.style_label_up) //delete all lines all_lines = line.all if array.size(all_lines) > 0 for i = 0 to array.size(all_lines) - 1 line.delete(array.get(all_lines, i)) //delete all labels all_labels = label.all if array.size(all_labels) > 0 for i = 0 to array.size(all_labels) - 1 label.delete(array.get(all_labels, i)) //trend line for l = min_length to max_length by length_step draw_trend_line(reg_forward, l, "top") draw_trend_line(reg_forward, l, "bottom") //end of file

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  // This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/ // © dandrideng   //@version=5 indicator(title="Trend Line Regression", shorttitle="TLR", overlay=true, max_bars_back=800, max_lines_count=400, max_labels_count=400)   import dandrideng/least_squares_regression/1 as lsr   //trend line regression lookback_left = input.int(defval=5, title="Pivot Lookback Left", group="Trend Line Regression") lookback_right = input.int(defval=5, title="Pivot Lookback Right", group="Trend Line Regression") reg_forward = input.int(defval=20, title="Max Lookback Forward", group="Trend Line Regression") min_length = input.int(defval=100, title="Min Regression Length", maxval=500, group="Trend Line Regression") max_length = input.int(defval=200, title="Max Regression Length", maxval=500, group="Trend Line Regression") length_step = input.int(defval=100, title="Regression Length Steps", group="Trend Line Regression") max_iteration = input.int(defval=4, title="Max Iterations of Regression", group="Trend Line Regression") max_points = input.int(defval=30, title="Max Point Numbers of Trend Line", group="Trend Line Regression") min_points = input.int(defval=3, title="Min Point Numbers of Trend Line", group="Trend Line Regression") min_reg_level = input.float(defval=4, title="Min Regression Level of Trend Line", group="Trend Line Regression") std_offset = input.float(defval=0.5, title="Multiply Regression Std", group="Trend Line Regression") top_line_color = input.color(defval=color.red, title="Color of Top Trend Line", group="Trend Line Regression") bottom_line_color = input.color(defval=color.green, title="Color of Bottom Trend Line", group="Trend Line Regression") extend_lines = input.bool(defval=true, title="Extend Lines", group="Trend Line Regression") ? extend.right : extend.none draw_pivot = input.bool(defval=false, title="Draw Piovt High/Low", group="Trend Line Regression") show_labels = input.bool(defval=false, title="Show Labels", group="Trend Line Regression")   pivot_high = ta.pivothigh(high, lookback_left, lookback_right) pivot_low = ta.pivotlow(low, lookback_left, lookback_right)   plot(draw_pivot ? pivot_high : na, offset=-lookback_left) plot(draw_pivot ? pivot_low : na, offset=-lookback_left)   //check regression (functional) check_trend_line(start, end, type, a, b) =>     res = true     for i = start + 1 to end - 1         yi = a * i + b         yi_1 = a * (i - 1) + b         if type == "top" and (close[i] > yi and close[i - 1] > yi_1)             res := false             break         else if type == "bottom" and (close[i] < yi and close[i - 1] < yi_1)             res := false             break          res      //draw trend line regressions (functional) draw_trend_line(forward, length, type) =>     pivot_src = type == "top" ? pivot_high : pivot_low       tick = array.new_int(max_points, 0)     price = array.new_float(max_points, 0)     num = 0       for i = 0 to length - 1         if not na(pivot_src[i + forward]) and num < max_points             array.set(tick, num, i + lookback_left + forward)             array.set(price, num, pivot_src[i + forward])             num := num + 1              if num > 0         [x2, x1, a, b, r, s, n] = lsr.trend_line_lsr(tick, price, num, type, max_iteration, min_points)         y1 = x1 * a + b         y2 = x2 * a + b         o = s * std_offset                  if n > min_points and r > min_reg_level and check_trend_line(x1, x2, type, a, b)             var line mid_line = na             mid_line := line.new(x1=bar_index-x1, y1=y1, x2=bar_index-x2, y2=y2)             line.set_width(mid_line, 1)             line.set_color(mid_line, color.new(type == "top" ? top_line_color : bottom_line_color, 25))             line.set_extend(mid_line, extend_lines)             line.set_style(mid_line, line.style_dashed)                      var line top_line = na             top_line := line.new(x1=bar_index-x1, y1=y1+o, x2=bar_index-x2, y2=y2+o)             line.set_width(top_line, 1)             line.set_color(top_line, color.new(type == "top" ? top_line_color : bottom_line_color, 25))             line.set_extend(top_line, extend_lines)                          var line bottom_line = na             bottom_line := line.new(x1=bar_index-x1, y1=y1-o, x2=bar_index-x2, y2=y2-o)             line.set_width(bottom_line, 1)             line.set_color(bottom_line, color.new(type == "top" ? top_line_color : bottom_line_color, 25))             line.set_extend(bottom_line, extend_lines)                          linefill.new(mid_line, top_line, color=color.new(type == "top" ? top_line_color : bottom_line_color, 90))             linefill.new(mid_line, bottom_line, color=color.new(type == "top" ? top_line_color : bottom_line_color, 90))                          if show_labels                 var label reg_label = na                 reg_label := label.new(x=bar_index-x2, y=type == "top" ? y2+o : y2-o, textcolor=color.white)                 label.set_color(reg_label, color.new(type == "top" ? top_line_color : bottom_line_color, 30))                 label.set_text(reg_label, "Level: " + str.tostring(r, "#.###") + "\nPoints: " + str.tostring(n, "#"))                 label.set_style(reg_label, type == "top" ? label.style_label_down : label.style_label_up)   //delete all lines all_lines = line.all if array.size(all_lines) > 0 for i = 0 to array.size(all_lines) - 1 line.delete(array.get(all_lines, i)) //delete all labels all_labels = label.all if array.size(all_labels) > 0 for i = 0 to array.size(all_labels) - 1 label.delete(array.get(all_labels, i))   //trend line for l = min_length to max_length by length_step     draw_trend_line(reg_forward, l, "top")     draw_trend_line(reg_forward, l, "bottom")          //end of file

  

  ashehzi

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  10/30/2023 at 11:31 AM #223047
  JC_Bywan

  Hi, I’ve added it to the list of conversions to assess. If someone “speaks” the TV language and wants to attempt the conversion before Nicolas is back, please feel free to proceed, thanks. (Edit: I didn’t phrase this very well, any translation contribution is welcome at any time, whether he’s here or not)

  

  JC_Bywan

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  01/22/2024 at 11:32 PM #226740
  ashehzi

  I feel it can be a good trading strat if  converted to PRT.

  

  ashehzi

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  01/24/2024 at 9:29 AM #226820
  LucasBest

  As this indicator use a lot of functions, it would be more easy to understand its spirit (how it works to determine supports and resistance) and then code it in PRT language than trying to convert it like it have been coded in Tradingview language.

  Also note that the use of linear regression is not mandatory here. I have done alsmost the same without using it.

  

  LucasBest

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