Trendline Regression TV -> PRT

• 11/07/2022 at 1:24 PM #203758
  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 Library 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]

   

  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

  Participant

  Topics: 29

  Replies: 88

  Been thanked: 9 times
  11/10/2022 at 12:32 PM #203938
  ashehzi

  Hi Team,

  appreciate any input or pointers? I can try myself but could you please see if it is doable in PRC and what do I need to avoid/use?

  Regards

  Ash

  

  ashehzi

  Participant

  Topics: 29

  Replies: 88

  Been thanked: 9 times
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