안녕하세요 예스스탁입니다. 업무상 시간이 많이 요구되는 내용은 작성해 드리기 어렵습니다. 수식내용상 오실레이터만 표시되게 변환해 드립니다. 1 input : length(20); input : upper(1.5); input : lower(-1.5); var : source(0),n(0),sum_x(0),sum_y(0),sum_xy(0),sum_x_squared(0),i(0); var : mm(0),cc(0),linear_regression_osc(0),linear_regression(0); source = c; n = length; sum_x = 0.0; sum_y = 0.0; sum_xy = 0.0; sum_x_squared = 0.0; for i = 0 to n - 1 { sum_x = sum_x + i; sum_y = sum_y + source[i]; sum_xy = sum_xy + i * source[i]; sum_x_squared = sum_x_squared + i * i; mm = (n * sum_xy - sum_x * sum_y) / (n * sum_x_squared - sum_x * sum_x); cc = (sum_y - mm * sum_x) / n; linear_regression_osc = ((mm * Index+ cc)*-1); } linear_regression = linear_regression_osc; var1 = (linear_regression - ma(linear_regression, 100)) / std(linear_regression, 100); Plot1(var1); PlotBaseLine1(0); PlotBaseLine2(upper); PlotBaseLine3(lower); 2 input : length(20); input : upper(1.5); input : lower(-1.5); var : source(0),n(0),sum_x(0),sum_y(0),sum_xy(0),sum_x_squared(0),i(0); var : mm(0),cc(0),linear_regression_osc(0),linear_regression(0); source = c; n = length; sum_x = 0.0; sum_y = 0.0; sum_xy = 0.0; sum_x_squared = 0.0; for i = 0 to n - 1 { sum_x = sum_x + i; sum_y = sum_y + source[i]; sum_xy = sum_xy + i * source[i]; sum_x_squared = sum_x_squared + i * i; mm = (n * sum_xy - sum_x * sum_y) / (n * sum_x_squared - sum_x * sum_x); cc = (sum_y - mm * sum_x) / n; linear_regression_osc = ((mm * Index+ cc)*-1); } linear_regression = linear_regression_osc; var1 = (linear_regression - ma(linear_regression, 100)) / std(linear_regression, 100); if crossup(var1,0) then find(1); 즐거운 하루되세요 > hanoi250 님이 쓴 글입니다. > 제목 : 문의 드립니다. > 해외 유튜브 [chart prime]에서 소개된 하기 내용을 다음 2가지로 부탁 드립니다. 1)Yes Language 지표로 변환 2)예스 트레이더 종목 검색 부탁 드립니다. =====하기==== Linear Regression Oscillator [ChartPrime] indicator("Linear Regression Oscillator [ChartPrime]", overlay=false, max_labels_count = 500) // ---------------------------------------------------------------------------------------------------------------------} // &#120400;&#120398;&#120384;&#120397; &#120388;&#120393;&#120395;&#120400;&#120399;&#120398; // ---------------------------------------------------------------------------------------------------------------------{ int length = input.int(20, "Length") float upper = input.float(1.5, "Upper Threshold", step = 0.1) float lower = input.float(-1.5, "Lower Threshold", step = 0.1) bool candles = input.bool(false, "Plot Bar Color") color col_up = #10cab8 color col_dn = color.blue // Source series float source = close[barstate.isconfirmed ? 0 : 1] // Max and Min prices for Invalidation Levels (period 5 bars back) var lowest = array.new<float>(5) var highest = array.new<float>(5) var series float points = na // ---------------------------------------------------------------------------------------------------------------------} // &#120388;&#120393;&#120383;&#120388;&#120382;&#120380;&#120399;&#120394;&#120397; &#120382;&#120380;&#120391;&#120382;&#120400;&#120391;&#120380;&#120399;&#120388;&#120394;&#120393;&#120398; // ---------------------------------------------------------------------------------------------------------------------{ //@function Calculation of slope and intercept //@param length LookBack Period //@returns Linear Regression Oscillator linear_regression_osc(int length)=> n = length sum_x = 0.0 sum_y = 0.0 sum_xy = 0.0 sum_x_squared = 0.0 for i = 0 to n - 1 sum_x := sum_x + i sum_y := sum_y + source[i] sum_xy := sum_xy + i * source[i] sum_x_squared := sum_x_squared + i * i m = (n * sum_xy - sum_x * sum_y) / (n * sum_x_squared - sum_x * sum_x) c = (sum_y - m * sum_x) / n linear_regression = ((m * bar_index + c)*-1) // Calculate linear regression Oscillator linear_regression = linear_regression_osc(length) // Normaliztion linear_regression := (linear_regression - ta.sma(linear_regression, 100)) / ta.stdev(linear_regression, 100) // Conditions of Mean Reversion and Trends cond1 = ta.crossunder(linear_regression, 0) cond2 = ta.crossover( linear_regression, 0) cond_1 = ta.crossunder(linear_regression, linear_regression[2]) and linear_regression > upper ? linear_regression[1] : na cond_2 = ta.crossover(linear_regression, linear_regression[2]) and linear_regression < lower ? linear_regression[1] : na // Invalidation Price Levels lowest. push(low) highest.push(high) switch lowest.size() > 5 => lowest.shift(), highest.shift() switch cond1 => points := highest.max() cond2 => points := lowest. min() // ---------------------------------------------------------------------------------------------------------------------} // &#120401;&#120388;&#120398;&#120400;&#120380;&#120391;&#120388;&#120405;&#120380;&#120399;&#120388;&#120394;&#120393; // ---------------------------------------------------------------------------------------------------------------------{ // Defined Colors color = linear_regression > 0 ? color.from_gradient(linear_regression, 0, 2, color(na), col_up) : color.from_gradient(linear_regression, -2, 0, col_dn, color(na)) color1= linear_regression > 0 ? color.from_gradient(linear_regression, 0, 2, color(na), color.new(chart.fg_color, 50)) : color.from_gradient(linear_regression, -2, 0, color.new(chart.fg_color, 50), color(na)) color2= linear_regression > 0 ? color.from_gradient(linear_regression, 0, 1, color(na), col_up) : color.from_gradient(linear_regression, -1, 0, col_dn, color(na)) // Plot Oscillator plot(linear_regression, color = color, style = plot.style_area) // Zero Line plot(0, color = color1, linewidth = 2) // Upper and Lower Thresholds p1 = plot(upper, display = display.none) p3 = plot(lower, display = display.none) p4 = plot(-4, display = display.none) p2 = plot(4, display = display.none) fill(p1, p2, 4, upper, na, color.new(col_up, 75)) fill(p3, p4, lower, -4, color.new(col_dn, 75), na) if barstate.islast l1 = label.new(bar_index, upper, str.tostring(upper), style = label.style_diamond, size = size.tiny, textcolor = chart.fg_color, color = col_up) label.delete(l1[1]) l2 = label.new(bar_index, lower, str.tostring(lower), style = label.style_diamond, size = size.tiny, textcolor = chart.fg_color, color = col_dn) label.delete(l2[1]) // Oscillator Mean Reversion above or below Thresholds plotchar(cond_1, "", "◇", color = col_dn, location = location.absolute, size = size.small, offset = -1 ) plotchar(cond_2, "", "◇", color = col_up, location = location.absolute, size = size.small, offset = -1 ) // Oscillator Crosses zero Line plotchar(cond1 ? 0 : na, "", "◆", color = color.new(col_dn, 0), location = location.absolute, size = size.tiny, offset = -1 ) plotchar(cond2 ? 0 : na, "", "◆", color = color.new(col_up, 0), location = location.absolute, size = size.tiny, offset = -1 ) // Trend Labels on the chart switch cond2 => label.new(chart.point.from_index(bar_index, low), "▲", style = label.style_label_up, textcolor = col_up, size = size.large, color = color(na), force_overlay = true ) cond1 => label.new(chart.point.from_index(bar_index, high), "▽", style = label.style_label_down, textcolor = col_dn, size = size.large, color = color(na), force_overlay = true ) // Meant Reversion Labels on the chart switch cond_2 => label.new(chart.point.from_index(bar_index, low), text = "◆￦nReversion", style = label.style_label_up, textcolor = col_up, size = size.normal, color = color(na), force_overlay = true ) cond_1 => label.new(chart.point.from_index(bar_index, high), text = "Reversion￦n◆", style = label.style_label_down, textcolor = col_dn, size = size.normal, color = color(na), force_overlay = true ) // Invalidation Price Levels plot(not ta.change(points) ? points : na, color = color2, linewidth = 2, style = plot.style_linebr, force_overlay = true ) if ta.change(points) label.new(chart.point.from_index(bar_index, points[1]), text = "Invalidation￦nLevel", style = label.style_label_left, textcolor = linear_regression < 0 ? col_up : col_dn, size = size.small, color = color(na), force_overlay = true ) // Candles Color candle_col = candles ? (linear_regression < 0 ? col_dn : col_up) : na plotcandle(open, high, low, close, title = "Color Candles", color = candle_col, bordercolor = candle_col, wickcolor = candle_col, force_overlay= true ) barcolor(candle_col)