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2025-06-20 10:40:52
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// This Pine S cript™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © ChartPrime
//@version=5
indicator('Linear Regression Channel', overlay = true, max_lines_count = 3)
// ---------------------------------------------------------------------------------------------------------------------}
// 𝙐𝙎𝙀𝙍 𝙄𝙉𝙋𝙐𝙏𝙎
// ---------------------------------------------------------------------------------------------------------------------{
bool bands = input.bool(true, "Plot Linear Regression Bands", group = "Settings Bands")
int window = input.int(150, "Length", group = "Settings Bands")
float devlen_b = input.float(3., "Deviation Linear Regression Bands",step=0.1, group = "Settings Bands")
bool channel = input.bool(false, "Plot Linear Regression Channel", group = "Settings Channel")
int window1 = input.int(150, "Channel Length", group = "Settings Channel")
float devlen1 = input.float(1., "Deviation Linear Regression Channel",step=0.1, group = "Settings Channel")
bool channel1 = input.bool(false, "Plot Future Projection of linear regression", group = "Future Projection Channel")
bool arr_dirc = input.bool(false, "Plot Arrow Direction", group = "Future Projection Channel")
int window2 = input.int(50, "Length", group = "Future Projection Channel")
float devlen2 = input.float(1., "Deviation Future Projection Regression Channel",step=0.1, group = "Future Projection Channel")
// Define colors for up, down, and mid lines
color col_dn = #f01313
color col_up = color.aqua
color col_mid = color.yellow
color gray = color.gray
color fg_col = chart.fg_color
// Regression Channel Arrays Line
var reglines = array.new_line(3)
var reglines_ = array.new_line(3)
// ---------------------------------------------------------------------------------------------------------------------}
// 𝙄𝙉𝘿𝙄𝘾𝘼𝙏𝙊𝙍 𝘾𝘼𝙇𝘾𝙐𝙇𝘼𝙏𝙄𝙊𝙉𝙎
// ---------------------------------------------------------------------------------------------------------------------{
//@function linear_regression
//@des cription Calculates linear regression coefficients for a given source and window.
//@param src (series float) The data series on which linear regression is calculated.
//@param window (int) The number of bars to use in the calculation.
//@returns the intercept slope, Deviation, end of the channel.
linear_regression(src, window) =>
sum_x = 0.0
sum_y = 0.0
sum_xy = 0.0
sum_x_sq = 0.0
// Calculate sums
for i = 0 to window - 1 by 1
sum_x += i + 1
sum_y += src[i]
sum_xy += (i + 1) * src[i]
sum_x_sq += math.pow(i + 1, 2)
// Calculate linear regression coefficients
slope = (window * sum_xy - sum_x * sum_y) / (window * sum_x_sq - math.pow(sum_x, 2))
intercept = (sum_y - slope * sum_x) / window
y1 = intercept + slope * (window - 1)
dev = 0.0
for i = 0 to window - 1
dev := dev + math.pow(src[i] - (slope * (window - i) + intercept), 2)
dev := math.sqrt(dev/window)
[intercept, y1, dev, slope]
[y2, y1, dev, slope] = linear_regression(close, window)
[y2_, y1_, dev_, slope_] = linear_regression(close, window1)
[y2__, y1__, dev__, slope__] = linear_regression(close, window2)
// Linear Regression Channel Lines
series float mid = y2 + slope
series float upper = mid + ta.rma(high - low, window) * devlen_b
series float lower = mid - ta.rma(high - low, window) * devlen_b
// Returns True for window length period
isAllowed = (last_bar_index - bar_index < window1)
// ---------------------------------------------------------------------------------------------------------------------}
// 𝙑𝙄𝙎𝙐𝘼𝙇𝙄𝙕𝘼𝙏𝙄𝙊𝙉
// ---------------------------------------------------------------------------------------------------------------------{
// Plot upper, lower, and mid lines if channel is not enabled
p_u = plot(upper, color = bands and channel ? na : bands ? gray : na, linewidth = 2)
p_l = plot(lower, color = bands and channel ? na : bands ? gray : na, linewidth = 2)
p_m = plot(mid, color = bands and channel ? na : bands ? gray : na)
// Fill areas between upper/mid and lower/mid lines
fill(p_u, p_m, mid, upper, na, bands and channel ? na : bands ? color.new(col_up, 80) : na)
fill(p_m, p_l, lower, mid, bands and channel ? na : bands ? color.new(col_dn, 80) : na, na)
if barstate.islast and channel
for i = 0 to 2
array.set(reglines, i,
line.new(x1 = bar_index - (window1 - 1),
y1 = y1_ + dev_ * devlen1 * (i - 1),
x2 = bar_index,
y2 = y2_ + dev_ * devlen1 * (i - 1),
color = color.gray,
style = i % 2 == 1 ? line.style_dashed : line.style_solid,
width = 2,
extend = extend.none)
)
linefill.new(array.get(reglines, 1), array.get(reglines, 2), color = color.new(col_up, 90))
linefill.new(array.get(reglines, 1), array.get(reglines, 0), color = color.new(col_dn, 90))
if barstate.islast and channel1
for i = 0 to 2
array.set(reglines_, i,
line.new(x1 = bar_index - (window2 - 1),
y1 = y1__ + dev__ * devlen2 * (i - 1),
x2 = bar_index,
y2 = y2__ + dev__ * devlen2 * (i - 1),
color = color.gray,
style = i % 2 == 1 ? line.style_dotted : line.style_dashed,
width = 1,
extend = extend.right)
)
linefill.new(array.get(reglines_, 1), array.get(reglines_, 2), color = color.new(col_up, 95))
linefill.new(array.get(reglines_, 1), array.get(reglines_, 0), color = color.new(col_dn, 95))
if arr_dirc
l1 = label.new(chart.point.from_index(bar_index, hl2 > y2__ ? high : low),
text = hl2 > y2__ ? "⇗" : hl2 < y2__ ? "⇘" : "⇒",
textcolor = hl2 > y2__ ? col_up : hl2 < y2__ ? col_dn : gray,
color = color(na),
size = size.huge,
style = label.style_label_left
)
label.delete(l1[1])
// Bar Heat Map
b_c = (close - lower) / (upper - lower)
b_c := b_c > 1 ? 1 : b_c < 0 ? 0 : b_c
bar_color = channel ?
(isAllowed ?
(b_c <= 0.5 ? color.from_gradient(b_c, 0, 0.5, col_up, col_mid) : color.from_gradient(b_c, 0.5, 1, col_mid, col_dn))
: na)
: (b_c >= 0.5 ? color.from_gradient(b_c, 0.5, 1, col_mid, col_up) : color.from_gradient(b_c, 0, 0.5, col_dn, col_mid))
plotcandle(open, high, low, close,
title = "Bar HeatMap",
color = bar_color,
wickcolor = bar_color,
bordercolor = bar_color
)
barcolor(bar_color)
// Conditions for crossovers
condition1 = bands and channel ? na : bands ? ta.pivotlow(3, 3) and close < lower : na
condition2 = bands and channel ? na : bands ? ta.pivothigh(3, 3) and close > upper: na
// Plot markers for channel break outs
plotchar(condition1, "", "◆", size=size.tiny, location=location.belowbar, color = col_up)
plotchar(condition2, "", "◆", size=size.tiny, location=location.abovebar, color = col_dn)
// ------------------------------------------------------------------------------------
답변 1
예스스탁 예스스탁 답변
2025-06-20 14:40:13
안녕하세요
예스스탁입니다.
선그래프만 작성해 드립니다.
input : window(100),devlen_b(2.0);
var : sum_x(0),sum_y(0),sum_xy(0),sum_x_sq(0),i(0);
var : slope(0),y2(0),y1(0),dev(0);
var : mid(0),upper(0),lower(0),alpha(0),rmav(0);
sum_x = 0.0;
sum_y = 0.0;
sum_xy = 0.0;
sum_x_sq = 0.0;
for i = 0 to window - 1
{
sum_x = sum_x + i + 1;
sum_y = sum_y + close[i];
sum_xy = sum_xy + (i + 1) * close[i];
sum_x_sq = sum_x_sq + pow(i + 1, 2);
}
slope = (window * sum_xy - sum_x * sum_y) / (window * sum_x_sq - pow(sum_x, 2));
y2 = (sum_y - slope * sum_x) / window;
y1 = y2 + slope * (window - 1);
dev = 0.0;
for i = 0 to window - 1
{
dev = dev + pow(close[i] - (slope * (window - i) + y2), 2);
}
dev = sqrt(dev / window);
mid = y2 + slope;
alpha = 1 / window ;
rmav = IFf(IsNan(rmav[1]) == true, ma(H-L,window) , alpha * (H-L) + (1 - alpha) * IFf(isnan(rmav[1])==true,0,rmav[1]));
upper = mid + rmav * devlen_b;
lower = mid - rmav * devlen_b;
Plot1(upper,"upper",Gray);
Plot2(mid,"mid",Gray);
Plot3(lower,"lower",Gray);
즐거운 하루되세요
> 파생돌이 님이 쓴 글입니다.
> 제목 : 부틱드립니다
> 수고하십니다
매번 부탁드려서 죄송합니다
예스로 변경드립니다
// This Pine S cript™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © ChartPrime
//@version=5
indicator('Linear Regression Channel', overlay = true, max_lines_count = 3)
// ---------------------------------------------------------------------------------------------------------------------}
// 𝙐𝙎𝙀𝙍 𝙄𝙉𝙋𝙐𝙏𝙎
// ---------------------------------------------------------------------------------------------------------------------{
bool bands = input.bool(true, "Plot Linear Regression Bands", group = "Settings Bands")
int window = input.int(150, "Length", group = "Settings Bands")
float devlen_b = input.float(3., "Deviation Linear Regression Bands",step=0.1, group = "Settings Bands")
bool channel = input.bool(false, "Plot Linear Regression Channel", group = "Settings Channel")
int window1 = input.int(150, "Channel Length", group = "Settings Channel")
float devlen1 = input.float(1., "Deviation Linear Regression Channel",step=0.1, group = "Settings Channel")
bool channel1 = input.bool(false, "Plot Future Projection of linear regression", group = "Future Projection Channel")
bool arr_dirc = input.bool(false, "Plot Arrow Direction", group = "Future Projection Channel")
int window2 = input.int(50, "Length", group = "Future Projection Channel")
float devlen2 = input.float(1., "Deviation Future Projection Regression Channel",step=0.1, group = "Future Projection Channel")
// Define colors for up, down, and mid lines
color col_dn = #f01313
color col_up = color.aqua
color col_mid = color.yellow
color gray = color.gray
color fg_col = chart.fg_color
// Regression Channel Arrays Line
var reglines = array.new_line(3)
var reglines_ = array.new_line(3)
// ---------------------------------------------------------------------------------------------------------------------}
// 𝙄𝙉𝘿𝙄𝘾𝘼𝙏𝙊𝙍 𝘾𝘼𝙇𝘾𝙐𝙇𝘼𝙏𝙄𝙊𝙉𝙎
// ---------------------------------------------------------------------------------------------------------------------{
//@function linear_regression
//@des cription Calculates linear regression coefficients for a given source and window.
//@param src (series float) The data series on which linear regression is calculated.
//@param window (int) The number of bars to use in the calculation.
//@returns the intercept slope, Deviation, end of the channel.
linear_regression(src, window) =>
sum_x = 0.0
sum_y = 0.0
sum_xy = 0.0
sum_x_sq = 0.0
// Calculate sums
for i = 0 to window - 1 by 1
sum_x += i + 1
sum_y += src[i]
sum_xy += (i + 1) * src[i]
sum_x_sq += math.pow(i + 1, 2)
// Calculate linear regression coefficients
slope = (window * sum_xy - sum_x * sum_y) / (window * sum_x_sq - math.pow(sum_x, 2))
intercept = (sum_y - slope * sum_x) / window
y1 = intercept + slope * (window - 1)
dev = 0.0
for i = 0 to window - 1
dev := dev + math.pow(src[i] - (slope * (window - i) + intercept), 2)
dev := math.sqrt(dev/window)
[intercept, y1, dev, slope]
[y2, y1, dev, slope] = linear_regression(close, window)
[y2_, y1_, dev_, slope_] = linear_regression(close, window1)
[y2__, y1__, dev__, slope__] = linear_regression(close, window2)
// Linear Regression Channel Lines
series float mid = y2 + slope
series float upper = mid + ta.rma(high - low, window) * devlen_b
series float lower = mid - ta.rma(high - low, window) * devlen_b
// Returns True for window length period
isAllowed = (last_bar_index - bar_index < window1)
// ---------------------------------------------------------------------------------------------------------------------}
// 𝙑𝙄𝙎𝙐𝘼𝙇𝙄𝙕𝘼𝙏𝙄𝙊𝙉
// ---------------------------------------------------------------------------------------------------------------------{
// Plot upper, lower, and mid lines if channel is not enabled
p_u = plot(upper, color = bands and channel ? na : bands ? gray : na, linewidth = 2)
p_l = plot(lower, color = bands and channel ? na : bands ? gray : na, linewidth = 2)
p_m = plot(mid, color = bands and channel ? na : bands ? gray : na)
// Fill areas between upper/mid and lower/mid lines
fill(p_u, p_m, mid, upper, na, bands and channel ? na : bands ? color.new(col_up, 80) : na)
fill(p_m, p_l, lower, mid, bands and channel ? na : bands ? color.new(col_dn, 80) : na, na)
if barstate.islast and channel
for i = 0 to 2
array.set(reglines, i,
line.new(x1 = bar_index - (window1 - 1),
y1 = y1_ + dev_ * devlen1 * (i - 1),
x2 = bar_index,
y2 = y2_ + dev_ * devlen1 * (i - 1),
color = color.gray,
style = i % 2 == 1 ? line.style_dashed : line.style_solid,
width = 2,
extend = extend.none)
)
linefill.new(array.get(reglines, 1), array.get(reglines, 2), color = color.new(col_up, 90))
linefill.new(array.get(reglines, 1), array.get(reglines, 0), color = color.new(col_dn, 90))
if barstate.islast and channel1
for i = 0 to 2
array.set(reglines_, i,
line.new(x1 = bar_index - (window2 - 1),
y1 = y1__ + dev__ * devlen2 * (i - 1),
x2 = bar_index,
y2 = y2__ + dev__ * devlen2 * (i - 1),
color = color.gray,
style = i % 2 == 1 ? line.style_dotted : line.style_dashed,
width = 1,
extend = extend.right)
)
linefill.new(array.get(reglines_, 1), array.get(reglines_, 2), color = color.new(col_up, 95))
linefill.new(array.get(reglines_, 1), array.get(reglines_, 0), color = color.new(col_dn, 95))
if arr_dirc
l1 = label.new(chart.point.from_index(bar_index, hl2 > y2__ ? high : low),
text = hl2 > y2__ ? "⇗" : hl2 < y2__ ? "⇘" : "⇒",
textcolor = hl2 > y2__ ? col_up : hl2 < y2__ ? col_dn : gray,
color = color(na),
size = size.huge,
style = label.style_label_left
)
label.delete(l1[1])
// Bar Heat Map
b_c = (close - lower) / (upper - lower)
b_c := b_c > 1 ? 1 : b_c < 0 ? 0 : b_c
bar_color = channel ?
(isAllowed ?
(b_c <= 0.5 ? color.from_gradient(b_c, 0, 0.5, col_up, col_mid) : color.from_gradient(b_c, 0.5, 1, col_mid, col_dn))
: na)
: (b_c >= 0.5 ? color.from_gradient(b_c, 0.5, 1, col_mid, col_up) : color.from_gradient(b_c, 0, 0.5, col_dn, col_mid))
plotcandle(open, high, low, close,
title = "Bar HeatMap",
color = bar_color,
wickcolor = bar_color,
bordercolor = bar_color
)
barcolor(bar_color)
// Conditions for crossovers
condition1 = bands and channel ? na : bands ? ta.pivotlow(3, 3) and close < lower : na
condition2 = bands and channel ? na : bands ? ta.pivothigh(3, 3) and close > upper: na
// Plot markers for channel break outs
plotchar(condition1, "", "◆", size=size.tiny, location=location.belowbar, color = col_up)
plotchar(condition2, "", "◆", size=size.tiny, location=location.abovebar, color = col_dn)
// ------------------------------------------------------------------------------------
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