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// This work is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International https://creativecommons.org/licenses/by-nc-sa/4.0/
//@version=5
indicator("Volatility Gaussian Bands [BigBeluga]", overlay = true, max_labels_count = 500)
// ＩＮＰＵＴＳ ========================================================================================================{
//@variable len Length of the Gaussian filter for smoothing (minimum value: 5)
int len = input.int(20, "Length", minval = 5)
//@variable mode Select the mode of aggregation to be used: AVG (average), MEADIAN (median), or MODE (mode)
string mode = input.string("AVG", "Type", ["AVG", "MEADIAN", "MODE"])
//@variable distance Multiplier for calculating the distance between the Gaussian filter and the volatility bands
float distance = input.float(1, step = 0.1)
//@variable show_retest Boolean input to determine if retest signals should be displayed
bool show_retest = input.bool(false, "Retest Signals")
//@variable up Color for upward trends and visual signals, represented in RGB
color up = input.color(color.rgb(40, 218, 150), group = "Color")
//@variable dn Color for downward trends and visual signals, represented in hex code
color dn = input.color(#287bda, group = "Color")
// }
// ＣＡＬＣＵＬＡＴＩＯＮＳ ============================================================================================={
//@function Calculates a Gaussian filter for smoothing the data
//@param src (series float) Source price series
//@param length (int) Length of the filter
//@param sigma (float) Standard deviation for the Gaussian function
//@returns (float) Smoothed value for the current bar
gaussian_filter(src, length, sigma) =>
var float[] weights = array.new_float(100) // Create an array to store weights for Gaussian filter
float total = 0.0 // Sum of all weights, used for normalization
float pi = math.pi // Define Pi constant
// Calculate weights for Gaussian filter
for i = 0 to length - 1
float weight = math.exp(-0.5 * math.pow((i - length / 2) / sigma, 2.0)) / math.sqrt(sigma * 2.0 * pi)
weights.set(i, weight)
total := total + weight
// Normalize weights
for i = 0 to length - 1
weights.set(i, weights.get(i) / total)
// Apply Gaussian filter to the source series
float sum = 0.0
for i = 0 to length - 1
sum := sum + src[i] * weights.get(i)
sum
//@function Multi-trend calculation using Gaussian filter
//@param src (series float) Source price series
//@param period (int) Lookback period for trend calculation
//@returns (float[]) Returns score, value, color, trend line, and trend status
multi_trend(src, period) =>
array<float> g_value = array.new<float>() // Array to store Gaussian filtered values
float volatility = ta.sma(high - low, 100) // Calculate the average true range (ATR) volatility
var float lower_band = 0.0 // Lower band for trend analysis
var float upper_band = 0.0 // Upper band for trend analysis
var float trend_line = 0.0 // Trend line value
var bool trend = na // Trend direction status
// Apply Gaussian filter with a step adjustment to calculate multiple trend lines
for step = 0 to 20 by 1
float gaussian_filter = gaussian_filter(src, (period + step), 10)
g_value.push(gaussian_filter)
float coeff = 0.05
float score = 0.0
// Calculate score based on trend analysis
for i = 0 to g_value.size() - 1
float g_f = g_value.get(i)
if g_f > g_value.first()
score += coeff
// Determine color based on score
color color = score > 0.5
? color.from_gradient(score, 0.5, 1, na, dn)
: color.from_gradient(score, 0, 0.5, up, na)
// Determine value based on user-selected mode (AVG, MEDIAN, MODE)
float value = switch mode
"AVG" => g_value.avg()
"MEADIAN" => g_value.median()
"MODE" => g_value.mode()
lower_band := value - volatility * distance // Calculate lower band based on value and volatility
upper_band := value + volatility * distance // Calculate upper band based on value and volatility
// Check crossover and crossunder of price with bands to determine trend
if ta.crossover(close, upper_band)
trend := true
if ta.crossunder(close, lower_band)
trend := false
// Set trend line based on trend direction
trend_line :=
trend ? lower_band
: not trend ? upper_band : na
// Return values: score, value, color, trend line, and trend status
[score, value, color, trend_line, trend]
// Get the result from the multi-trend function
[score, avg, color, trend_line, trend] = multi_trend(close, len)
// }
// ＰＬＯＴ ============================================================================================================={
// Plot the average line returned from multi_trend function
p2 = plot(avg, color = color, linewidth=1)
// Plot the trend line based on trend status
p1 = plot(ta.change(trend) ? na : trend_line,
color = close > trend_line ? up : dn,
linewidth = 2,
style = plot.style_linebr)
// Plot the trend line again with styling
plot(trend_line, color = close > trend_line ? up : dn, linewidth=1, style = plot.style_linebr)
// Add labels for cross under and crossover events
if ta.crossunder(close, trend_line)
label.new(bar_index, trend_line, score < 0.5 ? "▼+" : "▼",
color = dn,
textcolor = chart.fg_color,
style = label.style_label_lower_right,
size = score < 0.5 ? size.small : size.tiny)
if ta.crossover(close, trend_line)
label.new(bar_index, trend_line, score > 0.5 ? "▲+" : "▲",
color = up,
textcolor = chart.bg_color,
style = label.style_label_upper_right,
size = score > 0.5 ? size.small : size.tiny)
// Determine trend color based on trend direction
color trend_color = trend ? color.new(up, 80) : color.new(dn, 80)
// Fill between trend line and average line to show areas of trend
fill(p1, p2, trend_line, avg, trend_color, na)
fill(p1, p2, trend_line, avg, trend_color, na)
// Add retest labels if the option is enabled
if show_retest
if ta.crossunder(high, avg) and not trend
label.new(bar_index[1], high[1], "▼",
color = color(na),
style = label.style_label_down,
textcolor = chart.fg_color,
size = size.small)
if ta.crossover(close, avg) and trend
label.new(bar_index[1], low[1], "▲",
color = color(na),
style = label.style_label_up,
textcolor = chart.fg_color,
size = size.small)
// Calculate score-up and score-down for trend strength representation
float score_up = (score - 1) * -1
float score_dn = 1 - score_up
// Display trend strength as a table if on the last bar
if barstate.islast
table trend_strength_up = table.new(position.bottom_center, 100, 100)
table trend_strength_dn = table.new(position.top_center, 100, 100)
// Create cells to represent trend strength up
for i = 0 to score_up * 20
trend_strength_up.cell(0 + i, 0, bgcolor = color.new(up, 100 - i * 5), text = i == 0 ? "|" : "", text_color = color.gray)
if i == score_up * 20
trend_strength_up.cell(0 + i, 0,
text = str.tostring(score_up * 100, format.percent) + " ▲",
text_color = chart.fg_color,
height = 2)
// Create cells to represent trend strength down
for i = 0 to score_dn * 20
trend_strength_dn.cell(0 + i, 0, bgcolor = color.new(dn, 100 - i * 5), text = i == 0 ? "|" : "", text_color = color.gray)
if i == score_dn * 20
trend_strength_dn.cell(0 + i, 0,
text = str.tostring(score_dn * 100, format.percent) + " ▼",
text_color = chart.fg_color,
height = 2)
// }