//@version=5
indicator("Modjoy-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 g_value     = array.new()       // 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)

// }

⚠️ High Risk Warning

Trading Forex and Futures involves a very high degree of risk and may not be suitable for all investors. The high degree of leverage can result in the complete loss of your funds. These indicators are educational tools and do not guarantee profits. Please trade responsibly with capital you can afford to lose.