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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
35a6702b06
Deep review of all indicator categories verified .md headers against .cs WarmupPeriod, parameters, inputs, and outputs. Fixes include warmup corrections, parameter documentation, output type accuracy, and Pine Script alignment.
63 lines
2.4 KiB
Plaintext
63 lines
2.4 KiB
Plaintext
// Licensed under the Apache License, Version 2.0
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// © mihakralj
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//@version=6
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indicator("Bollinger Band Width Percentile (BBWP)", "BBWP", overlay=false, format=format.percent)
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//@function Calculates Bollinger Band Width Percentile relative to historical range
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//@param source Series to calculate Bollinger Bands from
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//@param period Lookback period for BB calculations
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//@param multiplier Standard deviation multiplier for band width
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//@param lookback Historical lookback period for percentile calculation
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//@returns BBWP value representing current BBW percentile in historical range
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//@optimized for performance and dirty data
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bbwp(series float source, simple int period, simple float multiplier, simple int lookback) =>
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var int p = math.max(1, period), var int head = 0, var int count = 0
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var array<float> buffer = array.new_float(p, na)
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var float sum = 0.0, var float sumSq = 0.0
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float oldest = array.get(buffer, head)
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if not na(oldest)
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sum -= oldest
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sumSq -= oldest * oldest
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count -= 1
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float current_val = nz(source)
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sum += current_val
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sumSq += current_val * current_val
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count += 1
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array.set(buffer, head, current_val)
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head := (head + 1) % p
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float basis = nz(sum / count, source)
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float dev = count > 1 ? multiplier * math.sqrt(math.max(0.0, sumSq / count - basis * basis)) : 0.0
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float bbw = basis != 0.0 ? 2 * dev / basis : 0.0
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var int l = math.max(1, lookback), var int hist_head = 0, var int hist_count = 0
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var array<float> hist_buffer = array.new_float(l, na)
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float hist_oldest = array.get(hist_buffer, hist_head)
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if not na(hist_oldest)
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hist_count -= 1
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if not na(bbw)
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hist_count += 1
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array.set(hist_buffer, hist_head, bbw)
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hist_head := (hist_head + 1) % l
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if hist_count < 2
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0.5
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else
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int below_count = 0
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for i = 0 to hist_count - 1
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float val = array.get(hist_buffer, i)
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if not na(val) and val < bbw
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below_count += 1
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below_count / hist_count
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// ---------- Main loop ----------
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// Inputs
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i_period = input.int(20, "Period", minval=1)
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i_source = input.source(close, "Source")
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i_multiplier = input.float(2.0, "StdDev Multiplier", minval=0.001)
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i_lookback = input.int(252, "Lookback Period", minval=1)
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// Calculation
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bbwp_value = bbwp(i_source, i_period, i_multiplier, i_lookback)
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// Plot
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plot(bbwp_value, "BBWP", color=color.yellow, linewidth=2)
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