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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.
39 lines
2.2 KiB
Plaintext
39 lines
2.2 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("High-Low Volatility (HLV)", "HLV", overlay=false)
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//@function Calculates High-Low Volatility based on the Parkinson number.
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//@param length The period length for smoothing the Parkinson estimator.
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//@param annualize Boolean to indicate if the volatility should be annualized. Default is true.
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//@param annualPeriods Number of periods in a year for annualization. Default is 252 for daily data.
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//@returns float The High-Low Volatility value.
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//@optimized for performance and dirty data
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hlv(simple int length, simple bool annualize = true, simple int annualPeriods = 252) =>
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float lnH = math.log(high), float lnL = math.log(low)
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float C_4LN2_INV = 0.3606737602 // 1.0 / (4.0 * math.log(2.0))
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float parkinsonEstimator = C_4LN2_INV * math.pow(lnH - lnL, 2)
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var float raw_rma_parkinson = 0.0, var float e_rma = 1.0
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float rma_alpha = 1.0 / float(length)
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if not na(parkinsonEstimator)
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raw_rma_parkinson := na(raw_rma_parkinson[1]) ? parkinsonEstimator : (nz(raw_rma_parkinson[1], parkinsonEstimator) * (length - 1) + parkinsonEstimator) / length
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e_rma := na(e_rma[1]) ? (1.0 - rma_alpha) : (1.0 - rma_alpha) * nz(e_rma[1], 1.0)
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float EPSILON = 1e-10
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float corrected_rma_parkinson = e_rma > EPSILON and not na(raw_rma_parkinson) ? raw_rma_parkinson / (1.0 - e_rma) : raw_rma_parkinson
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float smoothedParkinsonEstimator = nz(corrected_rma_parkinson, parkinsonEstimator)
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float volatility = smoothedParkinsonEstimator < 0 ? na : math.sqrt(smoothedParkinsonEstimator)
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annualize and not na(volatility) ? volatility * math.sqrt(float(annualPeriods)) : volatility
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// ---------- Main loop ----------
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// Inputs
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i_length = input.int(20, "Length", minval=1, tooltip="Period for smoothing the Parkinson estimator")
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i_annualize = input.bool(true, "Annualize Volatility", tooltip="Annualize the volatility output")
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i_annualPeriods = input.int(252, "Annual Periods", minval=1, tooltip="Number of periods in a year for annualization (e.g., 252 for daily, 52 for weekly)")
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// Calculation
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hlvValue = hlv(i_length, i_annualize, i_annualPeriods)
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// Plot
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plot(hlvValue, "HLV", color=color.yellow, linewidth=2)
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