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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.
41 lines
2.2 KiB
Plaintext
41 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("Garman-Klass Volatility (GKV)", "GKV", overlay=false)
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//@function Calculates Garman-Klass Volatility.
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//@param length The period length for smoothing the Garman-Klass 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 Garman-Klass Volatility value.
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//@optimized for performance and dirty data
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gkv(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), float lnO = math.log(open), float lnC = math.log(close)
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float C_2LN2_1 = 0.3862941611 // 2 * math.log(2) - 1
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float term1 = 0.5 * math.pow(lnH - lnL, 2)
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float term2 = C_2LN2_1 * math.pow(lnC - lnO, 2)
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float gkEstimator = term1 - term2
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var float raw_rma_gk = 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(gkEstimator)
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raw_rma_gk := na(raw_rma_gk[1]) ? gkEstimator : (nz(raw_rma_gk[1], gkEstimator) * (length - 1) + gkEstimator) / 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_gk = e_rma > EPSILON and not na(raw_rma_gk) ? raw_rma_gk / (1.0 - e_rma) : raw_rma_gk
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float smoothedGkEstimator = nz(corrected_rma_gk, gkEstimator)
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float volatility = smoothedGkEstimator < 0 ? na : math.sqrt(smoothedGkEstimator)
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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 Garman-Klass 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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gkvValue = gkv(i_length, i_annualize, i_annualPeriods)
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// Plot
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plot(gkvValue, "GKV", color=color.yellow, linewidth=2)
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