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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.
40 lines
2.2 KiB
Plaintext
40 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("Exponential Weighted MA Volatility", "EWMA Volty", overlay=false)
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//@function Calculates Exponential Weighted Moving Average (EWMA) Volatility.
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//@param src The source series for price data. Default is close.
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//@param length The period length for the EWMA calculation.
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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 EWMA Volatility value.
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//@optimized for performance and dirty data
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ewmaVolty(series float src, simple int length, simple bool annualize = true, simple int annualPeriods = 252) =>
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float logReturn = nz(math.log(src / src[1]),0.0)
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float squaredReturn = logReturn * logReturn
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var float raw_rma_sq_ret = 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(squaredReturn)
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raw_rma_sq_ret := na(raw_rma_sq_ret[1]) ? squaredReturn : (nz(raw_rma_sq_ret[1],squaredReturn) * (length - 1) + squaredReturn) / 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_sq_ret = e_rma > EPSILON and not na(raw_rma_sq_ret) ? raw_rma_sq_ret / (1.0 - e_rma) : raw_rma_sq_ret
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float currentEwmaSqReturns = nz(corrected_rma_sq_ret, squaredReturn)
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float volatility = currentEwmaSqReturns < 0 ? na : math.sqrt(currentEwmaSqReturns)
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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_source = input.source(close, "Source")
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i_length = input.int(20, "Length", minval=1, tooltip="Period for EWMA calculation")
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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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ewmaVolatilityValue = ewmaVolty(i_source, i_length, i_annualize, i_annualPeriods)
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// Plot
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plot(ewmaVolatilityValue, "EWMA Volty", color=color.yellow, linewidth=2)
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