feat: add new indicators (Decay, Edecay, MinusDi, MinusDm, PlusDi, PlusDm, Maxindex, Minindex, Sarext) and update pine scripts, core libs, validation tests, and python bindings
2026-03-09 13:45:46 -07:00
// Licensed under the Apache License, Version 2.0
2026-01-18 19:02:03 -08:00
// © mihakralj
//@version=6
indicator("Exponential Weighted MA Volatility", "EWMA Volty", overlay=false)
//@function Calculates Exponential Weighted Moving Average (EWMA) Volatility.
//@param src The source series for price data. Default is close.
//@param length The period length for the EWMA calculation.
//@param annualize Boolean to indicate if the volatility should be annualized. Default is true.
//@param annualPeriods Number of periods in a year for annualization. Default is 252 for daily data.
//@returns float The EWMA Volatility value.
//@optimized for performance and dirty data
ewmaVolty(series float src, simple int length, simple bool annualize = true, simple int annualPeriods = 252) =>
float logReturn = nz(math.log(src / src[1]),0.0)
float squaredReturn = logReturn * logReturn
var float raw_rma_sq_ret = 0.0, var float e_rma = 1.0
float rma_alpha = 1.0 / float(length)
if not na(squaredReturn)
raw_rma_sq_ret := na(raw_rma_sq_ret[1]) ? squaredReturn : (nz(raw_rma_sq_ret[1],squaredReturn) * (length - 1) + squaredReturn) / length
e_rma := na(e_rma[1]) ? (1.0 - rma_alpha) : (1.0 - rma_alpha) * nz(e_rma[1],1.0)
float EPSILON = 1e-10
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
float currentEwmaSqReturns = nz(corrected_rma_sq_ret, squaredReturn)
float volatility = currentEwmaSqReturns < 0 ? na : math.sqrt(currentEwmaSqReturns)
annualize and not na(volatility) ? volatility * math.sqrt(float(annualPeriods)) : volatility
// ---------- Main loop ----------
// Inputs
i_source = input.source(close, "Source")
i_length = input.int(20, "Length", minval=1, tooltip="Period for EWMA calculation")
i_annualize = input.bool(true, "Annualize Volatility", tooltip="Annualize the volatility output")
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)")
// Calculation
ewmaVolatilityValue = ewmaVolty(i_source, i_length, i_annualize, i_annualPeriods)
// Plot
plot(ewmaVolatilityValue, "EWMA Volty", color=color.yellow, linewidth=2)
