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
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// Licensed under the Apache License, Version 2.0
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2026-01-18 19:02:03 -08:00
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// © mihakralj
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//@version=6
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indicator("Conditional Volatility (CV)", "CV", overlay=false)
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//@function Calculates GARCH(1,1) conditional volatility
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//@param length Initial period for parameter estimation
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//@param alpha Weight on previous squared return
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//@param beta Weight on previous variance
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//@returns float Conditional volatility value
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//@optimized for performance and efficient variance updating
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cv(simple int length, simple float alpha, simple float beta) =>
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var float omega = 0.0
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var float longRunVar = 0.0
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var float prevVariance = 0.0
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float DAYS_IN_YEAR = 252.0
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float MIN_PRICE = 1e-10
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float DEFAULT_VARIANCE = 0.0001
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float safeClose = nz(close, close[1])
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safeClose := math.max(safeClose, MIN_PRICE)
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float safePrevClose = nz(close[1], close[2] != 0.0 ? close[2] : safeClose)
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safePrevClose := math.max(safePrevClose, MIN_PRICE)
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float logReturn = 0.0
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if safeClose > 0.0 and safePrevClose > 0.0
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logReturn := math.log(safeClose / safePrevClose)
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logReturn := math.abs(logReturn) > 0.2 ? math.sign(logReturn) * 0.2 : logReturn
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float squaredReturn = logReturn * logReturn
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if bar_index < length
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longRunVar := (bar_index * longRunVar + squaredReturn) / (bar_index + 1)
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prevVariance := longRunVar
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else if bar_index == length
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omega := (1.0 - alpha - beta) * longRunVar
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prevVariance := longRunVar
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float variance = nz(prevVariance, DEFAULT_VARIANCE)
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variance := omega + alpha * squaredReturn + beta * variance
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variance := math.max(variance, 0.0000001)
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prevVariance := variance
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math.sqrt(DAYS_IN_YEAR * variance) * 100
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// ---------- Main loop ----------
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// Inputs
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i_length = input.int(20, "Length", minval=10, maxval=500, tooltip="Initial period for estimation")
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i_alpha = input.float(0.2, "Alpha", minval=0.01, maxval=0.99, step=0.01, tooltip="Weight on previous squared return")
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i_beta = input.float(0.7, "Beta", minval=0.01, maxval=0.99, step=0.01, tooltip="Weight on previous variance")
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// Calculation
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cvValue = cv(i_length, i_alpha, i_beta)
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// Plot
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plot(cvValue, "CV", color=color.yellow, linewidth=2)
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