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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.
51 lines
2.1 KiB
Plaintext
51 lines
2.1 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("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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