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SIMD Refactor: Merge simd-dev into dev (#55)

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Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: aider (openrouter/anthropic/claude-sonnet-4) <aider@aider.chat>
Co-authored-by: Warp <agent@warp.dev>
This commit is contained in:
Miha Kralj
2026-01-18 19:02:03 -08:00
committed by GitHub
co-authored by Claude Opus 4.5 aider Warp
parent 5bcdf8d614
commit 86fe32a682
1750 changed files with 198235 additions and 80539 deletions
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lib/volatility/cv/cv.pine
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// The MIT License (MIT)
// © mihakralj
//@version=6
indicator("Conditional Volatility (CV)", "CV", overlay=false)
//@function Calculates GARCH(1,1) conditional volatility
//@param length Initial period for parameter estimation
//@param alpha Weight on previous squared return
//@param beta Weight on previous variance
//@returns float Conditional volatility value
//@optimized for performance and efficient variance updating
cv(simple int length, simple float alpha, simple float beta) =>
if length <= 0
runtime.error("Length must be greater than 0")
if alpha <= 0.0 or alpha >= 1.0
runtime.error("Alpha must be between 0 and 1")
if beta <= 0.0 or beta >= 1.0
runtime.error("Beta must be between 0 and 1")
if alpha + beta >= 1.0
runtime.error("Alpha + Beta must be less than 1 for stationarity")
var float omega = 0.0
var float longRunVar = 0.0
var float prevVariance = 0.0
float DAYS_IN_YEAR = 252.0
float MIN_PRICE = 1e-10
float DEFAULT_VARIANCE = 0.0001
float safeClose = nz(close, close[1])
safeClose := math.max(safeClose, MIN_PRICE)
float safePrevClose = nz(close[1], close[2] != 0.0 ? close[2] : safeClose)
safePrevClose := math.max(safePrevClose, MIN_PRICE)
float logReturn = 0.0
if safeClose > 0.0 and safePrevClose > 0.0
logReturn := math.log(safeClose / safePrevClose)
logReturn := math.abs(logReturn) > 0.2 ? math.sign(logReturn) * 0.2 : logReturn
float squaredReturn = logReturn * logReturn
if bar_index < length
longRunVar := (bar_index * longRunVar + squaredReturn) / (bar_index + 1)
prevVariance := longRunVar
else if bar_index == length
omega := (1.0 - alpha - beta) * longRunVar
prevVariance := longRunVar
float variance = nz(prevVariance, DEFAULT_VARIANCE)
variance := omega + alpha * squaredReturn + beta * variance
variance := math.max(variance, 0.0000001)
prevVariance := variance
math.sqrt(DAYS_IN_YEAR * variance) * 100
// ---------- Main loop ----------
// Inputs
i_length = input.int(20, "Length", minval=10, maxval=500, tooltip="Initial period for estimation")
i_alpha = input.float(0.2, "Alpha", minval=0.01, maxval=0.99, step=0.01, tooltip="Weight on previous squared return")
i_beta = input.float(0.7, "Beta", minval=0.01, maxval=0.99, step=0.01, tooltip="Weight on previous variance")
// Calculation
cvValue = cv(i_length, i_alpha, i_beta)
// Plot
plot(cvValue, "CV", color=color.yellow, linewidth=2)