Rolling Skewness Pass Node
Rolling Skewness — Series Input
Overview
The Rolling Skewness Pass Node computes the rolling skewness of a series — the third standardised moment, measuring the asymmetry of the return distribution within a sliding window.
Positive skewness (right tail) means occasional large gains; negative skewness (left tail) means occasional large losses. Financial return distributions typically exhibit negative skewness.
Formula
Parameters
| Parameter | Default | Description |
|---|---|---|
| period | 20 | Rolling window in bars |
Inputs & Outputs
| Slot | Direction | Type | Description |
|---|---|---|---|
| input | Input | { values, timestamps } | Any upstream numeric series |
| values | Output | (number | null)[] | Rolling skewness per bar; nulls during warm-up |
| timestamps | Output | number[] | Unix timestamps aligned to input |
Use Cases
Tail Risk Direction
Negative skewness in returns signals greater downside tail risk — consider hedging or reducing leverage during periods of persistently negative rolling skewness.
Options Skew Analysis
Negative rolling skewness on return series corresponds to put options being more expensive than calls — useful for volatility surface analysis.
Distribution Shape Profiling
Combine with kurtosis for a full distributional picture: skewness gives direction of asymmetry; kurtosis gives magnitude of tail weight.
Tips & Best Practices
Apply to Returns, Not Prices
Skewness of raw price levels is dominated by trend effects. Feed log-returns or simple returns for meaningful distributional analysis.
Needs Sufficient Sample
Skewness estimates are unreliable for period < 20. Use period ≥ 30 for stable estimates; skewness is typically the noisiest of the moments.
Pair with Kurtosis
Skewness and kurtosis together characterise deviation from normality. High negative skewness + high kurtosis = crash-prone, fat left tail.