RollingSkewness Node
Rolling return distribution asymmetry measurement
Overview
Rolling Skewness measures return distribution asymmetry. Positive skew = more upside surprises (good). Negative skew = more downside surprises (bad). Rising rolling skew = market improving psychologically. Declining rolling skew = tail risk rising, need defense.
Traders prefer positive skew: win big occasionally, lose small frequently. Negative skew (eat fish, get caught by shark) is dangerous. Rolling skewness change is early indicator of regime shift. High volatility + negative skew = danger zone.
Formula & Calculation
Normal distribution: Skewness = 0
Skew = 0: Symmetric
Skew < 0: Left tail (negative outliers = crashes)
Parameters
| Parameter | Default | Description |
|---|---|---|
| lookback | 60 | Rolling window |
| bias | False | Sample vs population skew |
Common Use Cases
1. Risk Diagnosis
Negative rolling skew = crash risk. Defense needed (wider stops, reduced leverage, hedges). Positive skew = opportunity for aggression.
2. Optimization Target
Optimize strategies for positive skew (rare). Avoid negative skew strategies. Design portfolios: diversify to positive skew.
3. Tail Hedging
When rolling skew becomes negative, add tail hedges (long OTM puts). Skew inflation = tail fear increasing = OTM puts expensive but necessary.
4. Earnings Alert
Rising negative skew before earnings = market fearing downside. Match position sizing to skew (reduce into negative skew).
Advantages & Limitations
Advantages
- Captures distribution shape
- Simple interpretation
- Early warning of tail risk
- Guides position sizing
Limitations
- Requires many samples
- Influenced by large outliers
- Unstable in small windows
- Backward looking only