RollingStd / Computation Layer

Calculate the rolling standard deviation over a sliding window. Similar to pandas.rolling().std() or R's roll_sd().

Mathematical form: $σ_{i} = \frac{\sum _{j = i - w + 1}^{i} w _{j} ( x _{j} - μ ) ^{2}}{\sum _{j = i - w + 1}^{i} w _{j}}$

Common applications:

Volatility analysis
Risk measurement
Quality control
Signal variability
Process stability monitoring
Uncertainty estimation
Market volatility tracking

Note: Square root of rolling variance. Measures spread of values around mean.

Table

Select

column

The numeric column to compute rolling standard deviation for. Typical uses:

Price volatility (market risk)
Process variation (quality control)
Measurement uncertainty
Performance variability
Environmental fluctuations

WindowSize

u32

Number of observations in sliding window. Common choices:

20: Monthly volatility (trading days)
60: Hour analysis from minute data
252: Annual volatility (trading days)

Larger windows smooth volatility estimates.

Weights

[f64, ...]

Optional weights for window values. Must match window size if provided. Uses:

EWMA volatility calculation
Time-weighted standard deviation
Custom volatility patterns

Affects contribution of each observation to spread calculation.

MinPeriods

oneof

AsWindow

Require complete windows for standard deviation calculation. Ensures statistical reliability by using full window of data. Important for accurate volatility measurement and risk assessment.

Period

Value

u32

Minimum number of valid observations (Value) required for std calculation. Examples:

2: Minimum for std calculation
window_size/2: Require half window
window_size: Require full window

More observations provide more reliable volatility estimates.

Center

bool

false

Window label position. When true, aligns to window center; when false, to end. Effects:

true: Better for historical volatility analysis
false: Better for real-time risk monitoring

Name for the new column. If not provided, the system generates a unique name. If AsColumn matches an existing column, the existing column is replaced. The name should follow valid column naming conventions.