Struct YangZhangVolatility 

pub struct YangZhangVolatility { /* private fields */ }

Yang-Zhang Volatility — combines overnight, open-to-close and Rogers-Satchell volatilities into a single drift- and gap-robust estimator.

Yang & Zhang (2000) showed that the three estimators below are independent under a driftless GBM with overnight gaps, so a convex combination of their (sample) variances has minimum estimation variance at a specific blending factor k:

k        = 0.34 / (1.34 + (n + 1) / (n − 1))
σ²_on    = sample_var(ln(O_t / C_{t-1})    over n bars)         // overnight
σ²_oc    = sample_var(ln(C_t / O_t)        over n bars)         // open-to-close
σ²_rs    = mean(ln(H/C)·ln(H/O) + ln(L/C)·ln(L/O) over n bars)  // Rogers-Satchell
σ²_YZ    = σ²_on + k · σ²_oc + (1 − k) · σ²_rs
out      = √max(σ²_YZ, 0) · √trading_periods · 100

The “sample” variance uses Bessel’s correction (divisor n − 1), the same convention as HistoricalVolatility.

This is the gold-standard OHLC estimator for assets with both overnight gaps and intraday drift — equities, futures, and any market that doesn’t trade continuously. For pure intraday data (where C_{t-1} == O_t), the overnight term vanishes and Rogers-Satchell alone is sufficient.

Example

use wickra_core::{Candle, Indicator, YangZhangVolatility};

let mut indicator = YangZhangVolatility::new(20, 252).unwrap();
let mut last = None;
for i in 0..40 {
    let base = 100.0 + f64::from(i);
    let candle = Candle::new(base, base + 2.0, base - 2.0, base + 0.5, 1.0, i64::from(i))
        .unwrap();
    last = indicator.update(candle);
}
assert!(last.is_some());

Implementations

impl YangZhangVolatility

pub fn new(period: usize, trading_periods: usize) -> Result<Self>

Construct a Yang-Zhang Volatility estimator.

period is the rolling window of bars; trading_periods is the annualisation factor (252 daily, 52 weekly, 12 monthly, or 1 for raw per-bar volatility).

Errors

Returns Error::PeriodZero if either parameter is 0, or Error::InvalidPeriod if period < 2 (the sample variances inside Yang-Zhang need at least two samples).

pub const fn periods(&self) -> (usize, usize)

Configured (period, trading_periods).

pub const fn value(&self) -> Option<f64>

Current value if available.

pub const fn k(&self) -> f64

The Yang-Zhang blending factor k for this configuration.

Trait Implementations

impl Clone for YangZhangVolatility

fn clone(&self) -> Self

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for YangZhangVolatility

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Indicator for YangZhangVolatility

type Input = Candle

Type of one input data point (typically f64 for a price, or Candle / Tick).

type Output = f64

Type of one output value.

fn update(&mut self, candle: Candle) -> Option<f64>

Feed one new data point into the indicator and return the freshly computed output, or None if there is no value for this input.

fn reset(&mut self)

Reset all internal state, leaving the indicator equivalent to a freshly constructed instance with the same parameters.

fn warmup_period(&self) -> usize

Number of inputs required before the first non-None output can be produced.

fn is_ready(&self) -> bool

Whether the indicator has emitted at least one value since the last reset.

fn name(&self) -> &'static str

Stable, human-readable indicator name. Used by chaining and diagnostics.

fn batch_nan_into(&mut self, inputs: &[Self::Input], out: &mut [f64])

where Self::Input: Copy, Self::Output: Into<f64>,

Run the indicator over inputs, writing one output per input into the caller-owned out buffer (NaN where update returns None). It exists for every indicator whose output converts to an f64; the batch pipelines use it for the scalar f64 -> f64 ones.

fn batch_fast_into(&mut self, inputs: &[Self::Input], out: &mut [f64])

where Self::Input: Copy, Self::Output: Into<f64>,

Opt-in fast batch: like batch_nan_into, but an indicator with a vectorised kernel may reassociate its arithmetic to run it in SIMD lanes. Each value then agrees with the exact batch to within the tolerance the indicator documents (a few units in the last place), not bit for bit; warmup positions, NaN placement and the output length are identical. The kernels are deterministic: the same input produces the same bits on every platform, with or without SIMD hardware.