Struct RollingQuantile 

pub struct RollingQuantile { /* private fields */ }

The quantile-th quantile of the last period values, with linear interpolation between order statistics.

h        = (period − 1) · quantile
lower    = ⌊h⌋
result   = sorted[lower] + (h − lower) · (sorted[lower + 1] − sorted[lower])

This is the type-7 / NumPy-default quantile definition: quantile = 0.0 returns the window minimum, 0.5 the median, 1.0 the maximum, and fractional values interpolate linearly between the bracketing order statistics. Rolling quantiles are the building block for distribution-aware thresholds — a price sitting above its rolling 90th-percentile, a volatility regime split at the 25th/75th percentiles, robust band edges that ignore the tails.

Each update is O(period log period): the window is copied into a scratch buffer and sorted with total ordering (NaN-safe).

Example

use wickra_core::{Indicator, RollingQuantile};

// Rolling median of the last 5 values.
let mut indicator = RollingQuantile::new(5, 0.5).unwrap();
let out = indicator.update(1.0);
assert!(out.is_none()); // warming up

Implementations

impl RollingQuantile

pub fn new(period: usize, quantile: f64) -> Result<Self>

Construct a new rolling quantile.

quantile selects the order statistic in [0.0, 1.0].

Errors

Returns Error::PeriodZero if period == 0, or Error::InvalidParameter if quantile is not a finite value in [0.0, 1.0].

pub const fn period(&self) -> usize

Configured period.

pub const fn quantile(&self) -> f64

Configured quantile in [0.0, 1.0].

Trait Implementations

impl Clone for RollingQuantile

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 RollingQuantile

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

Formats the value using the given formatter.

impl Indicator for RollingQuantile

type Input = f64

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, value: f64) -> 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.