Struct LeadLagCrossCorrelation 

pub struct LeadLagCrossCorrelation { /* private fields */ }

Rolling lead–lag cross-correlation between two synchronised series.

Each update receives one (a, b) pair. The indicator keeps the most recent window + 2·max_lag samples of each series and, once full, reports the integer offset k ∈ [−max_lag, +max_lag] that maximises the absolute Pearson correlation between a and a copy of b shifted by k:

lag = argmax_k | corr( a[t], b[t+k] ) |

This answers “does BTC lead ETH on this timescale, and by how many bars?”. A positive lag means a leads b; a negative lag means b leads a. The reported correlation is the signed correlation at that lag, so its sign tells you whether the lead relationship is positive or inverse.

The comparison is fully causal: a’s window is held fixed in the centre of the buffer and b’s window slides across it, so every lag — positive and negative — is evaluated only against data already seen. The candidate lags are scanned in order of increasing |k|, so ties resolve to the smallest absolute offset (lag 0 wins an exact tie).

Each update is O(window · max_lag) — proportional to the fixed parameters, not the series length. A flat window in either channel makes a correlation undefined; it is reported as 0 rather than NaN.

Feed raw prices or returns depending on your convention; lead–lag on returns is the more common choice for relating two assets.

Example

use wickra_core::{Indicator, LeadLagCrossCorrelation};

let mut ll = LeadLagCrossCorrelation::new(12, 5).unwrap();
let mut last = None;
for t in 0..60 {
    let a = (f64::from(t) * 0.4).sin() + 0.4 * (f64::from(t) * 1.1).sin();
    // `b` is `a` delayed by 3 samples, so `a` leads `b` by 3.
    let b = (f64::from(t - 3) * 0.4).sin() + 0.4 * (f64::from(t - 3) * 1.1).sin();
    last = ll.update((a, b));
}
let out = last.unwrap();
assert_eq!(out.lag, 3);
assert!(out.correlation > 0.99);

Implementations

impl LeadLagCrossCorrelation

pub fn new(window: usize, max_lag: usize) -> Result<Self>

Construct a new lead–lag cross-correlation.

window is the number of overlapping points each correlation is computed over; max_lag is the largest offset (in either direction) that is searched.

Errors

Returns Error::InvalidPeriod if window < 2 or max_lag == 0.

pub const fn window(&self) -> usize

Number of overlapping points per correlation.

pub const fn max_lag(&self) -> usize

Largest offset searched in either direction.

Trait Implementations

impl Clone for LeadLagCrossCorrelation

fn clone(&self) -> LeadLagCrossCorrelation

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for LeadLagCrossCorrelation

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

Formats the value using the given formatter.

impl Indicator for LeadLagCrossCorrelation

type Input = (f64, f64)

(a, b) pair.

type Output = LeadLagCrossCorrelationOutput

Type of one output value.

fn update(&mut self, input: (f64, f64)) -> Option<LeadLagCrossCorrelationOutput>

Feed one new data point into the indicator and return the freshly computed output, or None if the indicator is still warming up.

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.