Struct PearsonCorrelation 

pub struct PearsonCorrelation { /* private fields */ }

Rolling Pearson correlation between two synchronised series.

Each update receives one (x, y) pair (e.g. the latest close of the asset and of the benchmark). Over the trailing window of period pairs:

cov_xy   = (1/n) · Σ x·y − x̄·ȳ
var_x    = (1/n) · Σ x² − x̄²
var_y    = (1/n) · Σ y² − ȳ²
Pearson  = cov_xy / √(var_x · var_y)

Output is in [−1, +1]. +1 means a perfect positive linear relationship; −1 is a perfect inverse one; 0 means no linear relationship. It is the same statistic SciPy / NumPy report as pearsonr and the standardised relative of crate::Beta — Beta scales Pearson by the ratio of standard deviations.

Each update is O(1): five running sums (Σx, Σy, Σx², Σy², Σxy) are maintained as the window slides. A flat series in either channel gives an undefined ratio; the indicator returns 0 in that case rather than producing NaN. The output is clamped to [−1, +1] to absorb tiny floating-point overshoots near the boundaries.

Example

use wickra_core::{Indicator, PearsonCorrelation};

let mut indicator = PearsonCorrelation::new(20).unwrap();
let mut last = None;
for i in 0..40 {
    last = indicator.update((f64::from(i), 2.0 * f64::from(i) + 1.0));
}
// A perfectly linear pair → +1.
assert!((last.unwrap() - 1.0).abs() < 1e-9);

Implementations

impl PearsonCorrelation

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

Construct a new rolling Pearson correlation.

Errors

Returns Error::InvalidPeriod if period < 2 — correlation is undefined for fewer than two pairs.

pub const fn period(&self) -> usize

Configured period.

impl PearsonCorrelation

pub fn batch_pairs_into(&mut self, a: &[f64], b: &[f64], out: &mut [f64])

Exact batch over two columns: one output per pair (NaN during warmup), bit for bit what replaying update gives, written into out.

Panics

Panics if a, b and out differ in length.

pub fn batch_pairs_fast_into(&mut self, a: &[f64], b: &[f64], out: &mut [f64])

Opt-in fast variant of batch_pairs_into: the shifted sums of a, b, a², b² and a·b run as SIMD prefix scans, re-centred every window like the exact accumulator, and the correlation is finished lane-parallel. Every value agrees with the exact batch to within a few units in the last place; warmup NaNs and length are identical, and the result is the same on every platform. Only a fresh indicator over finite values within 1e100, at least one window long, takes the kernel; anything else is the exact batch. The correlation only remembers its last period pairs, so afterwards the state is rebuilt exactly by replaying them.

Panics

Panics if a, b and out differ in length.

Trait Implementations

impl Clone for PearsonCorrelation

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 PearsonCorrelation

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

Formats the value using the given formatter.

impl Indicator for PearsonCorrelation

type Input = (f64, 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, input: (f64, 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.