Struct PairedStatistics

Source

pub struct PairedStatistics<T> { /* private fields */ }

Expand description

A structure that computes various statistics over a fixed-size window of paired values.

PairedStatistics<T> maintains a circular buffer of paired values and computes statistical measures such as covariance, correlation, beta, etc.

The structure automatically updates statistics as new values are added and old values are removed from the window, making it efficient for rolling statistics analysis.

Implementations§

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impl<T> PairedStatistics<T>
where T: Default + Clone + Float,

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pub fn new(period: usize) -> Self

Creates a new PairedStatistics instance with the specified period.

§Arguments

period - The period of the statistics

§Returns

Self - The PairedStatistics instance

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pub fn period(&self) -> usize

Returns the period of the statistics

§Returns

usize - The period of the statistics

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pub fn reset(&mut self) -> &mut Self

Resets the statistics

§Returns

&mut Self - The statistics object

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pub fn recompute(&mut self) -> &mut Self

Recomputes the paired statistics, could be called to avoid prolonged compounding of floating rounding errors

§Returns

&mut Self - The rolling moments object

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pub fn next(&mut self, (x, y): (T, T)) -> &mut Self

Updates the paired statistical calculations with a new value pair in the time series

Incorporates a new data point pair into the rolling window, maintaining the specified window size by removing the oldest pair when necessary. This core method provides the foundation for all paired statistical measures that examine relationships between two variables.

§Arguments

value - A tuple containing the paired values (x, y) to incorporate into calculations

§Returns

&mut Self - The updated statistics object for method chaining

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pub const fn ddof(&self) -> bool

Returns the Delta Degrees of Freedom

§Returns

bool - The Delta Degrees of Freedom

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pub const fn set_ddof(&mut self, ddof: bool) -> &mut Self

Sets the Delta Degrees of Freedom

§Arguments

ddof - The Delta Degrees of Freedom

§Returns

&mut Self - The statistics object

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pub fn cov(&self) -> Option<T>

Returns the covariance of the paired values in the rolling window

Covariance measures how two variables change together, indicating the direction of their linear relationship. This fundamental measure of association provides:

Directional relationship analysis between paired time series
Foundation for correlation, beta, and regression calculations
Raw measurement of how variables move in tandem
Basis for portfolio diversification and risk assessments

§Returns

Option<T> - The covariance of the values in the window, or None if the window is not full

§Examples

use ta_statistics::PairedStatistics;
use assert_approx_eq::assert_approx_eq;

let mut stats = PairedStatistics::new(3);
let mut results = vec![];
let inputs = [(2.0, 1.0), (4.0, 3.0), (6.0, 2.0), (8.0, 5.0), (10.0, 7.0)];
inputs.iter().for_each(|i| {
    stats.next(*i).cov().map(|v| results.push(v));
});

let expected: [f64; 3] = [0.6667, 1.3333, 3.3333];
 for (i, e) in expected.iter().enumerate() {
 assert_approx_eq!(e, results[i], 0.001);
 }

stats.reset().set_ddof(true);
results = vec![];
inputs.iter().for_each(|i| {
    stats.next(*i).cov().map(|v| results.push(v));
});

let expected: [f64; 3] = [1.0, 2.0, 5.0];
for (i, e) in expected.iter().enumerate() {
   assert_approx_eq!(e, results[i], 0.0001);
}

Source

pub fn corr(&self) -> Option<T>

Returns the correlation coefficient (Pearson’s r) of paired values in the rolling window

Correlation normalizes covariance by the product of standard deviations, producing a standardized measure of linear relationship strength between -1 and 1:

Quantifies the strength and direction of relationships between variables
Enables cross-pair comparison on a standardized scale
Provides the foundation for statistical arbitrage models
Identifies regime changes in intermarket relationships

§Returns

Option<T> - The correlation coefficient in the window, or None if the window is not full

§Examples

use ta_statistics::PairedStatistics;
use assert_approx_eq::assert_approx_eq;

let mut stats = PairedStatistics::new(3);
let mut results = vec![];
let inputs = [
    (0.496714, 0.115991),
    (-0.138264, -0.329650),
    (0.647689, 0.574363),
    (1.523030, 0.109481),
    (-0.234153, -1.026366),
    (-0.234137, -0.445040),
    (1.579213, 0.599033),
    (0.767435, 0.694328),
    (-0.469474, -0.782644),
    (0.542560, -0.326360)
];

inputs.iter().for_each(|i| {
    stats.next(*i).corr().map(|v| results.push(v));
});
let expected: [f64; 8] = [0.939464, 0.458316, 0.691218, 0.859137, 0.935658, 0.858379, 0.895148, 0.842302,];
for (i, e) in expected.iter().enumerate() {
    assert_approx_eq!(e, results[i], 0.0001);
}

Source

pub fn beta(&self) -> Option<T>

Returns the beta coefficient of the paired values in the rolling window

Beta measures the relative volatility between two time series, indicating the sensitivity of one variable to changes in another:

Quantifies systematic risk exposure between related instruments
Determines optimal hedge ratios for risk management
Provides relative sensitivity analysis for pair relationships
Serves as a key input for factor modeling and attribution analysis

§Returns

Option<T> - The beta coefficient in the window, or None if the window is not full

§Examples

use ta_statistics::PairedStatistics;
use assert_approx_eq::assert_approx_eq;

let mut stats = PairedStatistics::new(3);
let mut results = vec![];
let inputs = [
     (0.015, 0.010),
     (0.025, 0.015),
     (-0.010, -0.005),
     (0.030, 0.020),
     (0.005, 0.010),
     (-0.015, -0.010),
     (0.020, 0.015),
];

inputs.iter().for_each(|i| {
    stats.next(*i).beta().map(|v| results.push(v));
});

let expected: [f64; 5] = [1.731, 1.643, 1.553, 1.429, 1.286];
for (i, e) in expected.iter().enumerate() {
    assert_approx_eq!(e, results[i], 0.001);
}