ta_statistics

Trait PairedStatistics

Source 
pub trait PairedStatistics<T> {
    // Required methods
    fn next(&mut self, value: (T, T)) -> &mut Self
       where Self: Sized;
    fn cov(&self) -> Option<T>;
    fn corr(&self) -> Option<T>;
    fn beta(&self) -> Option<T>;
}
Expand description

Paired statistics trait for calculating statistics on pairs of values

This trait provides a foundation for calculating various statistics on pairs of values, such as covariance, correlation, and beta. It is used as a base trait for more specific statistical calculations that examine relationships between two variables.

Required Methods§

Source

fn next(&mut self, value: (T, T)) -> &mut Self
where Self: Sized,

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
Source

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::{Statistics, PairedStatistics};
use assert_approx_eq::assert_approx_eq;

let mut stats = Statistics::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], 0.1);
 }

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], 0.1);
}
Source

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::{Statistics, PairedStatistics};
use assert_approx_eq::assert_approx_eq;

let mut stats = Statistics::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], 0.1);
}
Source

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::{Statistics, PairedStatistics};
use assert_approx_eq::assert_approx_eq;

let mut stats = Statistics::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], 0.1);
}

Implementors§