QuanTAlib/lib/statistics/covariance/Covariance.md

Covariance: Covariance

"Correlation is just covariance normalized by standard deviation. But sometimes you want the raw, unadulterated relationship."

Covariance measures the joint variability of two random variables. It indicates the direction of the linear relationship between variables.

Architecture & Physics

Covariance is calculated using a sliding window approach. It maintains running sums of x, y, and xy to allow for O(1) updates.

• Positive Covariance: Indicates that the two variables tend to move in the same direction.
• Negative Covariance: Indicates that the two variables tend to move in opposite directions.
• Zero Covariance: Indicates that the two variables are uncorrelated.

Mathematical Foundation

1. Population Covariance

Cov(X, Y) = \frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y})}{n}

2. Sample Covariance

Cov(X, Y) = \frac{\sum_{i=1}^{n} (x_i - \bar{x})(y_i - \bar{y})}{n - 1}

3. Computational Formula (Running Sums)

Cov(X, Y) = \frac{\sum xy - \frac{(\sum x)(\sum y)}{n}}{n} \quad \text{(or } n-1 \text{)}

Performance Profile

Metric	Score	Notes
Throughput	High	O(1) updates using running sums.
Allocations	0	No heap allocations in hot path.
Complexity	O(1)	Constant time update regardless of period.
Accuracy	High	Uses double precision; periodic resync prevents drift.

Validation

Library	Status	Notes
Manual	✅	Verified against manual calculation.
Excel	✅	Matches COVARIANCE.P and COVARIANCE.S.

Usage

using QuanTAlib;

// Create a Covariance indicator with period 20 (Sample Covariance by default)
var cov = new Covariance(20);

// Update with new values
cov.Update(price1, price2);

// Access the result
double result = cov.Last.Value;