diff --git a/benches/traquer.rs b/benches/traquer.rs
index c532c70..4302cd8 100644
--- a/benches/traquer.rs
+++ b/benches/traquer.rs
@@ -611,6 +611,9 @@ fn criterion_benchmark(c: &mut Criterion) {
             black_box(correlation::hoeffd(&stats.close, &stats.close, 16).collect::<Vec<_>>())
         })
     });
+    c.bench_function("correlation-dcor", |b| {
+        b.iter(|| black_box(correlation::dcor(&stats.close, &stats.close, 16).collect::<Vec<_>>()))
+    });
 
     c.bench_function("stats-dist-variance", |b| {
         b.iter(|| {
diff --git a/src/correlation.rs b/src/correlation.rs
index a7cf639..82275e1 100644
--- a/src/correlation.rs
+++ b/src/correlation.rs
@@ -413,3 +413,101 @@ pub fn hoeffd<'a, T: ToPrimitive + PartialOrd + Clone>(
             }),
     )
 }
+
+/// Distance Correlation
+///
+/// Measures both linear and nonlinear association between two random variables or random vectors.
+/// Not to be confused with correlation distance which is related to Pearson Coefficient[3].
+///
+/// ## Usage
+///
+/// Generates a value between 0 and 1 where 0 implies series are independent and 1 implies they are
+/// surely equal.
+///
+/// ## Sources
+///
+/// [[1]](https://en.wikipedia.org/wiki/Distance_correlation)
+/// [[2]](https://www.freecodecamp.org/news/how-machines-make-predictions-finding-correlations-in-complex-data-dfd9f0d87889/)
+/// [[3]](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#Pearson's_distance)
+///
+/// # Examples
+///
+/// ```
+/// use traquer::correlation;
+///
+/// correlation::dcor(
+///     &[1.0,2.0,3.0,4.0,5.0,6.0,4.0,5.0],
+///     &[1.0,2.0,3.0,4.0,5.0,6.0,4.0,5.0],
+///     6).collect::<Vec<f64>>();
+///
+/// ```
+pub fn dcor<'a, T: ToPrimitive>(
+    series1: &'a [T],
+    series2: &'a [T],
+    window: usize,
+) -> impl Iterator<Item = f64> + 'a {
+    fn centred_matrix<T: ToPrimitive>(x: &[T]) -> Vec<f64> {
+        let n = x.len();
+        // flattened NxN distance matrix, where [x_00..x0j, ... ,x_i0..x_ij]
+        let mut matrix = vec![0.0; n * n];
+        for i in 0..n {
+            for j in 0..n {
+                matrix[(i * n) + j] = (x[i].to_f64().unwrap() - x[j].to_f64().unwrap()).abs();
+            }
+        }
+
+        // "double-centre" the matrix
+        let row_means: Vec<f64> = (0..matrix.len())
+            .step_by(n)
+            .map(|i| matrix[i..i + n].iter().sum::<f64>() / n as f64)
+            .collect();
+        let col_means: Vec<f64> = (0..n)
+            .map(|i| {
+                (i..matrix.len())
+                    .step_by(n)
+                    .fold(0.0, |acc, j| acc + matrix[j])
+                    / n as f64
+            })
+            .collect();
+        let matrix_mean: f64 = matrix.iter().sum::<f64>() / (n * n) as f64;
+        for i in 0..n {
+            for j in 0..n {
+                matrix[(i * n) + j] += -row_means[i] - col_means[j] + matrix_mean;
+            }
+        }
+        matrix
+    }
+
+    iter::repeat(f64::NAN).take(window - 1).chain(
+        series1
+            .windows(window)
+            .zip(series2.windows(window))
+            .map(move |(x_win, y_win)| {
+                let centred_x = centred_matrix(x_win);
+                let centred_y = centred_matrix(y_win);
+                let dcov = (centred_x
+                    .iter()
+                    .zip(&centred_y)
+                    .map(|(a, b)| a * b)
+                    .sum::<f64>()
+                    / window.pow(2) as f64)
+                    .sqrt();
+                let dvar_x = (centred_x
+                    .iter()
+                    .zip(&centred_x)
+                    .map(|(a, b)| a * b)
+                    .sum::<f64>()
+                    / window.pow(2) as f64)
+                    .sqrt();
+                let dvar_y = (centred_y
+                    .iter()
+                    .zip(&centred_y)
+                    .map(|(a, b)| a * b)
+                    .sum::<f64>()
+                    / window.pow(2) as f64)
+                    .sqrt();
+
+                dcov / (dvar_x * dvar_y).sqrt()
+            }),
+    )
+}
diff --git a/tests/correlation_test.rs b/tests/correlation_test.rs
index 85a5da6..7e1efb4 100644
--- a/tests/correlation_test.rs
+++ b/tests/correlation_test.rs
@@ -244,3 +244,46 @@ fn test_hoeffd() {
         result[16 - 1..]
     );
 }
+
+#[test]
+fn test_dcor() {
+    let stats = common::test_data();
+    let stats2 = common::test_data_path("./tests/sp500.input");
+    let ln_ret1 = stats
+        .close
+        .iter()
+        .zip(&stats.close[1..])
+        .map(|(x, y)| (y / x).ln())
+        .collect::<Vec<f64>>();
+    let ln_ret2 = stats2
+        .close
+        .iter()
+        .zip(&stats2.close[1..])
+        .map(|(x, y)| (y / x).ln())
+        .collect::<Vec<f64>>();
+    let result = correlation::dcor(&ln_ret1, &ln_ret2, 16).collect::<Vec<_>>();
+    assert_eq!(ln_ret1.len(), result.len());
+    assert_eq!(
+        vec![
+            0.39390614319365574,
+            0.39845847602318907,
+            0.4012684752778961,
+            0.4532725521408008,
+            0.5335623994772698,
+            0.5899262972738498,
+            0.6886450053961184,
+            0.7578847633388898,
+            0.7748853182014356,
+            0.7670646492585647,
+            0.8058436110412499,
+            0.818014456822133,
+            0.8064793069072755,
+            0.687572965245447,
+            0.6362198562198043,
+            0.5628963827860937,
+            0.5699710776508861,
+            0.44892542156220505
+        ],
+        result[16 - 1..]
+    );
+}