feat: init the repo

src/statistic/correl.rs

@@ -0,0 +1,36 @@
+use crate::validation;
+use numpy::{IntoPyArray, PyArray1, PyReadonlyArray1};
+use pyo3::prelude::*;
+
+/// Pearson correlation coefficient between two series over the rolling window.
+#[pyfunction]
+#[pyo3(signature = (real0, real1, timeperiod = 30))]
+pub fn correl<'py>(
+    py: Python<'py>,
+    real0: PyReadonlyArray1<'py, f64>,
+    real1: PyReadonlyArray1<'py, f64>,
+    timeperiod: usize,
+) -> PyResult<Bound<'py, PyArray1<f64>>> {
+    validation::validate_timeperiod(timeperiod, "timeperiod", 1)?;
+    let x = real0.as_slice()?;
+    let y = real1.as_slice()?;
+    let n = x.len();
+    validation::validate_equal_length(&[(n, "real0"), (y.len(), "real1")])?;
+    let mut result = vec![f64::NAN; n];
+    for i in (timeperiod - 1)..n {
+        let wx = &x[(i + 1 - timeperiod)..=i];
+        let wy = &y[(i + 1 - timeperiod)..=i];
+        let mean_x: f64 = wx.iter().sum::<f64>() / timeperiod as f64;
+        let mean_y: f64 = wy.iter().sum::<f64>() / timeperiod as f64;
+        let cov: f64 = wx
+            .iter()
+            .zip(wy.iter())
+            .map(|(&xi, &yi)| (xi - mean_x) * (yi - mean_y))
+            .sum::<f64>();
+        let std_x: f64 = (wx.iter().map(|&xi| (xi - mean_x).powi(2)).sum::<f64>()).sqrt();
+        let std_y: f64 = (wy.iter().map(|&yi| (yi - mean_y).powi(2)).sum::<f64>()).sqrt();
+        let denom = std_x * std_y;
+        result[i] = if denom != 0.0 { cov / denom } else { f64::NAN };
+    }
+    Ok(result.into_pyarray(py))
+}