diff --git a/src/value_estimation_team/indicators/linear_regression.rs b/src/value_estimation_team/indicators/linear_regression.rs
index 2009589..87861f0 100644
--- a/src/value_estimation_team/indicators/linear_regression.rs
+++ b/src/value_estimation_team/indicators/linear_regression.rs
@@ -14,12 +14,14 @@ use tokio::{fs::*, io::AsyncWriteExt, sync::Mutex, time::*};
 #[derive(Clone, Debug)]
 pub struct LrData {
     pub lr_value: f64,  // linear regression value
+    pub r_squared: f64,
     pub close_time: i64,
 }
 impl LrData {
     fn new() -> LrData {
         let a = LrData {
             lr_value: 0.0,
+            r_squared: 0.0,
             close_time: 0,
         };
         a
@@ -49,10 +51,10 @@ pub async fn linear_regression(
             if vec.len() >= length {
                 let rt_price_data = vec.clone();
                 task_vec.push(tokio::spawn(async move {
-                    // Calculate prediction of linear regression
                     let mut lr_data_vec: Vec<LrData> = Vec::new();
                     
                     for window in rt_price_data.windows(length) {
+                        // Calculate prediction of linear regression
                         let mut lr_data = LrData::new();
                         let x: Vec<f64> = (0..length).map(|x| x as f64).collect();
                         let y: Vec<f64> = window.iter().map(|x| x.close_price).collect();
@@ -67,7 +69,18 @@ pub async fn linear_regression(
                         let intercept = y_mean - slope * x_mean;
                 
                         let linreg = intercept + slope * (length as f64 - 1.0 - offset as f64);
+
+                        // Calculate R-Squared
+                        let ss_tot: f64 = y.iter().map(|y_i| (y_i - y_mean).powi(2)).sum();
+                        let ss_res: f64 = y.iter().zip(x.iter()).map(|(y_i, x_i)| {
+                            let y_pred = intercept + slope * x_i;
+                            (y_i - y_pred).powi(2)
+                        }).sum();
+                        let r_squared = 1.0 - (ss_res / ss_tot);
+                        let r_squared_rounded = (r_squared * 100.0).round() / 100.0;
+                        
                         lr_data.lr_value = linreg;
+                        lr_data.r_squared = r_squared_rounded;
                         lr_data.close_time = window.last().unwrap().close_time;
                         lr_data_vec.push(lr_data.clone());
                     }