finished simple moving averate

README.md

@@ -20,7 +20,9 @@ this is from Mannings "Building a Stock-Tracking CLI With Async Streams in Rust"
 4. Calculate performance indicators for the given period.
 - **OK** A period is the time between the “from” date and the current date
 - **OK** Aggregate the closing (adjclose) prices and find their minimum (fn min(series: &[f64]) -> Option<`f64`>) and maximum (fn max(series: &[f64]) -> Option<`f64`>) across the period. What data structures and types from the standard library can you use?
-- Calculate a simple moving average over the entire series. Here is the recommended function interface: fn n_window_sma(n: usize, series: &[f64]) -> Option<Vec<`f64`>>, where the series parameter is a std::slice with one value per day.
+- **OK** Calculate a simple moving average over the entire series. Here is the recommended function interface: fn n_window_sma(n: usize, series: &[f64]) -> Option<Vec<`f64`>>, where the series parameter is a std::slice with one value per day.
+
+
  - Using the function interface fn price_diff(series: &[f64]) -> Option<(f64, f64)>, return two price differences: one as a percentage of the starting price, one as an absolute difference between the first and the last price of the period.
 
 5. The company’s data pipeline expects a CSV file for input, so you decide to print the results in that format to stdout:

src/main.rs

@@ -12,6 +12,19 @@ struct Stock {
     quotes: Vec<yahoo::Quote>,
 }
 
+fn n_window_sma(n: usize, series: &[f64]) -> Option<Vec<f64>> {
+    if !series.is_empty() && n > 1 {
+        Some(
+            series
+                .windows(n)
+                .map(|w| w.iter().sum::<f64>() / w.len() as f64)
+                .collect(),
+        )
+    } else {
+        None
+    }
+}
+
 fn max(series: &[f64]) -> Option<f64> {
     let mut max_found: Option<f64> = None;
     for s in series.iter() {
@@ -159,4 +172,19 @@ mod tests {
 
         assert_eq!(result, expected);
     }
+
+    #[test]
+    fn test_n_window_sma() {
+        let input_usize: usize = 3;
+        let input_series: &[f64] = &[1.0, 2.0, 3.5, 4.5, 12.2];
+        let expected = Some(vec![
+            2.1666666666666665,
+            3.3333333333333335,
+            6.733333333333333,
+        ]);
+
+        let actual = n_window_sma(input_usize, input_series);
+
+        assert_eq!(actual, expected);
+    }
 }