code clean up

Author: PoojanSmart

Diff for: financial/exponential_moving_average.py

@@ -3,35 +3,38 @@
     Wikipedia Reference: https://en.wikipedia.org/wiki/Exponential_smoothing
     Reference: https://www.investopedia.com/terms/e/ema.asp#toc-what-is-an-exponential-moving-average-ema
 
-    Exponential moving average is used in financial field to analyze changes stock prices.
-    EMA is used in conjuction with Simple moving average (SMA), EMA reacts to the changes in
-    the value quicker than SMA, which is one of the advantages of using EMA.
+    Exponential moving average is used in finance to analyze changes stock prices.
+    EMA is used in conjuction with Simple moving average (SMA), EMA reacts to the
+    changes inthe value quicker than SMA, which is one of the advantages of using EMA.
 """
 
-import numpy as np
 
-def exponential_moving_average(series : list[float], window_size : int) -> list [float]:
-    '''
-        :param series: Array of numbers (Time series data)
-        :param window_size: Window size for calculating average (window_size > 0)
-        :return: Resulting array of exponentially averaged numbers
+def exponential_moving_average(series: list[float], window_size: int) -> list[float]:
+    """
+    Returns the exponential moving average of the given array list
+    >>> exponential_moving_average([2, 5, 3, 8.2, 6, 9, 10], 3)
+    [2.0, 3.5, 3.25, 5.725, 5.8625, 7.43125, 8.715625]
 
-        Formula: 
+    :param series: Array of numbers (Time series data)
+    :param window_size: Window size for calculating average (window_size > 0)
+    :return: Resulting array of exponentially averaged numbers
 
-        st = alpha * xt + (1 - alpha) * st_prev
-        alpha = 2/(1 + window_size) - smoothing factor
+    Formula:
 
-        Exponential moving average (EMA) is a rule of thumb technique for
-        smoothing time series data using the exponential window function.
-    '''
+    st = alpha * xt + (1 - alpha) * st_prev
+    alpha = 2/(1 + window_size) - smoothing factor
 
-    if (window_size <= 0):
+    Exponential moving average (EMA) is a rule of thumb technique for
+    smoothing time series data using the exponential window function.
+    """
+
+    if window_size <= 0:
         raise ValueError("window_size must be > 0")
-    elif (window_size >= len(series)):
+    elif window_size >= len(series):
         raise ValueError("window_size must be < length of series")
 
     # Resultent array
-    exp_averaged_arr : list[float] = []
+    exp_averaged_arr: list[float] = []
 
     # Calculating smoothing factor
     alpha = 2 / (1 + window_size)
@@ -50,18 +53,18 @@ def exponential_moving_average(series : list[float], window_size : int) -> list
             # point and previous exponential average value
             st = (alpha * series[t]) + ((1 - alpha) * st)
             exp_averaged_arr.append(st)
-    
+
     return exp_averaged_arr
 
+
 if __name__ == "__main__":
     import doctest
 
     doctest.testmod()
 
-    test_series = [2,5,3,8.2,6,9,10]
+    test_series = [2, 5, 3, 8.2, 6, 9, 10]
     test_window_size = 3
     result = exponential_moving_average(test_series, test_window_size)
     print("Test series: ", test_series)
     print("Window size: ", test_window_size)
     print("Result: ", result)
-