simple_moving_average.py

"""
The Simple Moving Average (SMA) is a statistical calculation used to analyze data points
by creating a constantly updated average price over a specific time period.
In finance, SMA is often used in time series analysis to smooth out price data
and identify trends.

Reference: https://en.wikipedia.org/wiki/Moving_average
"""

fromcollections.abcimportSequence


defsimple_moving_average(
data: Sequence[float], window_size: int
) ->list[float|None]:
"""
 Calculate the simple moving average (SMA) for some given time series data.

 :param data: A list of numerical data points.
 :param window_size: An integer representing the size of the SMA window.
 :return: A list of SMA values with the same length as the input data.

 Examples:
 >>> sma = simple_moving_average([10, 12, 15, 13, 14, 16, 18, 17, 19, 21], 3)
 >>> [round(value, 2) if value is not None else None for value in sma]
 [None, None, 12.33, 13.33, 14.0, 14.33, 16.0, 17.0, 18.0, 19.0]
 >>> simple_moving_average([10, 12, 15], 5)
 [None, None, None]
 >>> simple_moving_average([10, 12, 15, 13, 14, 16, 18, 17, 19, 21], 0)
 Traceback (most recent call last):
 ...
 ValueError: Window size must be a positive integer
 """
ifwindow_size<1:
raiseValueError("Window size must be a positive integer")

sma: list[float|None] = []

foriinrange(len(data)):
ifi<window_size-1:
sma.append(None) # SMA not available for early data points
else:
window=data[i-window_size+1 : i+1]
sma_value=sum(window) /window_size
sma.append(sma_value)
returnsma


if__name__=="__main__":
importdoctest

doctest.testmod()

# Example data (replace with your own time series data)
data= [10, 12, 15, 13, 14, 16, 18, 17, 19, 21]

# Specify the window size for the SMA
window_size=3

# Calculate the Simple Moving Average
sma_values=simple_moving_average(data, window_size)

# Print the SMA values
print("Simple Moving Average (SMA) Values:")
fori, valueinenumerate(sma_values):
ifvalueisnotNone:
print(f"Day {i+1}: {value:.2f}")
else:
print(f"Day {i+1}: Not enough data for SMA")