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* Add Simple Moving Average (SMA) Calculation This commit adds a Python script for calculating the Simple Moving Average (SMA) of a time series data. The script also includes a doctest that verifies the correctness of the SMA calculations for a sample dataset. Usage: - Run the script with your own time series data and specify the window size for SMA calculations. * Update financial/simple_moving_average.py Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com> * Update financial/simple_moving_average.py Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com> * Update financial/simple_moving_average.py Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com> * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update simple_moving_average.py * Update financial/simple_moving_average.py * Update simple_moving_average.py * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: Tianyi Zheng <tianyizheng02@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
"""
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The Simple Moving Average (SMA) is a statistical calculation used to analyze data points
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by creating a constantly updated average price over a specific time period.
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In finance, SMA is often used in time series analysis to smooth out price data
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and identify trends.
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Reference: https://en.wikipedia.org/wiki/Moving_average
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"""
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from collections.abc import Sequence
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def simple_moving_average(
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data: Sequence[float], window_size: int
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) -> list[float | None]:
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"""
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Calculate the simple moving average (SMA) for some given time series data.
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:param data: A list of numerical data points.
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:param window_size: An integer representing the size of the SMA window.
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:return: A list of SMA values with the same length as the input data.
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Examples:
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>>> sma = simple_moving_average([10, 12, 15, 13, 14, 16, 18, 17, 19, 21], 3)
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>>> [round(value, 2) if value is not None else None for value in sma]
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[None, None, 12.33, 13.33, 14.0, 14.33, 16.0, 17.0, 18.0, 19.0]
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>>> simple_moving_average([10, 12, 15], 5)
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[None, None, None]
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>>> simple_moving_average([10, 12, 15, 13, 14, 16, 18, 17, 19, 21], 0)
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Traceback (most recent call last):
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...
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ValueError: Window size must be a positive integer
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"""
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if window_size < 1:
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raise ValueError("Window size must be a positive integer")
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sma: list[float | None] = []
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for i in range(len(data)):
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if i < window_size - 1:
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sma.append(None) # SMA not available for early data points
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else:
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window = data[i - window_size + 1 : i + 1]
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sma_value = sum(window) / window_size
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sma.append(sma_value)
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return sma
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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# Example data (replace with your own time series data)
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data = [10, 12, 15, 13, 14, 16, 18, 17, 19, 21]
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# Specify the window size for the SMA
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window_size = 3
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# Calculate the Simple Moving Average
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sma_values = simple_moving_average(data, window_size)
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# Print the SMA values
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print("Simple Moving Average (SMA) Values:")
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for i, value in enumerate(sma_values):
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if value is not None:
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print(f"Day {i + 1}: {value:.2f}")
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else:
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print(f"Day {i + 1}: Not enough data for SMA")
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