From d051db1f14cbb0edd2b0db1e4edef76cce6c7823 Mon Sep 17 00:00:00 2001 From: Berat Osman Demiralay Date: Mon, 23 Oct 2023 16:25:07 +0300 Subject: [PATCH] Add Simple Moving Average (SMA) Calculation (#9300) * 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 * Update financial/simple_moving_average.py Co-authored-by: Tianyi Zheng * Update financial/simple_moving_average.py Co-authored-by: Tianyi Zheng * [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 Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- financial/simple_moving_average.py | 68 ++++++++++++++++++++++++++++++ 1 file changed, 68 insertions(+) create mode 100644 financial/simple_moving_average.py diff --git a/financial/simple_moving_average.py b/financial/simple_moving_average.py new file mode 100644 index 000000000..d5d68ffd3 --- /dev/null +++ b/financial/simple_moving_average.py @@ -0,0 +1,68 @@ +""" +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 +""" +from collections.abc import Sequence + + +def simple_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 + """ + if window_size < 1: + raise ValueError("Window size must be a positive integer") + + sma: list[float | None] = [] + + for i in range(len(data)): + if i < 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) + return sma + + +if __name__ == "__main__": + import doctest + + 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:") + for i, value in enumerate(sma_values): + if value is not None: + print(f"Day {i + 1}: {value:.2f}") + else: + print(f"Day {i + 1}: Not enough data for SMA")