Challenge: Plot Moving Average
Moving averages are widely used in financial analysis to smooth out short-term fluctuations and highlight longer-term trends in price data. By averaging a set number of past data points, a moving average helps you see the underlying direction of a financial time series, making it easier to identify trends and potential turning points. This is especially useful when analyzing volatile markets, as it reduces noise and provides a clearer picture of price movement. The simple moving average (SMA) is the most basic type, calculated by taking the mean of a fixed window of consecutive data points as you move through the series.
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Write a function that takes a list of daily closing prices and a window size, then calculates the simple moving average (SMA) for the given window. Plot both the original prices and the SMA on the same matplotlib chart with a legend and axis labels.
- Calculate the SMA for each position in the list where enough data points are available.
- For positions where the SMA cannot be calculated (before enough data points), use
Noneas a placeholder. - Plot both the original price series and the SMA on a single chart.
- Add a legend, x-axis label, y-axis label, and a title to the plot.
- Raise a
ValueErrorif the window size is less than 1 or greater than the number of prices.
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Can you explain how to calculate a simple moving average with an example?
What are the differences between a simple moving average and other types of moving averages?
How can moving averages be used to make trading decisions?
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Moving averages are widely used in financial analysis to smooth out short-term fluctuations and highlight longer-term trends in price data. By averaging a set number of past data points, a moving average helps you see the underlying direction of a financial time series, making it easier to identify trends and potential turning points. This is especially useful when analyzing volatile markets, as it reduces noise and provides a clearer picture of price movement. The simple moving average (SMA) is the most basic type, calculated by taking the mean of a fixed window of consecutive data points as you move through the series.
Swipe to start coding
Write a function that takes a list of daily closing prices and a window size, then calculates the simple moving average (SMA) for the given window. Plot both the original prices and the SMA on the same matplotlib chart with a legend and axis labels.
- Calculate the SMA for each position in the list where enough data points are available.
- For positions where the SMA cannot be calculated (before enough data points), use
Noneas a placeholder. - Plot both the original price series and the SMA on a single chart.
- Add a legend, x-axis label, y-axis label, and a title to the plot.
- Raise a
ValueErrorif the window size is less than 1 or greater than the number of prices.
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