Calculating Moving Averages in Python using Pandas: A Guide with Examples

Calculating moving averages is a common task in financial analysis and time series forecasting. In Python, there are several ways to calculate moving averages, but one of the most popular is to use the Pandas library. In this article, we will go through the steps to calculate a moving average using Pandas in Python.

What is a Moving Average?

A moving average is a statistical calculation that is used to analyze data points by creating a series of averages of different subsets of the full data set. In finance, a moving average is often used to identify trends and to smooth out fluctuations in stock prices or other financial data. There are different types of moving averages, such as simple moving average (SMA), exponential moving average (EMA), and weighted moving average (WMA).

Calculating a Simple Moving Average (SMA)

The most straightforward type of moving average is the simple moving average (SMA), which is calculated by taking the arithmetic mean of a set of values over a specified time period. To calculate the SMA in Python, we can use the rolling function from the Pandas library.

Here is an example code snippet that shows how to calculate the SMA of a dataset using Pandas:

import pandas as pd

# Define a sample dataset
data = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

# Convert the dataset into a Pandas dataframe
df = pd.DataFrame(data, columns=['Price'])

# Calculate the SMA using a rolling window of 3 periods
sma = df['Price'].rolling(window=3).mean()

# Print the result
print(sma)

In this example, we first define a sample dataset of 10 numbers. We then convert the dataset into a Pandas dataframe using the DataFrame function from the Pandas library. We then calculate the SMA using the rolling function, which takes the window size as an argument. In this case, we set the window size to 3 periods. Finally, we print the result, which is a Pandas series containing the SMA values.

Calculating an Exponential Moving Average (EMA)

Another popular type of moving average is the exponential moving average (EMA), which gives more weight to recent data points than older ones. The EMA is calculated using a formula that includes a smoothing factor that determines the weight given to each data point.

To calculate the EMA in Python, we can use the ewm function from the Pandas library. Here is an example code snippet that shows how to calculate the EMA of a dataset using Pandas:

import pandas as pd

# Define a sample dataset
data = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

# Convert the dataset into a Pandas dataframe
df = pd.DataFrame(data, columns=['Price'])

# Calculate the EMA using a smoothing factor of 0.3
ema = df['Price'].ewm(alpha=0.3).mean()

# Print the result
print(ema)

In this example, we first define a sample dataset of 10 numbers. We then convert the dataset into a Pandas dataframe using the DataFrame function from the Pandas library. We then calculate the EMA using the ewm function, which takes the smoothing factor as an argument. In this case, we set the smoothing factor to 0.3. Finally, we print the result, which is a Pandas series containing the EMA values.

Conclusion

In this article, we have shown how to calculate moving averages in Python using the Pandas library. We covered the two most common. I upcoming post I will write how to calculate ema, sma on data provided by Upstox API.