Simple and Exponential Moving Average with Python and Pandas

What will we cover in this tutorial?

In this tutorial we will cover how to calculate the Simple Moving Average (MA) and the Exponential Moving Average (EMA) of a Time Series using the Pandas library in Python.

Step 1: Read some Financial Historic Time Series Stock Prices

We will use Pandas Datareader to read some historic stock prices. See this tutorial for details.

import pandas_datareader as pdr
import datetime as dt
ticker = "AAPL"
start = dt.datetime(2019, 1, 1)
end = dt.datetime(2020, 12, 31)
data = pdr.get_data_yahoo(ticker, start, end)

Resulting in this.

                 High        Low       Open      Close       Volume  Adj Close
2019-01-02  39.712502  38.557499  38.722500  39.480000  148158800.0  38.505024
2019-01-03  36.430000  35.500000  35.994999  35.547501  365248800.0  34.669640
2019-01-04  37.137501  35.950001  36.132500  37.064999  234428400.0  36.149662
2019-01-07  37.207500  36.474998  37.174999  36.982498  219111200.0  36.069202
2019-01-08  37.955002  37.130001  37.389999  37.687500  164101200.0  36.756794

Step 2: Calculate the Simple Moving Average with Python and Pandas

To calculate the Simple Moving Average (MA) of the data can be done using the rolling and mean methods.

data['MA10'] = data['Close'].rolling(10).mean()

Where here we calculate the Simple Moving Average of 10 days. You can change it to fit your needs.

Step 3: Calculate the Exponential Moving Average with Python and Pandas

It is a bit more involved to calculate the Exponential Moving Average.

data['EMA10'] = data['Close'].ewm(span=10, adjust=False).mean()

There you need to set the span and adjust to False. This is needed to get the same numbers as on Yahoo! Finance.

Next steps?

Want to learn more?

This is part of the FREE online course on my page. No signup required and 2 hours of free video content with code and Jupyter Notebooks available on GitHub.

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