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# Simple and Exponential Moving Average with Python and Pandas

## Howto Calculate the Moving Average with Pyhton

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.

We will also take a brief look at what the Simple Moving Average and the Exponential Aveareg is.

## What is the Moving Average?

The Moving Average is calcualted to identify the trend direction of a stock.

• The Moving Average can be used to determine the support and restance levels.
• Often categorized as a lagging indicator, as it is trendfollowing.
• The longer period used, the greater the lag.
• The 50-day and 200-day Moving Averages are considered by many the as important trading signals.
• The Simple Moving Average is taking the arithmetic mean over a specific number of days.
• The Exponential Moving Average is a weighted average with more importance in recent days. This makes it a more responsive indicator.

## Watch how to calculate the Moving Average with Python

See the video to see how it can be done using the Python library pandas or see the code below.

## 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
from datetime import datetime
ticker = "AAPL"
start = datetime(2019, 1, 1)
data = pdr.get_data_yahoo(ticker, start)  # You can also add end-date
```

Resulting in this.

```                 High        Low       Open      Close       Volume  Adj Close
Date
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

Calculating 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 it to False. This is needed to get the same numbers as on Yahoo! Finance.

## Bonus: Visualize the result

If you want to see the data on a chart you can use the following code.

```import pandas_datareader as pdr
from datetime import datetime
import matplotlib.pyplot as plt
data = pdr.get_data_yahoo('AAPL', datetime(2021,1,1))
data['MA50'] = data['Close'].rolling(50).mean()
data[['Close', 'MA50', 'EMA50']].plot(figsize=(10,8))
```

This should result in a chart similar to this one.

This is part of the course of Master Technical Analysis with pandas.

In the next lesson you will learn about Calculating MACD with Pandas DataFrames.

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