**How** **to** 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
print(data.head())
```

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['EMA50'] = data['Close'].ewm(span=50, adjust=False).mean()
data[['Close', 'MA50', 'EMA50']].plot(figsize=(10,8))
```

This should result in a chart similar to this one.

**Want** to learn more?

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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