Python ## How to Calculate Volatility as Average True Range (ATR) with Python DataFrames and NumPy

In this tutorial we will learn about the volatility of a stock price measured with Average True Range. The **Volatility** of a stock is one measure of **investment risk**.

The **Average True Range** was originally designed to calculate the volatility of commodities, but the technical indicator can also be used for stock prices, as we will do in this tutorial.

We will get some stock price time series data and make the calculation using pandas DataFrames and NumPy.

To get started, we need some historic stock prices. This can be done as follows and is covered in Lesson 1.

```
import numpy as np
import pandas_datareader as pdr
import datetime as dt
import pandas as pd
start = dt.datetime(2020, 1, 1)
data = pdr.get_data_yahoo("NFLX", start)
```

This reads the time series data of the historic stock prices of **Netflix** (ticker **NFLX**).

The above formula can look intimidating at first, but don’t worry, this is where the power of **Pandas** **DataFrames** and **Series** come into the picture.

It is always a good idea to make your calculations simple.

```
high_low = data['High'] - data['Low']
high_cp = np.abs(data['High'] - data['Close'].shift())
low_cp = np.abs(data['Low'] - data['Close'].shift())
```

Here we make a **Series** for each of the values needed. Notice, that we get the previous close by using **shift()** (**data[‘Close’].shift()**).

Then a great way to get the maximum value of these is to create a DataFrame with all the values.

```
df = pd.concat([high_low, high_cp, low_cp], axis=1)
true_range = np.max(df, axis=1)
```

Now that is nice.

Then we get the ATR as the moving average of 14 days (14 days is the default).

```
average_true_range = true_range.rolling(14).mean()
```

Finally, let’s try to visualize it. Often visualization helps us understand it better.

```
import matplotlib.pyplot as plt
%matplotlib notebook
fig, ax = plt.subplots()
average_true_range.plot(ax=ax)
ax2 = data['Close'].plot(ax=ax, secondary_y=True, alpha=.3)
ax.set_ylabel("ATR")
ax2.set_ylabel("Price")
```

Resulting in the following chart.

This is part of a 2.5-hour full video course in 8 parts about Risk and Return.

In the next lesson you will learn how to Calculate Sharpe Ratio with Pandas and NumPy.

Would you like to get 12% in return of your investments?

D. A. Carter promises and shows how his simple investment strategy will deliver that in the book **The 12% Solution**. The book shows how to test this statement by using backtesting.

**Did Carter find a strategy that will consistently beat the market?**

Actually, it is not that hard to use Python to validate his calculations. But we can do better than that. If you want to work smarter than traditional investors then continue to read here.

Build and Deploy an AI App with Python Flask, OpenAI API, and Google Cloud: In…

5 days ago

Python REST APIs with gcloud Serverless In the fast-paced world of application development, building robust…

5 days ago

App Development with Python using Docker Are you an aspiring app developer looking to level…

6 days ago

Why Value-driven Data Science is the Key to Your Success In the world of data…

2 weeks ago

Harnessing the Power of Project-Based Learning and Python for Machine Learning Mastery In today's data-driven…

2 weeks ago

Is Python the right choice for Machine Learning? Should you learn Python for Machine Learning?…

2 weeks ago