Calculate the Average True Range (ATR) Easy with Pandas DataFrames

What will we cover in this tutorial?

In this tutorial we will cover the following

Step 1: Read historic stock prices from Yahoo! Finance API

To read data from Yahoo! Finance API we use Pandas-Datareader, which has a direct method.

This requires that we give a start date on how old data we want to retrieve.

import pandas_datareader as pdr
import datetime as dt

start = dt.datetime(2020, 1, 1)
data = pdr.get_data_yahoo("NFLX", start)

print(data.tail())

This we result in similar output.

	High	Low	Open	Close	Volume	Adj Close
Date						
2021-02-12	561.250000	550.849976	556.940002	556.520020	2195900	556.520020
2021-02-16	563.630005	552.729980	557.289978	557.280029	2622400	557.280029
2021-02-17	555.250000	543.030029	550.989990	551.340027	2069600	551.340027
2021-02-18	550.000000	538.229980	549.000000	548.219971	2456200	548.219971
2021-02-19	548.989990	538.809998	548.000000	540.219971	2838600	540.219971

Calculate the Average True Range (ATR)

The Average True Range (ATR) is calculated as follows, as investopedia.org defines it.

This can be calculated as follows.

import numpy as np
import pandas_datareader as pdr
import datetime as dt

start = dt.datetime(2020, 1, 1)
data = pdr.get_data_yahoo("NFLX", start)

high_low = data['High'] - data['Low']
high_close = np.abs(data['High'] - data['Close'].shift())
low_close = np.abs(data['Low'] - data['Close'].shift())

ranges = pd.concat([high_low, high_close, low_close], axis=1)
true_range = np.max(ranges, axis=1)

atr = true_range.rolling(14).sum()/14

Where we use the 14 days standard.

Visualize the ATR and the stock price

We will use Matplotlib to visualize it as it integrates well with DataFrames from Pandas.

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
atr.plot(ax=ax)
data['Close'].plot(ax=ax, secondary_y=True, alpha=0.3)
plt.show()

This will result in a chart similar to this one.

6 Replies to “Calculate the Average True Range (ATR) Easy with Pandas DataFrames”

    1. Hi Ricky,
      Yes, it could have been done by using rolling mean.
      Notice I did it with sum instead. Just to follow the formula from Investopedia.org.

      true_range.rolling(14).sum()/14

      Cheers,
      Rune

    1. Hi Geoff,
      Great point.
      It can be imported – but as we do not use any direct methods from pandas, it is enough to use it from the pandas_datareader.
      Cheers, Rune

    1. Hi Bahaa,
      Great to connect with you.
      Not sure what you are lookin for?
      This example used Pandas-datareader to get the data returned in a DataFrame (Pandas) to make the calculation.
      Cheers,
      Rune

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