Stock Return Heatmap using Seaborn

Hi all, this post is going to be a relatively short and to the point run through of creating an annotated heatmap for the Dow 30 stock returns using the Python Seaborn package.

Let’s start with what is a heatmap actually is; it’s defined as “a representation of data in the form of a map or diagram in which data values are represented as colours.”

This makes it a great tool to quickly visualise the magnitude of stock returns over time in a matrix/grid format, using a colour map/scale to represent the size and direction of each stock’s percentage change over that period of time.

Creating a heatmap without stock ticker labels annotated, i.e. a heatmap annotated with just the numerical value of the relevant cell is a very easy process, thanks to the power and ease of use of Seaborn.

It can be achieved as follows:

#import relevant modules
import pandas as pd
import numpy as np
import pandas_datareader as data
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
#create a small function that we can feed a list of stock tickers to to download pricing data
def get_prices(tickers,start):
    prices = data.DataReader(tickers,'yahoo',start=start)['Adj Close']
    return prices
#set the url that we will scrape the Dow 30 ticker information from 
dow = 'https://www.cnbc.com/dow-components/'
#read in the url and scrape ticker data
data_table = pd.read_html(dow)
#convert ticker column to list
tickers = data_table[:][0]['Symbol'].tolist()
#download daily pricing data for each ticker in the Dow 30
prices = get_prices(tickers,'01/01/2017')
#Calculate percentage return over download period
returns = (((prices.iloc[-1] / prices.iloc[0]) - 1) * 100).round(2)

The above code gets the data we actually need to populate the heatmap, with the actual heatmap creating being as easy as folows:

fig, ax = plt.subplots(figsize=(14,9))
plt.title('Dow 30 Heat Map',fontsize=18)
ax.title.set_position([0.5,1.05])
ax.set_xticks([])
sns.heatmap(per_change, annot=True, fmt="", cmap='RdYlGn', ax=ax)
plt.show()

This creates the following:

The above is all well and good, but we can’t actually see which cell relates to which individual stock! Not ideal.

The (slightly) tricky part is going to be creating the label arrays to actually annotate the heatmap with the relevant cell’s stock ticker information.

This can be added as follows:

#create a reshaped array of ticker symbols that matches the desired shape of the heatmap
symbol = ((np.asarray(returns.index)).reshape(6,5))
#create a reshaped array of percent returns that matches the desired shape of the heatmap
per_change = ((np.asarray(returns)).reshape(6,5))

#create a new array of the same shape as desired, combining the relevant ticker symbol
#and percentage return data
labels = (np.asarray(["{0} \n {1:.3f}".format(symbol, per_change)
                      for symbol, per_change in zip(symbol.flatten(),
                                               per_change.flatten())])).reshape(6,5)

Create the new heatmap, this time using the “annot” call to use our newly created “labels” list to annotate it.

fig, ax = plt.subplots(figsize=(14,9))
plt.title('Dow 30 Heat Map',fontsize=18)
ax.title.set_position([0.5,1.05])
ax.set_xticks([])
sns.heatmap(per_change, annot=labels, fmt="", cmap='RdYlGn', ax=ax)
plt.show()

We now have the following:

A nicely annotated heatmap showing both the returns and stock ticker relevant to each of the Dow 30 stocks.

I’ll leave this post here, and as always – any questions or comments just leave them below.

Cheers!