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Python Bokeh – Visualizing Stock Data

  • Last Updated : 03 Jul, 2020

Bokeh is a Python interactive data visualization. It renders its plots using HTML and JavaScript. It targets modern web browsers for presentation providing elegant, concise construction of novel graphics with high-performance interactivity.
Bokeh can be used to visualize stock market data. Visualization is be done using the plotting module. Here we will be using the sample stock datasets given to us by Bokeh.

Downloading the dataset :

To download the sample datasets run the following command on the command line :

bokeh sampledata

Alternatively, we can also execute the following Python code :

import bokeh
bokeh.sampledata.download()

Analyzing the dataset :

In the sample data provided by Bokeh, there are datasets of the stocks of the following companies :

  • AAPL which is Apple
  • FB which is Facebook
  • GOOG which is Google
  • IBM which is International Business Machines
  • MSFT which is Microsoft Corporation

All these datasets are available as CSV files. Below is a glimpse into the IBM.csv file :



Date        Open    High    Low    Close    Volume        Adj Close
01-03-2000    102    105.5    100.06    100.25    10807800    84.48
02-03-2000    100.5    105.44    99.5    103.12    11192900    86.9
03-03-2000    107.25    110    106.06    108    10162800    91.01
06-03-2000    109.94    111    101    103.06    10747400    86.85
07-03-2000    106    107    101.69    103    10035100    86.8

The file contains the stock data between the years 2000 and 2013 with over 3000 entries.

Visualizing the Stocks :

We will be plotting a line graph which will track the closing price of the stocks between the years 2000 and 2013 of all the 5 available companies.

  1. Import the required modules :
    • numpy
    • figure, output_file and show from bokeh.plotting
    • AAPL, FB, GOOG, IBM and MSFT from bokeh.sampledata.stocks
  2. Instantiate a figure object with the title and axis types.
  3. Give the names to x-axis and y-axis.
  4. Plot line graphs for all the 5 companies.
  5. Display the model.




# importing the modules
import numpy as np
from bokeh.plotting import figure, output_file, show
from bokeh.sampledata.stocks import AAPL, FB, GOOG, IBM, MSFT
   
# the file to save the model
output_file("gfg.html")
   
# instantiating the figure object
graph = figure(x_axis_type = "datetime", title = "Stock Closing Prices")
   
# name of the x-axis
graph.xaxis.axis_label = 'Date'
   
# name of the y-axis
graph.yaxis.axis_label = 'Price (in USD)'
   
# plotting the line graph for AAPL
x_axis_coordinates = np.array(AAPL['date'], dtype = np.datetime64)
y_axis_coordinates = AAPL['adj_close']
color = "lightblue"
legend_label = 'AAPL'
graph.line(x_axis_coordinates,
        y_axis_coordinates,
        color = color,
        legend_label = legend_label)
   
# plotting the line graph for FB
x_axis_coordinates = np.array(FB['date'], dtype = np.datetime64)
y_axis_coordinates = FB['adj_close']
color = "black"
legend_label = 'FB'
graph.line(x_axis_coordinates,
        y_axis_coordinates,
        color = color,
        legend_label = legend_label)
   
# plotting the line graph for GOOG
x_axis_coordinates = np.array(GOOG['date'], dtype = np.datetime64)
y_axis_coordinates = GOOG['adj_close']
color = "orange"
legend_label = 'GOOG'
graph.line(x_axis_coordinates,
        y_axis_coordinates,
        color = color,
        legend_label = legend_label)
   
# plotting the line graph for IBM
x_axis_coordinates = np.array(IBM['date'], dtype = np.datetime64)
y_axis_coordinates = IBM['adj_close']
color = "darkblue"
legend_label = 'IBM'
graph.line(x_axis_coordinates,
        y_axis_coordinates,
        color = color,
        legend_label = legend_label)
   
# plotting the line graph for MSFT
x_axis_coordinates = np.array(MSFT['date'], dtype = np.datetime64)
y_axis_coordinates = MSFT['adj_close']
color = "yellow"
legend_label = 'MSFT'
graph.line(x_axis_coordinates,
        y_axis_coordinates,
        color = color,
        legend_label = legend_label)
   
# relocating the legend table to 
# avoid abstruction of the graph
graph.legend.location = "top_left"
   
# displaying the model
show(graph)

Output :

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