stocks.py

A timeseries plot using stock price data. This example demonstrates adding multiple plots to a gridplot, and configuring grid bands on an axis.

Details

Sampledata

bokeh.sampledata.stocks

Bokeh APIs

Figure.line, Figure.scatter, bokeh.layouts.gridplot

More info

Creating layouts > Grid layout for plots

Keywords

bands, gridplot, line, timeseries, stocks

import numpy as np

from bokeh.layouts import gridplot
from bokeh.plotting import figure, show
from bokeh.sampledata.stocks import AAPL, GOOG, IBM, MSFT


def datetime(x):
    return np.array(x, dtype=np.datetime64)

p1 = figure(x_axis_type="datetime", title="Stock Closing Prices")
p1.grid.grid_line_alpha=0.3
p1.xaxis.axis_label = 'Date'
p1.yaxis.axis_label = 'Price'

p1.line(datetime(AAPL['date']), AAPL['adj_close'], color='#A6CEE3', legend_label='AAPL')
p1.line(datetime(GOOG['date']), GOOG['adj_close'], color='#B2DF8A', legend_label='GOOG')
p1.line(datetime(IBM['date']), IBM['adj_close'], color='#33A02C', legend_label='IBM')
p1.line(datetime(MSFT['date']), MSFT['adj_close'], color='#FB9A99', legend_label='MSFT')
p1.legend.location = "top_left"

aapl = np.array(AAPL['adj_close'])
aapl_dates = np.array(AAPL['date'], dtype=np.datetime64)

window_size = 30
window = np.ones(window_size)/float(window_size)
aapl_avg = np.convolve(aapl, window, 'same')

p2 = figure(x_axis_type="datetime", title="AAPL One-Month Average")
p2.grid.grid_line_alpha = 0
p2.xaxis.axis_label = 'Date'
p2.yaxis.axis_label = 'Price'
p2.ygrid.band_fill_color = "olive"
p2.ygrid.band_fill_alpha = 0.1

p2.scatter(aapl_dates, aapl, size=4, legend_label='close',
           color='darkgrey', alpha=0.2)

p2.line(aapl_dates, aapl_avg, legend_label='avg', color='navy')
p2.legend.location = "top_left"

show(gridplot([[p1,p2]], width=400, height=400))  # open a browser