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import time


    

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from numpy import cumprod, linspace, random


    

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from bokeh.sampledata.stocks import AAPL, FB, GOOG, IBM, MSFT
from bokeh.plotting import *
from bokeh.models import GridPlot


    

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num_points = 300

now = time.time()
dt = 24*3600 # days in seconds
dates = linspace(now, now + num_points*dt, num_points) * 1000 # times in ms
acme = cumprod(random.lognormal(0.0, 0.04, size=num_points))
choam = cumprod(random.lognormal(0.0, 0.04, size=num_points))


    

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output_notebook()


    

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p1 = figure(x_axis_type = "datetime")

p1.line(dates, acme, color='#1F78B4', legend='ACME')
p1.line(dates, choam, color='#FB9A99', legend='CHOAM')

p1.title = "Stock Returns"
p1.grid.grid_line_alpha=0.3

show(p1)


    

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p2 = figure()

p2.scatter(acme, choam, color='#A6CEE3', legend='close')

p2.title = "ACME / CHOAM Correlations"
p2.grid.grid_line_alpha=0.3

show(p2)


    

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