Most examples work across multiple plotting backends, this example is also available for:

Bokeh - stocks example



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import numpy as np
import pandas as pd
import holoviews as hv
from holoviews import opts

hv.extension('matplotlib')
hv.output(fig='svg', dpi=120)

Defining the data



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from holoviews.operation.timeseries import rolling
from bokeh.sampledata.stocks import AAPL, GOOG, IBM, MSFT


def get_curve(data, label=''):
    df = pd.DataFrame(data)
    df['date'] = df.date.astype('datetime64[ns]')
    return hv.Curve(df, ('date', 'Date'), ('adj_close', 'Price'), label=label)

hv.Dimension.type_formatters[np.datetime64] = '%Y'

aapl = get_curve(AAPL, label='AAPL')
goog = get_curve(GOOG, label='GOOG')
ibm  = get_curve(IBM, label='IBM')
msft = get_curve(MSFT, label='MSFT')

avg_curve = rolling(aapl, rolling_window=30).relabel('Average')
avg_scatter = hv.Scatter((np.array(AAPL['date'], dtype=np.datetime64), np.array(AAPL['adj_close'])), 
                         ('date', 'Date'), ('adj_close', 'Price'), label='close')

Plot



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color_cycle = hv.Cycle(values=['#A6CEE3', '#B2DF8A','#33A02C', '#FB9A99'])
plot_opts = opts.Overlay(aspect=1, fig_size=200, legend_position='top_left')
curve_opts = opts.Curve(color=color_cycle)

stocks = (aapl * goog * ibm * msft).opts(plot_opts, curve_opts)

appl_stats = (avg_scatter * avg_curve).opts(
    opts.Scatter(alpha=0.2, s=4, color='darkgrey'),
    opts.Curve(color='navy'), plot_opts)

stocks + appl_stats