import pandas as pd
import holoviews as hv
from bokeh.sampledata import stocks
from holoviews.operation.timeseries import rolling, rolling_outlier_std
from holoviews.streams import Stream
hv.notebook_extension('bokeh')

def load_symbol(symbol, **kwargs):
    df = pd.DataFrame(getattr(stocks, symbol))
    df['date'] = df.date.astype('datetime64[ns]')
    return hv.Curve(df, ('date', 'Date'), ('adj_close', 'Adjusted Close'))

stock_symbols = ['AAPL', 'FB', 'GOOG', 'IBM', 'MSFT']
dmap = hv.DynamicMap(load_symbol, kdims='Symbol').redim.values(Symbol=stock_symbols)

%%opts Curve [width=600] {+framewise}
dmap

smoothed = rolling(dmap, rolling_window=30)
smoothed

rolling_stream = Stream.define('rolling', rolling_window=5)
stream = rolling_stream()

%%opts Curve [width=600] {+framewise}

def rolled_data(symbol, rolling_window, **kwargs):
    curve = load_symbol(symbol)
    return rolling(curve, rolling_window=rolling_window)
    
rolled_dmap = hv.DynamicMap(rolled_data, kdims='Symbol',
                            streams=[stream]).redim.values(Symbol=stock_symbols)

rolled_dmap

stream.event(rolling_window=50)

%%opts Scatter (color='red' marker='triangle')
stream = rolling_stream()

smoothed = rolling(dmap, streams=[stream])
outliers = rolling_outlier_std(dmap, streams=[stream])

smoothed * outliers

stream.event(rolling_window=50)