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')

%%opts Curve {+framewise}

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

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

import param
import parambokeh

class StockExplorer(Stream):
    
    rolling_window = param.Integer(default=10, bounds=(1, 365))
    
    symbol = param.ObjectSelector(default='AAPL', objects=stock_symbols)
    
    def view(self):
        stocks = hv.DynamicMap(load_symbol, kdims=[], streams=[self])

        # Apply rolling mean
        smoothed = rolling(stocks, streams=[self])

        # Find outliers
        outliers = rolling_outlier_std(stocks, streams=[self])
        return smoothed * outliers

%opts Curve [width=600] {+framewise} Scatter (color='red' marker='triangle')
explorer = StockExplorer()
parambokeh.Widgets(explorer, continuous_update=True, callback=explorer.event, on_init=True)
explorer.view()

def render(obj, view):
    renderer = hv.renderer('bokeh')
    return renderer.get_plot(obj).state

class AdvancedStockExplorer(StockExplorer):
    
    output = parambokeh.view.Plot(renderer=render)
    
    variable = param.ObjectSelector(default='adj_close', objects=[c for c in stocks.AAPL.keys() if c!= 'date'])

    def event(self, **kwargs):
        if self.output is None or 'variable' in kwargs:
            self.output = self.view()
        else:
            super(AdvancedStockExplorer, self).event(**kwargs)

explorer = AdvancedStockExplorer()
parambokeh.Widgets(explorer, continuous_update=True, callback=explorer.event, on_init=True, view_position='right')