import pandas as pd
import holoviews as hv

from holoviews import opts
from bokeh.sampledata import stocks
from holoviews.operation.timeseries import rolling, rolling_outlier_std

hv.extension('bokeh')

opts.defaults(opts.Curve(width=600, framewise=True))

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)

dmap

smoothed = rolling(dmap, rolling_window=30)
smoothed

import panel as pn

slider = pn.widgets.IntSlider(name='rolling_window', start=1, end=100, value=50)

rolled_dmap = dmap.apply.transform(adj_close=hv.dim('adj_close').df.rolling(slider).mean())

rolled_dmap

slider

outliers = dmap.apply(rolling_outlier_std, rolling_window=slider.param.value)

rolled_dmap * outliers.opts(color='red', marker='triangle')

def residual_fn(overlay):
    # Get first and second Element in overlay
    el1, el2 = overlay.get(0), overlay.get(1)

    # Get x-values and y-values of curves
    xvals  = el1.dimension_values(0)
    yvals  = el1.dimension_values(1)
    yvals2 = el2.dimension_values(1)

    # Return new Element with subtracted y-values
    # and new label
    return el1.clone((xvals, yvals-yvals2),
                     vdims='Residual')

residual = (dmap * rolled_dmap).apply(residual_fn)

residual