Python：米主要12社の相対収益率をPlotlyでグラフ化する

import numpy as np
import pandas as pd
import plotly.graph_objs as go
from plotly.offline import plot,iplot
import datetime
import matplotlib.pyplot as plt
%matplotlib inline

from pandas_datareader import data as pdr
import fix_yahoo_finance as yf

tickers = (['AAPL','JNJ','MCD','NFLX','WMT','FB','TWTR','AMZN','GOOGL',
            'DIS','INTC','AMD','MSFT'])

stocks_start = datetime.datetime(2010, 1, 1)
stocks_end = datetime.datetime(2019, 7, 18)

def get(tickers, startdate, enddate):
    def data(ticker):
        return (pdr.get_data_yahoo(ticker, start=startdate, end=enddate))
    datas = map(data, tickers)
    return(pd.concat(datas, keys=tickers, names=['Ticker', 'Date']))
               
all_data = get(tickers, stocks_start, stocks_end)

all_data.tail()

close = all_data[['Close']].reset_index()
close.head()

start_of_year = datetime.datetime(2017, 1, 3)
close_start = close[close['Date']>=start_of_year]
close_start.tail()

chart_data_eval = close_start
chart_data_eval.reset_index(inplace=True)
chart_data_eval.head()

chart_data_eval_pivot = pd.pivot_table(chart_data_eval, index='Date', columns='Ticker', values = 'Close')
chart_data_eval_pivot.reset_index(inplace=True)
chart_data_eval_pivot.head()

chart_data_eval_pivot_relative = pd.pivot_table(chart_data_eval, index='Date', columns='Ticker', values = 'Close')
chart_data_eval_pivot_relative.tail()

chart_data_eval_pivot_relative_first = chart_data_eval_pivot_relative.iloc[0,:]
chart_data_eval_pivot_relative_first.head()

chart_data_eval_pivot_relative = (chart_data_eval_pivot_relative.divide(chart_data_eval_pivot_relative_first, axis=1))-1
chart_data_eval_pivot_relative.reset_index(inplace=True)
chart_data_eval_pivot_relative.head()

chart_data_eval_pivot_relative.tail()

trace1 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['FB'],
    mode = 'lines',
    name = 'FB Prices')
trace2 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['AAPL'],
    mode = 'lines',
    name = 'AAPL Returns')
trace3 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['NFLX'],
    mode = 'lines',
    name = 'NFLX Returns')
trace4 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['WMT'],
    mode = 'lines',
    name = 'WMT Returns')
trace5 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['TWTR'],
    mode = 'lines',
    name = 'TWTR Returns')
trace6 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['JNJ'],
    mode = 'lines',
    name = 'JNJ Returns')
trace7 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['AMZN'],
    mode = 'lines',
    name = 'AMZN Returns')
trace8 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['GOOGL'],
    mode = 'lines',
    name = 'GOOGL Returns')
trace9 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['DIS'],
    mode = 'lines',
    name = 'DIS Returns')
trace10 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['INTC'],
    mode = 'lines',
    name = 'INTC Returns')
trace11 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['AMD'],
    mode = 'lines',
    name = 'AMD Returns')
trace12 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['MSFT'],
    mode = 'lines',
    name = 'MSFT Returns')
trace12 = go.Scatter(
    x = chart_data_eval_pivot_relative['Date'],
    y = chart_data_eval_pivot_relative['MCD'],
    mode = 'lines',
    name = 'MCD Returns')

data = [trace1,trace2,trace3,trace4,trace5,trace6,trace7,trace8, 
        trace9,trace10,trace11,trace12]

layout = dict(autosize=False,width=1050, height=1100
    , title = 'Share Price Returns by Ticker'
    , title_font=dict(size=24, family='Courier', color='black')
    , barmode = 'group'
    , hovermode= 'x'
    , yaxis=dict(title='Relative Returns',tickformat=".1%",title_font=dict(size=22)
                ,tickfont=dict(size=20))
    , xaxis=dict(title='Ticker',title_font=dict(size=22),tickfont=dict(size=20))
    , legend=dict(x=-.001,y=1,font=dict(size=21,color='white'),bgcolor='black')
    , legend_orientation="v"
    , hoverlabel=dict(font=dict(size=31))
    , plot_bgcolor='black'
    )
fig = go.Figure(data=data, layout=layout)
plot(fig,show_link=False,filename="a.html",include_plotlyjs=False)