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def nearest(dates, dateRef):
    # Get the previous days before the reference date (dateRef)
    prevDate = dates[dates < dateRef]
    return prevDate[-1] # return the last date
    
def getReturn(period, number, ticker, dt, val):
    # Subset the dataset by stock index
    df = msi.loc[msi.ticker == ticker].reset_index()
    # Get all business days
    existingDates = df['Date'].unique()

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lastDate = msi.loc[msi.Date == '2020-09-30'].reset_index().drop(['index'],axis=1)

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fig, axes = plt.subplots(3,2, figsize=(12, 8),sharex=True)
pagoda = ["#965757", "#D67469", "#4E5A44", "#A1B482", '#EFE482', "#99BFCF"] # for coloring
for i, k in enumerate(region_idx.keys()):
# Iterate for each region
    ax = axes[int(i/2), int(i%2)]
    for j,t in enumerate(region_idx[k]):
        # Iterate and plot for each stock index in this region
        ax.plot(chBegin.index, chBegin[t], marker='', linewidth=1, color = pagoda[j])
        ax.legend([ticker[t] for t in region_idx[k]], loc='upper left', fontsize=7)
        ax.set_title(k, fontweight='bold')

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# Transform the data to be ticker column-wise
chBegin = msi.groupby(['Date', 'ticker'])['chBegin'].first().unstack()
# Fill null values with the values on the row before
chBegin = chBegin.fillna(method='bfill')

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# Get the data for 4 Jan 2010
begRef = msi.loc[msi.Date == '2010-01-04']
def retBegin(ticker, val):
    start_val = begRef.loc[begRef.ticker == ticker, 'Close'].values[0]
    return (val/start_val - 1) * 100
    
msi['chBegin'] = msi.apply(lambda x: retBegin(x.ticker, x.Close), axis = 1)

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region_idx= 
{ 'US & Canada' : ['^GSPC', '^DJI', '^IXIC', '^RUT','^GSPTSE'],
  'Latin America' : ['^BVSP', '^MXX', '^IPSA'],
  'East Asia' : ['^N225', '^HSI', '000001.SS', '399001.SZ', '^TWII', '^KS11'],
  'ASEAN & Oceania' : ['^STI', '^JKSE', '^KLSE','^AXJO',  '^NZ50'],
  'South & West Asia' : ['^BSESN', '^TA125.TA'],
  'Europe' : ['^FTSE', '^GDAXI', '^FCHI', '^STOXX50E','^N100', '^BFX']
}

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stock_list = []
for s in majorStockIdx.Symbol: # iterate for every stock indices
    # Retrieve data from Yahoo! Finance
    tickerData = yf.Ticker(s)
    tickerDf1 = tickerData.history(period='1d', start='2010-1-1', end='2020-9-30')
    # Save historical data 
    tickerDf1['ticker'] = s # don't forget to specify the index
    stock_list.append(tickerDf1)
# Concatenate all data
msi = pd.concat(stock_list, axis = 0)

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# Retrieving List of World Major Stock Indices from Yahoo! Finance
df_list = pd.read_html('https://finance.yahoo.com/world-indices/')
majorStockIdx = df_list[0]
majorStockIdx.head()

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!pip install yfinance # install yfinance
# importing needed libraries/modules
import numpy as np 
import pandas as pd 
import yfinance as yf