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%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
matplotlib.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (16, 12)


    

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# FTSE100指数
stock = pd.read_csv('FTSE100_data.txt', sep='\s+', index_col=0)
stock.index = stock.index.astype('datetime64[ns]')
s1 = stock['Close']
gp1 = stock['Close'].resample('MS').mean()
std1 = gp1.std()
miu1 = gp1.mean()
gp11 = (gp1-miu1) /std1
gp11.tail()


    

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# N225指数
stock = pd.read_csv('N225_data.txt', sep='\s+', index_col=0)
stock.index = stock.index.astype('datetime64[ns]')
s2 = stock['Close']
gp2 = stock['Close'].resample('MS').mean()
std2 = gp2.std()
miu2 = gp2.mean()
gp22 = (gp2-miu2) /std2
gp22.tail()


    

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# NASDAQ指数
stock = pd.read_csv('NASDAQ_data.txt', sep='\s+', index_col=0)
stock.index = stock.index.astype('datetime64[ns]')
s3 = stock['Close']
gp3 = stock['Close'].resample('MS').mean()
std3 = gp3.std()
miu3 = gp3.mean()
gp33 = (gp3-miu3) /std3
gp33.tail()


    

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df = pd.concat([s1, s2, s3], axis=1, join='inner')
df.columns = ["FTSE100", "N225", "NASDAQ"]
df.plot()


    

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df = pd.concat([gp1, gp2, gp3], axis=1, join='inner')
df.columns = ["FTSE100", "N225", "NASDAQ"]
df.plot()


    

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df = pd.concat([gp11, gp22, gp33], axis=1, join='inner')
df.columns = ["FTSE100", "N225", "NASDAQ"]
df.plot()