In [24]:

    

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import pandas_datareader.data as web
from datetime import datetime
%matplotlib inline


    

In [2]:

    

# set dates
end = datetime.now()
start = datetime(end.year-2, end.month, end.day)


    

In [19]:

    

FCT = web.DataReader("J69U.SI", 'yahoo', start, end)
CMT = web.DataReader("C38U.SI", 'yahoo', start, end)
SGX = web.DataReader("S68.SI", 'yahoo', start, end)
SLA = web.DataReader("5CP.SI", 'yahoo', start, end)
MIT = web.DataReader("ME8U.SI", 'yahoo', start, end)
SING = web.DataReader("Z74.SI", 'yahoo', start, end)
FIRST = web.DataReader("AW9U.SI", 'yahoo', start, end)


# create new dataframe with just closing price for each stock
df = pd.DataFrame({'FCT': FCT['Adj Close'], 'CMT': CMT['Adj Close'],
                   'SGX': SGX['Adj Close'], 'SLA': SLA['Adj Close'],
                   'MIT': MIT['Adj Close'], 'SING': SING['Adj Close'],
                   'FIRST': FIRST['Adj Close']})
df.head(2)


    

    
Out[19]:






  

    

      

      
CMT
      
FCT
      
FIRST
      
MIT
      
SGX
      
SING
      
SLA
    
    

      
Date
      

      

      

      

      

      

      

    
  
  

    

      
2015-02-16
      
1.885
      
1.801
      
1.18166
      
1.33454
      
7.581
      
3.99
      
0.98637
    
    

      
2015-02-17
      
1.876
      
1.810
      
1.16853
      
1.33882
      
7.599
      
3.96
      
0.96733
    
  

In [20]:

    

df.plot(figsize=(10,4))
plt.ylabel('Price')


    

    
Out[20]:





<matplotlib.text.Text at 0x11d20b150>






    

In [21]:

    

returnfstart = df.apply(lambda x: x / x[0])
returnfstart.plot(figsize=(10,4)).axhline(1, lw=1, color='black')
plt.ylabel('Return From Start Price')


    

    
Out[21]:





<matplotlib.text.Text at 0x11c76a210>






    

In [22]:

    

df2=df.pct_change()
df2.head(3)


    

    
Out[22]:






  

    

      

      
CMT
      
FCT
      
FIRST
      
MIT
      
SGX
      
SING
      
SLA
    
    

      
Date
      

      

      

      

      

      

      

    
  
  

    

      
2015-02-16
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
      
NaN
    
    

      
2015-02-17
      
-0.004775
      
0.004997
      
-0.011111
      
0.003207
      
0.002374
      
-0.007519
      
-0.019303
    
    

      
2015-02-18
      
0.028785
      
-0.009945
      
-0.003748
      
0.006386
      
0.003685
      
0.007576
      
0.015744
    
  

In [7]:

    

df2.plot(figsize=(10,4))
plt.axhline(0, color='black', lw=1)
plt.ylabel('Daily Percentage Return')


    

    
Out[7]:





<matplotlib.text.Text at 0x11b6c4f50>






    

In [23]:

    

import numpy as np

# set size of circles
area = np.pi*50

# note mean represents average returns, stdev represents volatility and risk
plt.scatter(df2.mean(), df2.std(), alpha = 0.5, s =area)

# see this link http://matplotlib.org/users/annotations_guide.html#plotting-guide-annotation
for label, x, y in zip(df2.columns, df2.mean(), df2.std()):
    plt.annotate(label, 
                 xy = (x, y), 
                 xytext = (50, 50),
                 textcoords = 'offset points', 
                 ha = 'center', 
                 va = 'bottom',
                 arrowprops = dict(arrowstyle='-', connectionstyle='angle, angleA=90, angleB=180, rad=5'))

plt.ylabel('Risk / Stdev / Volatility')
plt.xlabel('Avg Return / Mean of Daily Pct Change')
# set axes limits
plt.ylim([0, 0.055])
plt.xlim([-0.0005, 0.001])


    

    
Out[23]:





(-0.0005, 0.001)






    

In [119]:

    

sns.jointplot('MIT', 'SLA', df, kind='scatter', color='seagreen')


    

    
Out[119]:





<seaborn.axisgrid.JointGrid at 0x15a15c490>






    

In [120]:

    

plt.figure(figsize=(8,8))
sns.corrplot(df.dropna())


    

    
Out[120]:





<matplotlib.axes._subplots.AxesSubplot at 0x15a7eaf90>






    

In [124]:

    

sns.pairplot(df.dropna())


    

    
Out[124]:





<seaborn.axisgrid.PairGrid at 0x15f8f9a10>






    

In [99]:

    

plt.figure(figsize=(10,10))
# use pairgrid for full control of each plot
fig = sns.PairGrid(df.dropna())

# define top, bottom and diagonal plots
fig.map_upper(plt.scatter, color='purple')
fig.map_lower(sns.kdeplot, cmap='cool_d')
fig.map_diag(sns.distplot, bins=30)


    

    
Out[99]:





<seaborn.axisgrid.PairGrid at 0x15104ccd0>






    






<matplotlib.figure.Figure at 0x15104ce10>