In [2]:

    

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 [179]:

    

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


    

In [160]:

    

CMT = web.DataReader("C38U.SI", 'yahoo', start, end)
CMT.head(2)


    

    
Out[160]:






  

    

      

      
Open
      
High
      
Low
      
Close
      
Volume
      
Adj Close
    
    

      
Date
      

      

      

      

      

      

    
  
  

    

      
2015-02-12
      
2.15
      
2.16
      
2.10
      
2.11
      
9178700
      
1.894
    
    

      
2015-02-13
      
2.10
      
2.12
      
2.08
      
2.11
      
6409100
      
1.894
    
  

In [180]:

    

CMT['Adj Close'].plot(legend=True, figsize=(10, 5), \
                      title='CapitaMall Trust', \
                      label='Adjusted Closing Price')


    

    
Out[180]:





<matplotlib.axes._subplots.AxesSubplot at 0x14ae50c10>






    

In [182]:

    

plt.figure(figsize=(10,5))
top = plt.subplot2grid((4,4), (0, 0), rowspan=3, colspan=4)
bottom = plt.subplot2grid((4,4), (3,0), rowspan=1, colspan=4)
top.plot(CMT.index, CMT['Adj Close'])
bottom.bar(CMT.index, CMT['Volume'])

# set the labels
top.axes.get_xaxis().set_visible(False)
top.set_title('CapitalMall Trust')
top.set_ylabel('Adj Closing Price')
bottom.set_ylabel('Volume')


    

    
Out[182]:





<matplotlib.text.Text at 0x14cf5e250>






    

In [165]:

    

# plot a histogram with closing price
plt.figure(figsize=(10,5))
sns.distplot(CMT['Adj Close'].dropna(), bins=20, color='purple')


    

    
Out[165]:





<matplotlib.axes._subplots.AxesSubplot at 0x14856ab90>






    

In [231]:

    

# plot a boxplot to see the interquantile range.
q25 = round(CMT['Adj Close'].quantile(0.25),2)
q75 = round(CMT['Adj Close'].quantile(0.75),2)

sns.boxplot(CMT['Adj Close'].dropna(), orient='v')
plt.text(0.04,2.1,'Interquantile Range: {}-{}'.format(q25,q75))


    

    
Out[231]:





<matplotlib.text.Text at 0x14c1db590>






    

In [152]:

    

# simple moving averages
sma10 = CMT['Close'].rolling(10).mean() #10 days
sma20 = CMT['Close'].rolling(20).mean() #20 days
sma50 = CMT['Close'].rolling(50).mean() #50 days

CMTsma = pd.DataFrame({'CMT': CMT['Close'], 'SMA 10': sma10, 'SMA 20': sma20, 'SMA 50': sma50})
CMTsma.plot(figsize=(10, 5), legend=True, title='CapitaMall Trust')

# add vertical lines with annotations
plt.axvline(x = '2015-11-3', color = "black", lw=0.5)
plt.text('2015-10-23', 2.25, 'SMA 10-20',rotation=90)
plt.axvline(x = '2015-11-20', color = "black", lw=0.5)
plt.text('2015-11-24', 2.25, 'SMA 10-50',rotation=90)


    

    
Out[152]:





<matplotlib.text.Text at 0x1474cd050>