In [1]:

    

import seaborn as sns
import datetime
import pandas as pd
import numpy as np
import sweet_charts as sc


    

In [2]:

    

import matplotlib.pyplot as plt
%matplotlib inline


    

In [3]:

    

# run the broad function to get the rolling betas for a list of stock tickers
df = sc.get_chart_data(['BRK-B', 'AMZN', 'GOOG'])


    

    




/home/jpuser/anaconda3/envs/py27/lib/python2.7/site-packages/pandas/core/indexing.py:284: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[key] = _infer_fill_value(value)
/home/jpuser/anaconda3/envs/py27/lib/python2.7/site-packages/pandas/core/indexing.py:545: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[item_labels[indexer[info_axis]]] = value

In [4]:

    

# look at the last few rows to understand how the data is structured
df.tail(15)


    

    
Out[4]:






  

    

      

      
AMZN
      
BRK-B
      
GOOG
    
  
  

    

      
2016-12-16
      
1.206290
      
0.933485
      
1.088248
    
    

      
2016-12-19
      
1.205595
      
0.934477
      
1.087660
    
    

      
2016-12-20
      
1.206183
      
0.933964
      
1.086916
    
    

      
2016-12-21
      
1.206742
      
0.933961
      
1.087208
    
    

      
2016-12-22
      
1.206884
      
0.933880
      
1.087294
    
    

      
2016-12-23
      
1.206145
      
0.933761
      
1.087251
    
    

      
2016-12-27
      
1.206290
      
0.933600
      
1.087186
    
    

      
2016-12-28
      
1.203998
      
0.933506
      
1.086674
    
    

      
2016-12-29
      
1.204083
      
0.933545
      
1.086697
    
    

      
2016-12-30
      
1.206866
      
0.933061
      
1.088009
    
    

      
2017-01-03
      
1.205773
      
0.932187
      
1.089995
    
    

      
2017-01-04
      
1.205148
      
0.932495
      
1.088145
    
    

      
2017-01-05
      
1.204658
      
0.932513
      
1.087981
    
    

      
2017-01-06
      
1.205562
      
0.932347
      
1.088673
    
    

      
2017-01-09
      
1.205650
      
0.932935
      
1.088465
    
  

In [5]:

    

# create a simple plot of the stock rolling betas
df.plot(kind='line', figsize=(12,9))


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f3355602ed0>