In [6]:

    

import numpy as np
import pandas as pd
from pandas import Series, DataFrame
from pandas.io.data import DataReader

import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')

%matplotlib inline


    

In [8]:

    

from datetime import datetime
from __future__ import division


    

In [9]:

    

tech_list = ['AAPL', 'GOOG', 'MSFT', 'AMZN']


    

In [10]:

    

end = datetime.now()

start = datetime(end.year - 1, end.month, end.day)


    

In [15]:

    

stocks = {}
for stock in tech_list:
    stocks[stock] = DataReader(stock, 'yahoo', start, end)


    

In [17]:

    

stocks['AAPL'].describe()


    

    
Out[17]:






  

    

      

      
Open
      
High
      
Low
      
Close
      
Volume
      
Adj Close
    
  
  

    

      
count
      
253.000000
      
253.000000
      
253.000000
      
253.000000
      
2.530000e+02
      
253.000000
    
    

      
mean
      
105.885020
      
106.778933
      
105.175810
      
106.047075
      
3.556875e+07
      
105.189008
    
    

      
std
      
8.251499
      
8.227360
      
8.335292
      
8.305925
      
1.455107e+07
      
8.711312
    
    

      
min
      
90.000000
      
91.669998
      
89.470001
      
90.339996
      
1.142440e+07
      
89.394274
    
    

      
25%
      
97.610001
      
98.480003
      
96.919998
      
97.720001
      
2.640410e+07
      
96.505269
    
    

      
50%
      
107.120003
      
107.879997
      
106.180000
      
107.129997
      
3.193190e+07
      
106.274363
    
    

      
75%
      
112.709999
      
113.389999
      
111.800003
      
112.709999
      
4.019160e+07
      
112.134277
    
    

      
max
      
122.139999
      
122.440002
      
121.599998
      
121.949997
      
1.146021e+08
      
121.949997
    
  

In [20]:

    

stocks['AAPL']['Adj Close'].plot(legend=True, figsize=(10,4))


    

    
Out[20]:





<matplotlib.axes._subplots.AxesSubplot at 0x11ca33910>






    

In [21]:

    

stocks['AAPL']['Volume'].plot(legend=True, figsize=(10,4))


    

    
Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x11ccb1590>






    

In [22]:

    

ma_day = [10,20,50]


    

In [23]:

    

AAPL = stocks['AAPL']
for ma in ma_day:
    column_name = "MA for %s days" %(str(ma))
    
    AAPL[column_name] = pd.rolling_mean(AAPL['Adj Close'], ma)


    

    




/Users/andymeyers/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=10,center=False).mean()
/Users/andymeyers/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=20,center=False).mean()
/Users/andymeyers/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with 
	Series.rolling(window=50,center=False).mean()

In [24]:

    

AAPL[['Adj Close', 'MA for 10 days', 'MA for 20 days', 'MA for 50 days']].plot(subplots=False, figsize=(10,4))


    

    
Out[24]:





<matplotlib.axes._subplots.AxesSubplot at 0x113fbb790>






    

In [25]:

    

AAPL['Daily Return'] = AAPL['Adj Close'].pct_change()

AAPL['Daily Return'].plot(figsize=(10,4), legend=True, linestyle='--',marker='o')


    

    
Out[25]:





<matplotlib.axes._subplots.AxesSubplot at 0x11cf332d0>






    

In [26]:

    

sns.distplot(AAPL['Daily Return'].dropna(), bins=100)


    

    
Out[26]:





<matplotlib.axes._subplots.AxesSubplot at 0x11d335550>






    

In [28]:

    

closing_df = DataReader(tech_list, 'yahoo', start, end)['Adj Close']


    

In [29]:

    

tech_rets = closing_df.pct_change()


    

In [31]:

    

sns.jointplot('GOOG', 'GOOG', tech_rets, kind='scatter')


    

    
Out[31]:





<seaborn.axisgrid.JointGrid at 0x11d8ad590>






    

In [32]:

    

sns.jointplot('GOOG', 'MSFT', tech_rets, kind='scatter')


    

    
Out[32]:





<seaborn.axisgrid.JointGrid at 0x11dcd8d90>






    

In [34]:

    

sns.jointplot('GOOG', 'AAPL', tech_rets, kind='scatter')


    

    
Out[34]:





<seaborn.axisgrid.JointGrid at 0x11e3086d0>






    

In [35]:

    

sns.jointplot('MSFT', 'AAPL', tech_rets, kind='scatter')


    

    
Out[35]:





<seaborn.axisgrid.JointGrid at 0x11ef29f50>






    

In [36]:

    

sns.pairplot(tech_rets.dropna())


    

    
Out[36]:





<seaborn.axisgrid.PairGrid at 0x11f5e9910>






    

In [37]:

    

returns_fig = sns.PairGrid(tech_rets.dropna())

returns_fig.map_upper(plt.scatter)
returns_fig.map_lower(sns.kdeplot)
returns_fig.map_diag(plt.hist, bins=30)


    

    
Out[37]:





<seaborn.axisgrid.PairGrid at 0x1217ba410>






    

In [38]:

    

returns_fig = sns.PairGrid(closing_df)

returns_fig.map_upper(plt.scatter)
returns_fig.map_lower(sns.kdeplot)
returns_fig.map_diag(plt.hist, bins=30)


    

    
Out[38]:





<seaborn.axisgrid.PairGrid at 0x125251410>






    

In [41]:

    

sns.heatmap(tech_rets.dropna().corr(), annot=True)


    

    
Out[41]:





<matplotlib.axes._subplots.AxesSubplot at 0x129b5e4d0>






    

In [42]:

    

sns.heatmap(closing_df.dropna().corr(), annot=True)


    

    
Out[42]:





<matplotlib.axes._subplots.AxesSubplot at 0x11e777850>






    

In [ ]: