In [1]:

    

from datetime import datetime
import pandas as pd
import pandas.io.data
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
pd.set_option('max_columns', 50)
%matplotlib inline


    

In [2]:

    

run_console = False
if run_console:
    %qtconsole


    

In [3]:

    

update_from_web = False
if update_from_web:
    today = datetime.now()
    # http://research.stlouisfed.org/fred2/series/DJIA/downloaddata
    # http://measuringworth.com/DJA/
    # http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RWTC&f=D
    # http://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm


    

In [4]:

    

# From http://measuringworth.com/DJA/
dow = pd.read_csv('DJA.csv', skiprows=3, index_col='Date', parse_dates=True, usecols=['Date','DJIA'])
# From  http://research.stlouisfed.org/fred2/series/DJIA/downloaddata
# dow1 = pd.read_excel('DJIA.xls', 'DJIA', skiprows=23, index_col='DATE', parse_dates=True, usecols=['DATE','VALUE'])


    

In [5]:

    

# dow['5/4/1970':].plot(figsize=(10, 10))


    

In [6]:

    

dow[np.isnan(dow['DJIA'])]


    

    
Out[6]:






  

    

      

      
DJIA
    
    

      
Date
      

    
  
  

    

      
NaN
      
NaN
    
    

      
Citation: Samuel H. Williamson, 'Daily Closing Value of the Dow Jones Average, 1885 to Present,' MeasuringWorth, 2012. 
      
NaN
    
  

2 rows × 1 columns

In [7]:

    

dow.dropna(inplace=True)


    

In [8]:

    

dow.tail()


    

    
Out[8]:






  

    

      

      
DJIA
    
    

      
Date
      

    
  
  

    

      
2/28/2014
      
 16321.71
    
    

      
3/3/2014
      
 16168.03
    
    

      
3/4/2014
      
 16395.88
    
    

      
3/5/2014
      
 16360.18
    
    

      
3/6/2014
      
 16421.89
    
  

5 rows × 1 columns

In [9]:

    

wti = pd.read_csv('WTI.csv', skiprows=6, index_col='Day', parse_dates=True, usecols=['Day','Cushing OK WTI Spot Price FOB $/bbl'])
# From http://www.eia.gov/dnav/pet/pet_pri_spt_s1_d.htm
# wti1 = pd.read_excel('PET_PRI_SPT_S1_D.xls', 'Data 1', skiprows=2, index_col='Date', parse_dates=True, usecols=['Date','Cushing, OK WTI Spot Price FOB (Dollars per Barrel)'])


    

In [10]:

    

wti.columns = ['WTI']
wti.index.names = ['Date']


    

In [11]:

    

df = pd.DataFrame()
df = df.combineAdd(dow)
df = df.combineAdd(wti)


    

In [12]:

    

df.info()


    

    




<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 35449 entries, 1885-05-04 00:00:00 to 2014-03-06 00:00:00
Data columns (total 2 columns):
DJIA    35410 non-null float64
WTI     7100 non-null float64
dtypes: float64(2)

In [13]:

    

df.plot(subplots=True, figsize=(10, 10), sharex=True)


    

    
Out[13]:





array([<matplotlib.axes.AxesSubplot object at 0x093B19B0>,
       <matplotlib.axes.AxesSubplot object at 0x0957FE30>], dtype=object)






    

In [14]:

    

df['19870102':].plot(subplots=True, figsize=(10, 10), sharex=True)


    

    
Out[14]:





array([<matplotlib.axes.AxesSubplot object at 0x093B1EB0>,
       <matplotlib.axes.AxesSubplot object at 0x096D52D0>], dtype=object)






    

In [15]:

    

# df.diff().plot(xlim=datetime(2006,01,02), figsize=(10, 10))


    

In [16]:

    

# df.pct_change().plot(subplots=True, xlim=datetime(2006,01,02), figsize=(10, 10), sharex=True)


    

In [17]:

    

# df.pct_change().plot(subplots=True, figsize=(10, 10), xlim=datetime(1986,01,02), sharex=True)


    

In [18]:

    

# pd.rolling_max(df,100).plot(subplots=True, figsize=(10, 10), xlim=datetime(1999,01,02), sharex=True)


    

In [19]:

    

# pd.rolling_max(df['WTI'],100).plot(figsize=(10, 10), xlim=datetime(1999,01,02))


    

In [20]:

    

# pd.rolling_min(df['DJIA'],100).plot(figsize=(10, 10), xlim=datetime(1999,01,02))


    

In [21]:

    

df['DOW_WTI30'] = df.DJIA[df.WTI>30]
df['DOW_WTI70'] = df.DJIA[df.WTI>70]
df['DOW_WTI100'] = df.DJIA[df.WTI>100]


    

In [22]:

    

df['1987':][df.columns.drop('WTI')].plot(figsize=(10, 10))


    

    
Out[22]:





<matplotlib.axes.AxesSubplot at 0xab35670>






    

In [23]:

    

df['2007':][['DJIA','DOW_WTI100']].plot(color=('b','r'), figsize=(10, 10))


    

    
Out[23]:





<matplotlib.axes.AxesSubplot at 0xad92670>






    

In [30]:

    

df1 = df.drop(['DOW_WTI30', 'DOW_WTI70', 'DOW_WTI100'],axis=1)


    

In [36]:

    

plt.figure()
df1['1995':].plot(secondary_y=['WTI'], figsize=(10, 10))


    

    
Out[36]:





<matplotlib.axes.AxesSubplot at 0xce15490>






    






<matplotlib.figure.Figure at 0xce0be50>






    

In [34]: