import pandas as pd
import pandas.io.data as web
import datetime
import seaborn as sns
%pylab inline

# Helper function to get data from yahoo finance API 
# as google does not offer adjusted close prices
oracle_data = web.get_data_yahoo('ORC', '2013-02-15','2014-02-15')
sap_data = web.get_data_yahoo('SAP', '2013-02-15','2014-02-15')

print pd.concat((sap_data.head(1),sap_data.tail(1)))
print pd.concat((oracle_data.head(1),oracle_data.tail(1)))

# Only need time-index and daily-close.
oracle = oracle_data['Adj Close'] 
sap = sap_data['Adj Close']

# Create a dataframe from the two series for ploting
df = pd.concat([oracle,sap],axis=1) 
df.columns =['Oracle','SAP'] 

plt.rcParams['figure.figsize'] = 11, 10
df.plot(subplots = True); plt.legend(loc='upper right')
plt.title("One Year Daily Adjusted Close Price for Oracle and SAP")

# Compute a day-to-day percentage change array (pd.series) with default settings
oracle_pc = oracle.pct_change(periods=1, fill_method='pad', limit=None, freq=None)
sap_pc = sap.pct_change(periods=1, fill_method='pad', limit=None, freq=None)

plt.rcParams['figure.figsize'] = 10, 5
pd.rolling_corr(oracle_pc, sap_pc, window = 50).plot(title = "One Year 50-day Window Correlation of Oracle with SAP")

ols_model = pd.ols(y = oracle_pc, x = {'sap_pc' : sap_pc}, window = 50) # Window attribute makes it dynamic
ols_model.beta.head(2)

ols_model.beta['sap_pc'].plot(title = "One Year OLS beta of Oracle with SAP")

# Reusing our SAP adjusted close data to create a dataframe with a 20 and 50 day simple moving average
SAPdf = pd.DataFrame(sap_data)
SAPdf['SMA20'] = pd.stats.moments.rolling_mean(SAPdf['Adj Close'], 20)
SAPdf['SMA50'] = pd.stats.moments.rolling_mean(SAPdf['Adj Close'], 50)

SAPdf.head(2)

plt.rcParams['figure.figsize'] = 11, 7

main = plt.subplot2grid((4,4), (0, 0), rowspan=3, colspan=4)
main.plot(SAPdf.index, SAPdf['Adj Close'], label='Adj Close')
main.plot(SAPdf.index, SAPdf['SMA20'], label='SMA20')
main.plot(SAPdf.index, SAPdf['SMA50'], label='SMA50')
main.axes.xaxis.set_ticklabels([])

plt.title('One Year SAP Daily Adj Close w/ 20 & 50 day SMA')
plt.legend()

vol = plt.subplot2grid((4,4), (3,0), rowspan=1, colspan=4)
vol.bar(SAPdf.index, SAPdf['Volume'])

plt.title('SAP Daily Volume')

# savefig('img.png', bbox_inches='tight', transparent=True)