import pandas as pd
import numpy as np
import statsmodels.formula.api as sm
import statsmodels

#from yahoo_finance import Share
#import scipy
#import statsmodels.formula.api as sm





#import datetime
#from datetime import date

#today = str(date.today())
#month = 12
#start_date = str((date.today() - datetime.timedelta(month * 365 / 12)).isoformat())

#sym_1 = Share("aapl")
#sym_2 = Share("msft")
#sym_3 = Share("cvx")

#data_1 = pd.DataFrame(sym_1.get_historical(start_date, today))
#data_2 = pd.DataFrame(sym_2.get_historical(start_date, today))
#data_3 = pd.DataFrame(sym_3.get_historical(start_date, today))

#df = pd.DataFrame({'apple':data_1.Close, 'microsoft':data_2.Close, 'chevron':data_3.Close,})

#dmdl  = pd.stats.ols.OLS(y=df['apple'], x =df[['microsoft','chevron']])

df = pd.DataFrame({'A': [10,20,30,40,50],
                   'B': [20, 30, 10, 40, 50],
                   'C': [32, 234, 23, 23, 42523]})
result = sm.ols(formula="A ~ B + C", data=df).fit()
#jesper = pd.ols(y=df['A'], x=df[['B','C']])
jesper = print(result.summary())

print(result.params[1])

print(result.conf_int(alpha=0.05, cols=None)[0][0])
df2 = pd.DataFrame()

print(df[0])
print (pd.__version__)
print (np.__version__)
print (statsmodels.__version__)

#x = pd.Series(np.random.randn(100))
#sm.ols