import sys
import pandas as pd
from pandas.io.data import DataReader
from datetime import date, timedelta
import numpy as np


ticker = sys.argv[1]
n = int(sys.argv[2])

start = (date.today() - timedelta(days=n))

end = date.today()

# Get data frame of that period
df = DataReader(ticker ,'yahoo',start,end)
dfm = DataReader('^GSPC','yahoo',start,end)

# Group the data monthly

resample_stock_series = df.resample('M').last()
resample_mkt_series = dfm.resample('M').last()

#create new combined dataframe by using ADJ_CLOSE column of respective data frame

dfsm = pd.DataFrame({'s_adjclose' : resample_stock_series['Adj Close'],
                        'm_adjclose' : resample_mkt_series['Adj Close']},
                        index=resample_stock_series.index)

# Calculate the returns based on close
dfsm[['s_returns','m_returns']] = dfsm[['s_adjclose','m_adjclose']]/\
    dfsm[['s_adjclose','m_adjclose']].shift(1) -1

# Calculate the volatility of a stock 

dfsm = dfsm.dropna()
covarience_matrix = np.cov(dfsm["s_returns"],dfsm["m_returns"])
volatility = np.sqrt(covarience_matrix[0,0])

print volatility