def __init__(self, symbol):

self.symbol = symbol

self.start_date = date(2010,12,31)

self.end_date = date(2013,12,31)

self.period = 12

self.stock_returns = self.calculate_stock_returns()

self.sp_returns = self.calculate_sp_returns()

def get_data(self):

stock = DataReader(self.symbol,'yahoo',self.start_date, self.end_date)

return stock

def get_sp_data(self):

sp = DataReader('^GSPC','yahoo',self.start_date, self.end_date)

return sp

def calculate_stock_returns(self):

stock = self.get_data()

data = pd.DataFrame({'stock_adj_close':stock['Adj Close']}, index=stock.index)

data[['stock_returns']] = data[['stock_adj_close']]/data[['stock_adj_close']].shift(1)-1

stock_return = data.dropna()

return stock_return

def calculate_sp_returns(self):

sp = self.get_sp_data()

data = pd.DataFrame({'sp_adj_close':sp['Adj Close']}, index=sp.index)

data[['sp_returns']] = data[['sp_adj_close']]/data[['sp_adj_close']].shift(1)-1

sp_return = data.dropna()

return sp_return

def compute_covariance(self):

covariance = np.cov(self.stock_returns['stock_returns'], self.sp_returns['sp_returns'])

return covariance

def calculate_beta(self):

covariance = self.compute_covariance()

beta = covariance[0,1]/covariance[1,1]

print('Beta', beta)

return beta

def calculate_alpha(self):

beta = self.calculate_beta()

alpha = np.mean(self.stock_returns['stock_returns'])-beta*np.mean(self.sp_returns['sp_returns'])

annualized_alpha = alpha*self.period

print('annualized_alpha', annualized_alpha)

return alpha

def calculate_r_squared(self):

covariance = self.compute_covariance()

alpha = self.calculate_alpha()

beta = self.calculate_beta()

ypred = alpha+beta*self.sp_returns['sp_returns']

ss_res = np.sum(np.power(ypred-self.stock_returns['stock_returns'],2))

ss_tot = covariance[0,0]*(len(self.stock_returns)-1)

r_squared = 1.-ss_res/ss_tot

print('r_squared', r_squared)

return r_squared

def calculate_volatility(self):

covariance = self.compute_covariance()

volatility = np.sqrt(covariance[0,0])

volatility = volatility*np.sqrt(self.period)

print('volatility', volatility)

def calculate_moment(self):

momentum = np.prod(1+self.stock_returns['stock_returns'].tail(12).values)-1

print('momentum', momentum)