def __init__(self, P):
self.R_f = historical.risk_free_return()
self.R_m = historical.LogReturns('^GSPC')
self.P = P
self.R = P.variation()
self.first_year = max(2005, self.R.TimeWindow.begin.year)
self.__sharpe_ratio()
self.__alpha_beta()
def __init__(self, P):
self.R_f = historical.risk_free_return()
self.R_m = historical.LogReturns('^GSPC')
self.P = P
self.R = P.variation()
self.first_year = max(2005, self.R.TimeWindow.begin.year)
self.__sharpe_ratio()
self.__alpha_beta()
def alpha_beta(self, N = 260, market_return = None, risk_free_return = None):
import historical
if market_return is None: market_return = historical.LogReturns('^GSPC')
if risk_free_return is None: risk_free_return = historical.risk_free_return()
self_excess = self - risk_free_return
market_excess = market_return - risk_free_return
b = self_excess.MultLinearRegr(N, market_excess, constant = True, coef = True)[1]
W = b.TimeWindow
alpha = []
beta = []
for i in xrange(len(b)):
alpha.append(b[i][0])
beta.append(b[i][1])
return TimeSerie(alpha, W), TimeSerie(beta, W)
def alpha_beta(self, N=260, market_return=None, risk_free_return=None):
import historical
if market_return is None:
market_return = historical.LogReturns('^GSPC')
if risk_free_return is None:
risk_free_return = historical.risk_free_return()
self_excess = self - risk_free_return
market_excess = market_return - risk_free_return
b = self_excess.MultLinearRegr(N,
market_excess,
constant=True,
coef=True)[1]
W = b.TimeWindow
alpha = []
beta = []
for i in xrange(len(b)):
alpha.append(b[i][0])
beta.append(b[i][1])
return TimeSerie(alpha, W), TimeSerie(beta, W)
