def getFrontier(na_data):
"""Function gets a 100 sample point frontier for given returns"""
# Special Case with fTarget=None, just returns average rets.
(na_avgrets, na_std, b_error) = tsu.OptPort(na_data, None)
# Declaring bounds on the optimized portfolio
na_lower = np.zeros(na_data.shape[1])
na_upper = np.ones(na_data.shape[1])
# Getting the range of possible returns with these bounds
(f_min, f_max) = tsu.getRetRange(na_data, na_lower, na_upper, na_avgrets, s_type="long")
# Getting the step size and list of returns to optimize for.
f_step = (f_max - f_min) / 100.0
lf_returns = [f_min + x * f_step for x in range(101)]
# Declaring empty lists
lf_std = []
lna_portfolios = []
# Calling the optimization for all returns
for f_target in lf_returns:
(na_weights, f_std, b_error) = tsu.OptPort(na_data, f_target, na_lower, na_upper, s_type="long")
lf_std.append(f_std)
lna_portfolios.append(na_weights)
return (lf_returns, lf_std, lna_portfolios, na_avgrets, na_std)
def getFrontier(naData):
''' Special case for fTarget = None, just get average returns '''
(naAvgRets, naStd, b_error) = tsu.OptPort(naData, None)
naLower = np.zeros(naData.shape[1])
naUpper = np.ones(naData.shape[1])
(fMin, fMax) = tsu.getRetRange(naData,
naLower,
naUpper,
naAvgRets,
s_type="long")
fStep = (fMax - fMin) / 100.0
lfReturn = [fMin + x * fStep for x in range(101)]
lfStd = []
lnaPortfolios = []
''' Call the function 100 times for the given range '''
for fTarget in lfReturn:
(naWeights, fStd, b_error) = tsu.OptPort(naData,
fTarget,
naLower,
naUpper,
s_type="long")
#if b_error == False:
lfStd.append(fStd)
lnaPortfolios.append(naWeights)
#lfReturn.pop(lfReturn.index(fTarget))
return (lfReturn, lfStd, lnaPortfolios, naAvgRets, naStd)
def getFrontier(naData):
''' Special case for fTarget = None, just get average returns '''
(naAvgRets,naStd, b_error) = tsu.OptPort( naData, None )
naLower = np.zeros(naData.shape[1])
naUpper = np.ones(naData.shape[1])
(fMin, fMax) = tsu.getRetRange( naData, naLower, naUpper, naAvgRets, s_type="long")
'''Modification- squeeze fMin/fMax to avoid infeasability on the
upper and lower limits. These usually manifest with a CVXOPT
error, and QSTK responds by telling you the KKT matrix is singular
By avoiding the absolute edges you can eliminate this error'''
reduce_amount = 0.0001
fMin += reduce_amount
fMax -= reduce_amount
'''End Modification'''
fStep = (fMax - fMin) / 100.0
lfReturn = [fMin + x * fStep for x in range(101)]
lfStd = []
lnaPortfolios = []
''' Call the function 100 times for the given range '''
for fTarget in lfReturn:
(naWeights, fStd, b_error) = tsu.OptPort( naData, fTarget, naLower, naUpper, s_type = "long")
#if b_error == False:
lfStd.append(fStd)
lnaPortfolios.append( naWeights )
#lfReturn.pop(lfReturn.index(fTarget))
return (lfReturn, lfStd, lnaPortfolios, naAvgRets, naStd)
def getFrontier(naData):
''' Special case for fTarget = None, just get average returns '''
(naAvgRets,naStd, b_error) = tsu.OptPort( naData, None )
naLower = np.zeros(naData.shape[1])
naUpper = np.ones(naData.shape[1])
(fMin, fMax) = tsu.getRetRange( naData, naLower, naUpper, naAvgRets, s_type="long")
fStep = (fMax - fMin) / 100.0
lfReturn = [fMin + x * fStep for x in range(101)]
lfStd = []
lnaPortfolios = []
''' Call the function 100 times for the given range '''
for fTarget in lfReturn:
(naWeights, fStd, b_error) = tsu.OptPort( naData, fTarget, naLower, naUpper, s_type = "long")
#if b_error == False:
lfStd.append(fStd)
lnaPortfolios.append( naWeights )
#lfReturn.pop(lfReturn.index(fTarget))
return (lfReturn, lfStd, lnaPortfolios, naAvgRets, naStd)
