def markowitz_r_spe_bl(funddfr, P, eta, alpha, bounds):
rf = Const.rf
final_risk = 0
final_return = 0
final_ws = []
final_ws = list(1.0 * np.ones(len(funddfr.columns)) / len(funddfr.columns))
final_sharp = -np.inf
final_codes = []
codes = funddfr.columns
return_rate = []
for code in codes:
return_rate.append(funddfr[code].values)
if eta.size == 0:
risks, returns, ws = fin.efficient_frontier_spe(return_rate, bounds)
else:
risks, returns, ws = fin.efficient_frontier_spe_bl(
return_rate, P, eta, alpha, bounds)
for j in range(0, len(risks)):
if risks[j] == 0:
continue
sharp = (returns[j] - rf) / risks[j]
if sharp > final_sharp:
final_risk = risks[j]
final_return = returns[j]
final_ws = ws[j]
final_sharp = sharp
return final_risk, final_return, final_ws, final_sharp
def markowitz_r_spe_bl(self, funddfr, P, eta, alpha, bounds):
final_risk = 0
final_return = 0
final_ws = []
final_ws = list(1.0 * np.ones(len(funddfr.columns)) / len(funddfr.columns))
final_sharpe = -np.inf
final_codes = []
codes = funddfr.columns
return_rate = []
for code in codes:
return_rate.append(funddfr[code].values)
if eta.size == 0:
risk, ret, ws = fin.efficient_frontier_spe(return_rate, bounds)
else:
risk, ret, ws = fin.efficient_frontier_spe_bl(return_rate, P, eta, alpha, bounds)
for j in range(len(risk)):
if risks[j] == 0:
continue
sharpe = (ret[j] - Const.rf) / risk[j]
if sharpe > final_sharp:
final_risk = risk[j]
final_return = ret[j]
final_ws = ws[j]
final_sharpe = sharpe
# final_res = np.append(final_ws, np.array([final_risk, final_ret, final_sharpe]))
return final_ws