def testPortfolioOptimizerWithMinimumVolatility(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(
guess, self.navData, OptTarget.VOL)
matlab_res = self.create_value_dict(self.instruments, [
0, 0, 0, 6.99175721467524e-39, 1.87467007228981e-38,
0.00376413514611989, 0, 7.75214182640125e-38, 0.000712597692602067,
0, 0, 0.994237264821637, 0.00128600233964123, 7.05296610493373e-38
])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMinimumVolatilityWithRebalance(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(
guess, self.navData, OptTarget.VOL, True)
matlab_res = self.create_value_dict(self.instruments, [
1.17549435082229e-38, 0, 0, 1.68242628961440e-37, 0,
0.00209693665998001, 0, 1.53548949576161e-37, 0.00135190283349081,
0, 6.31828213566980e-38, 0.994988759101536, 0.00156240140499358, 0
])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMaximumReturn(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(
guess, self.navData, OptTarget.RETURN)
matlab_res = self.create_value_dict(self.instruments, [
4.75393550839560e-19, 0, 3.43546906184303e-19, 0, 0,
8.36677837797548e-20, 0, 5.76673494924112e-19,
4.05923081267658e-19, 0, 3.12250225675825e-17,
2.41998887084620e-20, 0, 1
])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMaximumSharpWithRebalance(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(
guess, self.navData, OptTarget.SHARP, True)
matlab_res = self.create_value_dict(self.instruments, [
2.76471048953164e-22, 7.19170303955622e-21, 2.52983701805491e-21,
1.94636169061746e-22, 0, 0, 0, 0.000165267959303058,
0.00137258304784842, 0, 2.06247008802505e-22, 0.995739327032537,
0.00134638898667400, 0.00137643297363735
])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMaximumSharp(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(
guess, self.navData, OptTarget.SHARP)
matlab_res = self.create_value_dict(self.instruments, [
3.72414607721023e-39, 5.58329252509498e-40, 8.50903300813402e-40,
9.42738796971393e-40, 4.83561316181437e-40, 0.00102720032914267,
4.40810381558358e-39, 0.000145735792353755, 0.000756493258973369,
3.91725820023446e-40, 0, 0.996553125979859, 0.00117153574239032,
0.000345908897280735
])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMaximumReturnWithRebalance(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(
guess, self.navData, OptTarget.RETURN, True)
matlab_res = self.create_value_dict(self.instruments, [
1.59234884538490e-17, 1.64287856671892e-18, 2.31111593326468e-33,
5.02151358940695e-17, 0, 9.41672950270667e-18,
1.64287856671892e-18, 2.82144347121750e-17, 0,
2.24595602784406e-17, 2.44504165203472e-16, -1.68518870133883e-34,
0, 1.00000000000000
])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMinimumVolatility(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(guess, self.navData, OptTarget.VOL)
matlab_res = self.create_value_dict(self.instruments, [0,
0,
0,
6.99175721467524e-39,
1.87467007228981e-38,
0.00376413514611989,
0,
7.75214182640125e-38,
0.000712597692602067,
0,
0,
0.994237264821637,
0.00128600233964123,
7.05296610493373e-38])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMaximumReturn(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(guess, self.navData, OptTarget.RETURN)
matlab_res = self.create_value_dict(self.instruments, [4.75393550839560e-19,
0,
3.43546906184303e-19,
0,
0,
8.36677837797548e-20,
0,
5.76673494924112e-19,
4.05923081267658e-19,
0,
3.12250225675825e-17,
2.41998887084620e-20,
0,
1])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMaximumSharpWithRebalance(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(guess, self.navData, OptTarget.SHARP, True)
matlab_res = self.create_value_dict(self.instruments, [2.76471048953164e-22,
7.19170303955622e-21,
2.52983701805491e-21,
1.94636169061746e-22,
0,
0,
0,
0.000165267959303058,
0.00137258304784842,
0,
2.06247008802505e-22,
0.995739327032537,
0.00134638898667400,
0.00137643297363735])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMinimumVolatilityWithRebalance(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(guess, self.navData, OptTarget.VOL, True)
matlab_res = self.create_value_dict(self.instruments, [1.17549435082229e-38,
0,
0,
1.68242628961440e-37,
0,
0.00209693665998001,
0,
1.53548949576161e-37,
0.00135190283349081,
0,
6.31828213566980e-38,
0.994988759101536,
0.00156240140499358,
0])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMaximumReturnWithRebalance(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(guess, self.navData, OptTarget.RETURN, True)
matlab_res = self.create_value_dict(self.instruments, [1.59234884538490e-17,
1.64287856671892e-18,
2.31111593326468e-33,
5.02151358940695e-17,
0,
9.41672950270667e-18,
1.64287856671892e-18,
2.82144347121750e-17,
0,
2.24595602784406e-17,
2.44504165203472e-16,
-1.68518870133883e-34,
0,
1.00000000000000])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)
def testPortfolioOptimizerWithMaximumSharp(self):
guess = [0.1 / len(self.instruments)] * len(self.instruments)
out, fx, its, imode, smode = portfolio_optimization(guess, self.navData, OptTarget.SHARP)
matlab_res = self.create_value_dict(self.instruments, [3.72414607721023e-39,
5.58329252509498e-40,
8.50903300813402e-40,
9.42738796971393e-40,
4.83561316181437e-40,
0.00102720032914267,
4.40810381558358e-39,
0.000145735792353755,
0.000756493258973369,
3.91725820023446e-40,
0,
0.996553125979859,
0.00117153574239032,
0.000345908897280735])
for name in matlab_res:
self.assertAlmostEqual(matlab_res[name], out[name]['weight'], 4)