def test_simplex_projection(self):
vector = [random() for _ in range(4)]
proj = utils.simplex_projection(vector)
self.assertIsInstance(proj, np.ndarray)
eq_(len(vector), len(proj))
self.assertAlmostEqual(proj.sum(), 1.0)
eq_(filter(lambda x: x < 0, proj), [])
def test_simplex_projection(self):
vector = [random() for _ in range(4)]
proj = utils.simplex_projection(vector)
self.assertIsInstance(proj, np.ndarray)
eq_(len(vector), len(proj))
self.assertAlmostEqual(proj.sum(), 1.0)
eq_(filter(lambda x: x < 0, proj), [])
def optimize(self, date, to_buy, to_sell, parameters):
# This implementation only process buy signals
allocations = {}
for sid in to_sell:
allocations[sid] = to_sell[sid].amount
data = to_buy
#if not self.initialized:
#self._warm(data)
x_tilde = np.zeros(self.m)
b = np.zeros(self.m)
# find relative moving average price for each security
for i, stock in enumerate(data):
price = data[stock].price
# Relative mean deviation
if 'mavg' in data[stock]:
x_tilde[i] = data[stock]['mavg']['price'] / price
###########################
# Inside of OLMAR (algo 2)
x_bar = x_tilde.mean()
# market relative deviation
mark_rel_dev = x_tilde - x_bar
# Expected return with current portfolio
exp_return = np.dot(self.b_t, x_tilde)
weight = self.eps - exp_return
variability = (np.linalg.norm(mark_rel_dev))**2
# test for divide-by-zero case
if variability == 0.0:
step_size = 0
else:
step_size = max(0, weight / variability)
b = self.b_t + step_size * mark_rel_dev
b_norm = utils.simplex_projection(b)
np.testing.assert_almost_equal(b_norm.sum(), 1)
allocations.update(self._rebalance_portfolio(data, b_norm))
# update portfolio
self.b_t = b_norm
#e_ret = 0
#e_risk = 1
e_ret = exp_return
e_risk = variability
return allocations, e_ret, e_risk
def optimize(self, date, to_buy, to_sell, parameters):
# This implementation only process buy signals
allocations = {}
for sid in to_sell:
allocations[sid] = to_sell[sid].amount
data = to_buy
#if not self.initialized:
#self._warm(data)
x_tilde = np.zeros(self.m)
b = np.zeros(self.m)
# find relative moving average price for each security
for i, stock in enumerate(data):
price = data[stock].price
# Relative mean deviation
if 'mavg' in data[stock]:
x_tilde[i] = data[stock]['mavg']['price'] / price
###########################
# Inside of OLMAR (algo 2)
x_bar = x_tilde.mean()
# market relative deviation
mark_rel_dev = x_tilde - x_bar
# Expected return with current portfolio
exp_return = np.dot(self.b_t, x_tilde)
weight = self.eps - exp_return
variability = (np.linalg.norm(mark_rel_dev)) ** 2
# test for divide-by-zero case
if variability == 0.0:
step_size = 0
else:
step_size = max(0, weight / variability)
b = self.b_t + step_size * mark_rel_dev
b_norm = utils.simplex_projection(b)
np.testing.assert_almost_equal(b_norm.sum(), 1)
allocations.update(self._rebalance_portfolio(data, b_norm))
# update portfolio
self.b_t = b_norm
#e_ret = 0
#e_risk = 1
e_ret = exp_return
e_risk = variability
return allocations, e_ret, e_risk
