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Commit 94e6ee52 authored by Dr.李's avatar Dr.李
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merge 2 implementation

parent 2c94271e
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Showing with 56 additions and 79 deletions
alphamind/portfolio/linearbuilder.py
View file @ 94e6ee52
......@@ -15,19 +15,18 @@ def linear_build(er: np.ndarray,
lbound: Union[np.ndarray, float],
ubound: Union[np.ndarray, float],
risk_constraints: np.ndarray,
risk_target: Tuple[np.ndarray, np.ndarray]) -> Tuple[str, np.ndarray, np.ndarray]:
risk_target: Tuple[np.ndarray, np.ndarray],
turn_over_target: float = None,
current_position: np.ndarray = None) -> Tuple[str, np.ndarray, np.ndarray]:
er = er.flatten()
n, m = risk_constraints.shape
if not risk_target:
risk_lbound = -np.inf * np.ones(m)
risk_ubound = np.inf * np.ones(m)
cons_matrix = np.concatenate((risk_constraints.T, risk_lbound.reshape((-1, 1)), risk_ubound.reshape((-1, 1))),
axis=1)
risk_lbound = -np.inf * np.ones((m, 1))
risk_ubound = np.inf * np.ones((m, 1))
else:
cons_matrix = np.concatenate(
(risk_constraints.T, risk_target[0].reshape((-1, 1)), risk_target[1].reshape((-1, 1))),
axis=1)
risk_lbound = risk_target[0].reshape((-1, 1))
risk_ubound = risk_target[1].reshape((-1, 1))
if isinstance(lbound, float):
lbound = np.ones(n) * lbound
......@@ -35,78 +34,57 @@ def linear_build(er: np.ndarray,
if isinstance(ubound, float):
ubound = np.ones(n) * ubound
opt = LPOptimizer(cons_matrix, lbound, ubound, -er)
status = opt.status()
if not turn_over_target:
cons_matrix = np.concatenate((risk_constraints.T, risk_lbound, risk_ubound), axis=1)
opt = LPOptimizer(cons_matrix, lbound, ubound, -er)
if status == 0:
status = 'optimal'
status = opt.status()
return status, opt.feval(), opt.x_value()
if status == 0:
status = 'optimal'
def linear_build_with_to_constraint(er: np.ndarray,
lbound: Union[np.ndarray, float],
ubound: Union[np.ndarray, float],
risk_constraints: np.ndarray,
risk_target: Tuple[np.ndarray, np.ndarray],
turn_over_target: float,
current_position: np.ndarray):
er = er.flatten()
current_position = current_position.reshape((-1, 1))
n, m = risk_constraints.shape
if not risk_target:
risk_lbound = -np.inf * np.ones((m, 1))
risk_ubound = np.inf * np.ones((m, 1))
return status, opt.feval(), opt.x_value()
else:
risk_lbound = risk_target[0].reshape((-1, 1))
risk_ubound = risk_target[1].reshape((-1, 1))
if isinstance(lbound, float):
lbound = np.ones(n) * lbound
if isinstance(ubound, float):
ubound = np.ones(n) * ubound
current_position = current_position.reshape((-1, 1))
# we need to expand bounded condition and constraint matrix to handle L1 bound
lbound = np.concatenate((lbound, np.zeros(n)), axis=0)
ubound = np.concatenate((ubound, np.inf * np.ones(n)), axis=0)
# we need to expand bounded condition and constraint matrix to handle L1 bound
lbound = np.concatenate((lbound, np.zeros(n)), axis=0)
ubound = np.concatenate((ubound, np.inf * np.ones(n)), axis=0)
risk_lbound = np.concatenate((risk_lbound, [[0.]]), axis=0)
risk_ubound = np.concatenate((risk_ubound, [[turn_over_target]]), axis=0)
risk_lbound = np.concatenate((risk_lbound, [[0.]]), axis=0)
risk_ubound = np.concatenate((risk_ubound, [[turn_over_target]]), axis=0)
risk_constraints = np.concatenate((risk_constraints.T, np.zeros((m, n))), axis=1)
er = np.concatenate((er, np.zeros(n)), axis=0)
risk_constraints = np.concatenate((risk_constraints.T, np.zeros((m, n))), axis=1)
er = np.concatenate((er, np.zeros(n)), axis=0)
turn_over_row = np.zeros(2 * n)
turn_over_row[n:] = 1.
risk_constraints = np.concatenate((risk_constraints, [turn_over_row]), axis=0)
turn_over_row = np.zeros(2 * n)
turn_over_row[n:] = 1.
risk_constraints = np.concatenate((risk_constraints, [turn_over_row]), axis=0)
turn_over_matrix = np.zeros((2 * n, 2 * n))
for i in range(n):
turn_over_matrix[i, i] = 1.
turn_over_matrix[i, i + n] = -1.
turn_over_matrix[i + n, i] = 1.
turn_over_matrix[i + n, i + n] = 1.
turn_over_matrix = np.zeros((2 * n, 2 * n))
for i in range(n):
turn_over_matrix[i, i] = 1.
turn_over_matrix[i, i + n] = -1.
turn_over_matrix[i + n, i] = 1.
turn_over_matrix[i + n, i + n] = 1.
risk_constraints = np.concatenate((risk_constraints, turn_over_matrix), axis=0)
risk_constraints = np.concatenate((risk_constraints, turn_over_matrix), axis=0)
risk_lbound = np.concatenate((risk_lbound, -np.inf * np.ones((n, 1))), axis=0)
risk_lbound = np.concatenate((risk_lbound, current_position), axis=0)
risk_lbound = np.concatenate((risk_lbound, -np.inf * np.ones((n, 1))), axis=0)
risk_lbound = np.concatenate((risk_lbound, current_position), axis=0)
risk_ubound = np.concatenate((risk_ubound, current_position), axis=0)
risk_ubound = np.concatenate((risk_ubound, np.inf * np.ones((n, 1))), axis=0)
risk_ubound = np.concatenate((risk_ubound, current_position), axis=0)
risk_ubound = np.concatenate((risk_ubound, np.inf * np.ones((n, 1))), axis=0)
cons_matrix = np.concatenate((risk_constraints, risk_lbound, risk_ubound), axis=1)
opt = LPOptimizer(cons_matrix, lbound, ubound, -er)
cons_matrix = np.concatenate((risk_constraints, risk_lbound, risk_ubound), axis=1)
opt = LPOptimizer(cons_matrix, lbound, ubound, -er)
status = opt.status()
status = opt.status()
if status == 0:
status = 'optimal'
if status == 0:
status = 'optimal'
return status, opt.feval(), opt.x_value()[:n]
return status, opt.feval(), opt.x_value()[:n]
if __name__ == '__main__':
......@@ -122,13 +100,13 @@ if __name__ == '__main__':
risk_lbound = np.ones(1)
risk_ubound = np.ones(1)
status, fvalue, x_values = linear_build_with_to_constraint(er,
lb,
ub,
cons,
(risk_lbound, risk_ubound),
turn_over_target,
current_pos)
status, fvalue, x_values = linear_build(er,
lb,
ub,
cons,
(risk_lbound, risk_ubound),
turn_over_target,
current_pos)
print(status)
print(fvalue)
......
alphamind/tests/portfolio/test_linearbuild.py
View file @ 94e6ee52
......@@ -8,7 +8,6 @@ Created on 2017-5-5
import unittest
import numpy as np
from alphamind.portfolio.linearbuilder import linear_build
from alphamind.portfolio.linearbuilder import linear_build_with_to_constraint
class TestLinearBuild(unittest.TestCase):
......@@ -76,13 +75,13 @@ class TestLinearBuild(unittest.TestCase):
risk_lbound[:-1] = risk_lbound[:-1] - risk_tolerance
risk_ubound[:-1] = risk_ubound[:-1] + risk_tolerance
status, _, w = linear_build_with_to_constraint(self.er,
0.,
0.01,
self.risk_exp,
risk_target=(risk_lbound, risk_ubound),
turn_over_target=turn_over_target,
current_position=self.current_pos)
status, _, w = linear_build(self.er,
0.,
0.01,
self.risk_exp,
risk_target=(risk_lbound, risk_ubound),
turn_over_target=turn_over_target,
current_position=self.current_pos)
self.assertEqual(status, 'optimal')
self.assertAlmostEqual(np.sum(w), 1.)
self.assertTrue(np.all(w <= 0.01 + eplson))
......
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