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Unverified Commit 565be84d authored by iLampard's avatar iLampard Committed by GitHub
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Merge pull request #14 from alpha-miner/master 

merge update
parents 60fe5dca 8541e9e2
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Showing with 264 additions and 77 deletions
alphamind/cython/optimizers.pyx
View file @ 565be84d
...@@ -10,6 +10,7 @@ cimport numpy as cnp ...@@ -10,6 +10,7 @@ cimport numpy as cnp
from libcpp.string cimport string from libcpp.string cimport string
from libcpp.vector cimport vector from libcpp.vector cimport vector
import numpy as np import numpy as np
from PyFin.api import pyFinAssert
cdef extern from "lpoptimizer.hpp" namespace "pfopt": cdef extern from "lpoptimizer.hpp" namespace "pfopt":
...@@ -71,7 +72,11 @@ cdef extern from "tvoptimizer.hpp" namespace "pfopt": ...@@ -71,7 +72,11 @@ cdef extern from "tvoptimizer.hpp" namespace "pfopt":
double*, double*,
double*, double*,
double, double,
double) except + double,
int,
double*,
double*,
double*) except +
vector[double] xValue() vector[double] xValue()
double feval() double feval()
int status() int status()
...@@ -81,6 +86,7 @@ cdef class CVOptimizer: ...@@ -81,6 +86,7 @@ cdef class CVOptimizer:
cdef TVOptimizer* cobj cdef TVOptimizer* cobj
cdef int n cdef int n
cdef int m cdef int m
cdef int f
def __cinit__(self, def __cinit__(self,
double[:] expected_return, double[:] expected_return,
...@@ -91,20 +97,26 @@ cdef class CVOptimizer: ...@@ -91,20 +97,26 @@ cdef class CVOptimizer:
double[:] clbound=None, double[:] clbound=None,
double[:] cubound=None, double[:] cubound=None,
double target_low=0.0, double target_low=0.0,
double target_high=1.0): double target_high=1.0,
cnp.ndarray[double, ndim=2] factor_cov_matrix=None,
cnp.ndarray[double, ndim=2] factor_loading_matrix=None,
double[:] idsync_risk=None):
self.n = lbound.shape[0] self.n = lbound.shape[0]
self.m = 0 self.m = 0
cdef double[:] cov = cov_matrix.flatten(order='C') self.f = factor_cov_matrix.shape[0] if factor_cov_matrix is not None else 0
cdef double[:] cov = cov_matrix.flatten(order='C') if cov_matrix is not None else None
cdef double[:] cons cdef double[:] cons
cdef double[:] factor_cov = factor_cov_matrix.flatten(order='C') if factor_cov_matrix is not None else None
cdef double[:] factor_loading = factor_loading_matrix.flatten(order='C') if factor_loading_matrix is not None else None
if cons_matrix is not None: if cons_matrix is not None:
self.m = cons_matrix.shape[0] self.m = cons_matrix.shape[0]
cons = cons_matrix.flatten(order='C'); cons = cons_matrix.flatten(order='C')
self.cobj = new TVOptimizer(self.n, self.cobj = new TVOptimizer(self.n,
&expected_return[0], &expected_return[0],
&cov[0], &cov[0] if cov is not None else NULL,
&lbound[0], &lbound[0],
&ubound[0], &ubound[0],
self.m, self.m,
...@@ -112,11 +124,15 @@ cdef class CVOptimizer: ...@@ -112,11 +124,15 @@ cdef class CVOptimizer:
&clbound[0], &clbound[0],
&cubound[0], &cubound[0],
target_low, target_low,
target_high) target_high,
self.f,
&factor_cov[0] if factor_cov is not None else NULL,
&factor_loading[0] if factor_loading is not None else NULL,
&idsync_risk[0] if idsync_risk is not None else NULL)
else: else:
self.cobj = new TVOptimizer(self.n, self.cobj = new TVOptimizer(self.n,
&expected_return[0], &expected_return[0],
&cov[0], &cov[0] if cov is not None else NULL,
&lbound[0], &lbound[0],
&ubound[0], &ubound[0],
0, 0,
...@@ -124,7 +140,11 @@ cdef class CVOptimizer: ...@@ -124,7 +140,11 @@ cdef class CVOptimizer:
NULL, NULL,
NULL, NULL,
target_low, target_low,
target_high) target_high,
self.f,
&factor_cov[0] if factor_cov is not None else NULL,
&factor_loading[0] if factor_loading is not None else NULL,
&idsync_risk[0] if idsync_risk is not None else NULL)
def __dealloc__(self): def __dealloc__(self):
del self.cobj del self.cobj
...@@ -150,7 +170,11 @@ cdef extern from "mvoptimizer.hpp" namespace "pfopt": ...@@ -150,7 +170,11 @@ cdef extern from "mvoptimizer.hpp" namespace "pfopt":
double*, double*,
double*, double*,
double*, double*,
double) except + double,
int,
double*,
double*,
double*) except +
vector[double] xValue() vector[double] xValue()
double feval() double feval()
int status() int status()
...@@ -171,30 +195,36 @@ cdef extern from "qpalglib.hpp" namespace "pfopt": ...@@ -171,30 +195,36 @@ cdef extern from "qpalglib.hpp" namespace "pfopt":
cdef class QPOptimizer: cdef class QPOptimizer:
cdef MVOptimizer* cobj cdef MVOptimizer* cobj
cdef QPAlglib* cobj2
cdef cnp.ndarray er cdef cnp.ndarray er
cdef cnp.ndarray cov cdef cnp.ndarray cov
cdef double risk_aversion cdef double risk_aversion
cdef int n cdef int n
cdef int m cdef int m
cdef int f
def __cinit__(self, def __cinit__(self,
double[:] expected_return, double[:] expected_return,
cnp.ndarray[double, ndim=2] cov_matrix, cnp.ndarray[double, ndim=2] cov_matrix,
double[:] lbound, double[:] lbound,
double[:] ubound, double[:] ubound,
cnp.ndarray[double, ndim=2] cons_matrix=None, cnp.ndarray[double, ndim=2] cons_matrix=None,
double[:] clbound=None, double[:] clbound=None,
double[:] cubound=None, double[:] cubound=None,
double risk_aversion=1.0): double risk_aversion=1.0,
cnp.ndarray[double, ndim=2] factor_cov_matrix=None,
cnp.ndarray[double, ndim=2] factor_loading_matrix=None,
double[:] idsync_risk=None):
self.n = lbound.shape[0] self.n = lbound.shape[0]
self.m = 0 self.m = 0
self.f = factor_cov_matrix.shape[0] if factor_cov_matrix is not None else 0
self.er = np.array(expected_return) self.er = np.array(expected_return)
self.cov = np.array(cov_matrix) self.cov = np.array(cov_matrix)
self.risk_aversion = risk_aversion self.risk_aversion = risk_aversion
cdef double[:] cov = cov_matrix.flatten(order='C') cdef double[:] cov = cov_matrix.flatten(order='C') if cov_matrix is not None else None
cdef double[:] cons cdef double[:] cons
cdef double[:] factor_cov = factor_cov_matrix.flatten(order='C') if factor_cov_matrix is not None else None
cdef double[:] factor_loading = factor_loading_matrix.flatten(order='C') if factor_loading_matrix is not None else None
if cons_matrix is not None: if cons_matrix is not None:
self.m = cons_matrix.shape[0] self.m = cons_matrix.shape[0]
...@@ -202,48 +232,49 @@ cdef class QPOptimizer: ...@@ -202,48 +232,49 @@ cdef class QPOptimizer:
self.cobj = new MVOptimizer(self.n, self.cobj = new MVOptimizer(self.n,
&expected_return[0], &expected_return[0],
&cov[0], &cov[0] if cov is not None else NULL,
&lbound[0], &lbound[0],
&ubound[0], &ubound[0],
self.m, self.m,
&cons[0], &cons[0],
&clbound[0], &clbound[0],
&cubound[0], &cubound[0],
risk_aversion) risk_aversion,
self.f,
&factor_cov[0] if factor_cov is not None else NULL,
&factor_loading[0] if factor_loading is not None else NULL,
&idsync_risk[0] if idsync_risk is not None else NULL)
else: else:
self.cobj2 = new QPAlglib(self.n, # self.cobj2 = new QPAlglib(self.n,
&expected_return[0], # &expected_return[0],
&cov[0], # &cov[0] if cov is not None else NULL,
&lbound[0], # &lbound[0],
&ubound[0], # &ubound[0],
risk_aversion) # risk_aversion)
self.cobj = new MVOptimizer(self.n,
&expected_return[0],
&cov[0] if cov is not None else NULL,
&lbound[0],
&ubound[0],
self.m,
NULL,
NULL,
NULL,
risk_aversion,
self.f,
&factor_cov[0] if factor_cov is not None else NULL,
&factor_loading[0] if factor_loading is not None else NULL,
&idsync_risk[0] if idsync_risk is not None else NULL)
def __dealloc__(self): def __dealloc__(self):
if self.cobj: del self.cobj
del self.cobj
else:
del self.cobj2
def feval(self): def feval(self):
if self.cobj: return self.cobj.feval()
return self.cobj.feval()
else:
x = np.array(self.cobj2.xValue())
return 0.5 * self.risk_aversion * x @ self.cov @ x - self.er @ x
def x_value(self): def x_value(self):
if self.cobj: return np.array(self.cobj.xValue())
return np.array(self.cobj.xValue())
else:
return np.array(self.cobj2.xValue())
def status(self): def status(self):
if self.cobj: return self.cobj.status()
return self.cobj.status()
else:
status = self.cobj2.status()
if 1 <= status <= 4:
return 0
else:
return status
alphamind/data/engines/sqlengine.py
View file @ 565be84d
...@@ -371,7 +371,7 @@ class SqlEngine(object): ...@@ -371,7 +371,7 @@ class SqlEngine(object):
res['chgPct'] = df.chgPct res['chgPct'] = df.chgPct
res = res.loc[ref_date] res = res.loc[ref_date]
res.index = list(range(len(res))) res.index = list(range(len(res)))
return res.drop_duplicates(['trade_date', 'code']) return res
def fetch_factor_range(self, def fetch_factor_range(self,
universe: Universe, universe: Universe,
......
pfopt @ 40976afd
Subproject commit 1dcecd88728512ff50730f418d9d58195bf33851 Subproject commit 40976afd3ea03b921177cef364fa8a6b37bf4dda
alphamind/portfolio/meanvariancebuilder.py
View file @ 565be84d
...@@ -51,7 +51,10 @@ def mean_variance_builder(er: np.ndarray, ...@@ -51,7 +51,10 @@ def mean_variance_builder(er: np.ndarray,
ubound: Union[np.ndarray, float], ubound: Union[np.ndarray, float],
risk_exposure: Optional[np.ndarray], risk_exposure: Optional[np.ndarray],
risk_target: Optional[Tuple[np.ndarray, np.ndarray]], risk_target: Optional[Tuple[np.ndarray, np.ndarray]],
lam: float=1.) -> Tuple[str, float, np.ndarray]: lam: float=1.,
factor_cov: np.ndarray=None,
factor_loading: np.ndarray=None,
idsync: np.ndarray=None) -> Tuple[str, float, np.ndarray]:
lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target) lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target)
optimizer = QPOptimizer(er, optimizer = QPOptimizer(er,
...@@ -61,7 +64,10 @@ def mean_variance_builder(er: np.ndarray, ...@@ -61,7 +64,10 @@ def mean_variance_builder(er: np.ndarray,
cons_mat, cons_mat,
clbound, clbound,
cubound, cubound,
lam) lam,
factor_cov,
factor_loading,
idsync)
return _create_result(optimizer, bm) return _create_result(optimizer, bm)
...@@ -74,7 +80,10 @@ def target_vol_builder(er: np.ndarray, ...@@ -74,7 +80,10 @@ def target_vol_builder(er: np.ndarray,
risk_exposure: Optional[np.ndarray], risk_exposure: Optional[np.ndarray],
risk_target: Optional[Tuple[np.ndarray, np.ndarray]], risk_target: Optional[Tuple[np.ndarray, np.ndarray]],
vol_low: float = 0., vol_low: float = 0.,
vol_high: float = 1.)-> Tuple[str, float, np.ndarray]: vol_high: float = 1.,
factor_cov: np.ndarray = None,
factor_loading: np.ndarray = None,
idsync: np.ndarray = None)-> Tuple[str, float, np.ndarray]:
lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target) lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target)
optimizer = CVOptimizer(er, optimizer = CVOptimizer(er,
...@@ -85,7 +94,10 @@ def target_vol_builder(er: np.ndarray, ...@@ -85,7 +94,10 @@ def target_vol_builder(er: np.ndarray,
clbound, clbound,
cubound, cubound,
vol_low, vol_low,
vol_high) vol_high,
factor_cov,
factor_loading,
idsync)
return _create_result(optimizer, bm) return _create_result(optimizer, bm)
......
alphamind/tests/cython/test_optimizers.py
View file @ 565be84d
...@@ -54,6 +54,36 @@ class TestOptimizers(unittest.TestCase): ...@@ -54,6 +54,36 @@ class TestOptimizers(unittest.TestCase):
[0.1996, 0.3004, 0.5000], [0.1996, 0.3004, 0.5000],
4) 4)
def test_qpoptimizer_with_factor_model(self):
objective = np.array([0.1, 0.2, 0.3])
lbound = np.array([0.0, 0.0, 0.0])
ubound = np.array([1.0, 1.0, 1.0])
factor_var = np.array([[0.5, -0.3], [-0.3, 0.7]])
factor_load = np.array([[0.8, 0.2], [0.5, 0.5], [0.2, 0.8]])
idsync = np.array([0.1, 0.3, 0.2])
cons = np.array([[1., 1., 1.]])
clbound = np.array([1.])
cubound = np.array([1.])
optimizer = QPOptimizer(objective,
None,
lbound,
ubound,
cons,
clbound,
cubound,
1.,
factor_var,
factor_load,
idsync)
# check against cvxpy result
np.testing.assert_array_almost_equal(optimizer.x_value(),
[0.2866857, 0.21416417, 0.49915014],
4)
def test_qpoptimizer_with_identity_matrix(self): def test_qpoptimizer_with_identity_matrix(self):
objective = np.array([-0.02, 0.01, 0.03]) objective = np.array([-0.02, 0.01, 0.03])
cov = np.diag([1., 1., 1.]) cov = np.diag([1., 1., 1.])
...@@ -122,6 +152,38 @@ class TestOptimizers(unittest.TestCase): ...@@ -122,6 +152,38 @@ class TestOptimizers(unittest.TestCase):
[-0.3, -0.10919033, 0.40919033], [-0.3, -0.10919033, 0.40919033],
4) 4)
def test_cvoptimizer_with_factor_model(self):
objective = np.array([0.1, 0.2, 0.3])
lbound = np.array([0.0, 0.0, 0.0])
ubound = np.array([1.0, 1.0, 1.0])
factor_var = np.array([[0.5, -0.3], [-0.3, 0.7]])
factor_load = np.array([[0.8, 0.2], [0.5, 0.5], [0.2, 0.8]])
idsync = np.array([0.1, 0.3, 0.2])
cons = np.array([[1., 1., 1.]])
clbound = np.array([1.])
cubound = np.array([1.])
target_vol = 0.5
optimizer = CVOptimizer(objective,
None,
lbound,
ubound,
cons,
clbound,
cubound,
0.,
target_vol,
factor_var,
factor_load,
idsync)
# check against cvxpy result
np.testing.assert_array_almost_equal(optimizer.x_value(),
[0.26595552, 0.21675092, 0.51729356],
4)
def test_cvoptimizer_with_cons_and_ieq(self): def test_cvoptimizer_with_cons_and_ieq(self):
objective = np.array([0.1, 0.2, 0.3]) objective = np.array([0.1, 0.2, 0.3])
cov = np.array([[0.05, 0.01, 0.02], cov = np.array([[0.05, 0.01, 0.02],
......
notebooks/Example 7 - Portfolio Optimizer Performance.ipynb
View file @ 565be84d
...@@ -243,7 +243,7 @@ ...@@ -243,7 +243,7 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"from cvxpy import quad_form\n", "from cvxpy import *\n",
"\n", "\n",
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n", "df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n",
"number = 1\n", "number = 1\n",
...@@ -262,16 +262,36 @@ ...@@ -262,16 +262,36 @@
" lbound = -np.ones(n) * np.inf\n", " lbound = -np.ones(n) * np.inf\n",
" ubound = np.ones(n) * np.inf\n", " ubound = np.ones(n) * np.inf\n",
"\n", "\n",
" status, y, x1 = mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\n", " status, y, x1 = mean_variance_builder(er,\n",
" elasped_time1 = timeit.timeit(\"mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\",\n", " None,\n",
" bm,\n",
" lbound,\n",
" ubound,\n",
" None,\n",
" None,\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\n",
" elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n",
" None,\n",
" bm,\n",
" lbound,\n",
" ubound,\n",
" None,\n",
" None,\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\"\"\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
" risk = quad_form(w, sec_cov)\n", " risk = sum_squares(mul_elemwise(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n",
" objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n", " objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n",
" prob = cvxpy.Problem(objective)\n", " prob = cvxpy.Problem(objective)\n",
" prob.solve(solver='CVXOPT')\n", " prob.solve(solver='ECOS')\n",
" elasped_time2 = timeit.timeit(\"prob.solve(solver='CVXOPT')\",\n", " elasped_time2 = timeit.timeit(\"prob.solve(solver='ECOS')\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n", " u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n",
...@@ -325,18 +345,38 @@ ...@@ -325,18 +345,38 @@
" lbound = np.zeros(n)\n", " lbound = np.zeros(n)\n",
" ubound = np.ones(n) * 0.1\n", " ubound = np.ones(n) * 0.1\n",
"\n", "\n",
" status, y, x1 = mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\n", " status, y, x1 = mean_variance_builder(er,\n",
" elasped_time1 = timeit.timeit(\"mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\",\n", " None,\n",
" bm,\n",
" lbound,\n",
" ubound,\n",
" None,\n",
" None,\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\n",
" elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n",
" None,\n",
" bm,\n",
" lbound,\n",
" ubound,\n",
" None,\n",
" None,\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\"\"\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
" risk = quad_form(w, sec_cov)\n", " risk = sum_squares(mul_elemwise(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n",
" objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n", " objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
" w <= ubound]\n", " w <= ubound]\n",
" prob = cvxpy.Problem(objective, constraints)\n", " prob = cvxpy.Problem(objective, constraints)\n",
" prob.solve(solver='CVXOPT')\n", " prob.solve(solver='ECOS')\n",
" elasped_time2 = timeit.timeit(\"prob.solve(solver='CVXOPT')\",\n", " elasped_time2 = timeit.timeit(\"prob.solve(solver='ECOS')\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n", " u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n",
...@@ -393,20 +433,40 @@ ...@@ -393,20 +433,40 @@
" risk_constraints = np.ones((len(er), 1))\n", " risk_constraints = np.ones((len(er), 1))\n",
" risk_target = (np.array([1.]), np.array([1.]))\n", " risk_target = (np.array([1.]), np.array([1.]))\n",
"\n", "\n",
" status, y, x1 = mean_variance_builder(er, sec_cov, bm, lbound, ubound, risk_constraints, risk_target, lam=1)\n", " status, y, x1 = mean_variance_builder(er,\n",
" elasped_time1 = timeit.timeit(\"mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\",\n", " None,\n",
" bm,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\n",
" elasped_time1 = timeit.timeit(\"\"\"mean_variance_builder(er,\n",
" None,\n",
" bm,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" lam=1,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\"\"\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
" risk = quad_form(w, sec_cov)\n", " risk = sum_squares(mul_elemwise(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n",
" objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n", " objective = cvxpy.Minimize(-w.T * er + 0.5 * risk)\n",
" curr_risk_exposure = risk_constraints.T @ w\n", " curr_risk_exposure = risk_constraints.T @ w\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
" w <= ubound,\n", " w <= ubound,\n",
" curr_risk_exposure == risk_target[0]]\n", " curr_risk_exposure == risk_target[0]]\n",
" prob = cvxpy.Problem(objective, constraints)\n", " prob = cvxpy.Problem(objective, constraints)\n",
" prob.solve(solver='CVXOPT')\n", " prob.solve(solver='ECOS')\n",
" elasped_time2 = timeit.timeit(\"prob.solve(solver='CVXOPT')\",\n", " elasped_time2 = timeit.timeit(\"prob.solve(solver='ECOS')\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
"\n", "\n",
" u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n", " u1 = -x1 @ er + 0.5 * x1 @ sec_cov @ x1\n",
...@@ -445,7 +505,7 @@ ...@@ -445,7 +505,7 @@
"source": [ "source": [
"df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n", "df = pd.DataFrame(columns=u_names, index=['cvxpy', 'alphamind'])\n",
"number = 1\n", "number = 1\n",
"target_vol = 0.1\n", "target_vol = 0.5\n",
"\n", "\n",
"\n", "\n",
"for u_name, sample_data in zip(u_names, data_set):\n", "for u_name, sample_data in zip(u_names, data_set):\n",
...@@ -468,12 +528,34 @@ ...@@ -468,12 +528,34 @@
" risk_constraints = np.ones((n, 1))\n", " risk_constraints = np.ones((n, 1))\n",
" risk_target = (np.array([bm.sum()]), np.array([bm.sum()]))\n", " risk_target = (np.array([bm.sum()]), np.array([bm.sum()]))\n",
"\n", "\n",
" status, y, x1 = target_vol_builder(er, sec_cov, bm, lbound, ubound, risk_constraints, risk_target, vol_low=0, vol_high=target_vol)\n", " status, y, x1 = target_vol_builder(er,\n",
" elasped_time1 = timeit.timeit(\"mean_variance_builder(er, sec_cov, bm, lbound, ubound, None, None, lam=1)\",\n", " None,\n",
" bm,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" vol_low=0,\n",
" vol_high=target_vol,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\n",
" elasped_time1 = timeit.timeit(\"\"\"target_vol_builder(er,\n",
" None,\n",
" bm,\n",
" lbound,\n",
" ubound,\n",
" risk_constraints,\n",
" risk_target,\n",
" vol_low=0,\n",
" vol_high=target_vol,\n",
" factor_cov=risk_cov / 10000.,\n",
" factor_loading=risk_exposure,\n",
" idsync=(special_risk ** 2.) / 10000)\"\"\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
" \n", " \n",
" w = cvxpy.Variable(n)\n", " w = cvxpy.Variable(n)\n",
" risk = quad_form(w - bm, sec_cov)\n", " risk = sum_squares(mul_elemwise(special_risk / 100., w)) + quad_form((w.T * risk_exposure).T, risk_cov / 10000.)\n",
" objective = cvxpy.Minimize(-w.T * er)\n", " objective = cvxpy.Minimize(-w.T * er)\n",
" curr_risk_exposure = risk_constraints.T @ w\n", " curr_risk_exposure = risk_constraints.T @ w\n",
" constraints = [w >= lbound,\n", " constraints = [w >= lbound,\n",
...@@ -481,8 +563,8 @@ ...@@ -481,8 +563,8 @@
" curr_risk_exposure == risk_target[0],\n", " curr_risk_exposure == risk_target[0],\n",
" risk <= target_vol * target_vol]\n", " risk <= target_vol * target_vol]\n",
" prob = cvxpy.Problem(objective, constraints)\n", " prob = cvxpy.Problem(objective, constraints)\n",
" prob.solve(solver='CVXOPT')\n", " prob.solve(solver='ECOS')\n",
" elasped_time2 = timeit.timeit(\"prob.solve(solver='CVXOPT')\",\n", " elasped_time2 = timeit.timeit(\"prob.solve(solver='ECOS')\",\n",
" number=number, globals=globals()) / number * 1000\n", " number=number, globals=globals()) / number * 1000\n",
"\n", "\n",
" u1 = -x1 @ er\n", " u1 = -x1 @ er\n",
......
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