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alpha-mind
Commit b482c543 authored by Dr.李's avatar Dr.李
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reimplementation of risk model and mean variance builder

parent 8b061a73
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Showing with 49 additions and 69 deletions
alphamind/portfolio/meanvariancebuilder.py
View file @ b482c543
......@@ -9,11 +9,9 @@ import numpy as np
from typing import Union
from typing import Tuple
from typing import Optional
from typing import Dict
from alphamind.cython.optimizers import QPOptimizer
from alphamind.cython.optimizers import CVOptimizer
from alphamind.portfolio.riskmodel import RiskModel
from alphamind.portfolio.riskmodel import FullRiskModel
from alphamind.portfolio.riskmodel import FactorRiskModel
def _create_bounds(lbound,
......@@ -48,7 +46,7 @@ def _create_result(optimizer, bm):
def mean_variance_builder(er: np.ndarray,
risk_model: RiskModel,
risk_model: Dict[str, Union[None, np.ndarray]],
bm: np.ndarray,
lbound: Union[np.ndarray, float],
ubound: Union[np.ndarray, float],
......@@ -57,40 +55,23 @@ def mean_variance_builder(er: np.ndarray,
lam: float=1.) -> Tuple[str, float, np.ndarray]:
lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target)
if isinstance(risk_model, FullRiskModel):
cov = risk_model.get_cov()
optimizer = QPOptimizer(er,
cov,
lbound,
ubound,
cons_mat,
clbound,
cubound,
lam)
elif isinstance(risk_model, FactorRiskModel):
cov = None
factor_cov = risk_model.get_factor_cov()
factor_loading = risk_model.get_risk_exp()
idsync = risk_model.get_idsync()
optimizer = QPOptimizer(er,
cov,
lbound,
ubound,
cons_mat,
clbound,
cubound,
lam,
factor_cov,
factor_loading,
idsync)
else:
raise ValueError("{0} is not recognized as valid risk model".format(risk_model))
optimizer = QPOptimizer(er,
risk_model['cov'],
lbound,
ubound,
cons_mat,
clbound,
cubound,
lam,
risk_model['factor_cov'],
risk_model['factor_loading'],
risk_model['idsync'])
return _create_result(optimizer, bm)
def target_vol_builder(er: np.ndarray,
risk_model: RiskModel,
risk_model: Dict[str, Union[None, np.ndarray]],
bm: np.ndarray,
lbound: Union[np.ndarray, float],
ubound: Union[np.ndarray, float],
......@@ -99,36 +80,18 @@ def target_vol_builder(er: np.ndarray,
vol_target: float = 1.)-> Tuple[str, float, np.ndarray]:
lbound, ubound, cons_mat, clbound, cubound = _create_bounds(lbound, ubound, bm, risk_exposure, risk_target)
if isinstance(risk_model, FullRiskModel):
cov = risk_model.get_cov()
optimizer = CVOptimizer(er,
cov,
lbound,
ubound,
cons_mat,
clbound,
cubound,
0.,
vol_target)
elif isinstance(risk_model, FactorRiskModel):
cov = None
factor_cov = risk_model.get_factor_cov()
factor_loading = risk_model.get_risk_exp()
idsync = risk_model.get_idsync()
optimizer = CVOptimizer(er,
cov,
lbound,
ubound,
cons_mat,
clbound,
cubound,
0.,
vol_target,
factor_cov,
factor_loading,
idsync)
else:
raise ValueError("{0} is not recognized as valid risk model".format(risk_model))
optimizer = CVOptimizer(er,
risk_model['cov'],
lbound,
ubound,
cons_mat,
clbound,
cubound,
0.,
vol_target,
risk_model['factor_cov'],
risk_model['factor_loading'],
risk_model['idsync'])
return _create_result(optimizer, bm)
......
alphamind/portfolio/riskmodel.py
View file @ b482c543
......@@ -11,7 +11,9 @@ import pandas as pd
class RiskModel(metaclass=abc.ABCMeta):
pass
def get_risk_profile(self):
pass
class FullRiskModel(RiskModel):
......@@ -26,6 +28,14 @@ class FullRiskModel(RiskModel):
else:
return self.sec_cov.values
def get_risk_profile(self, codes: List[int]=None):
return dict(
cov=self.get_cov(codes),
factor_cov=None,
factor_loading=None,
idsync=None
)
class FactorRiskModel(RiskModel):
......@@ -54,4 +64,12 @@ class FactorRiskModel(RiskModel):
if codes:
return self.idsync[codes].values
else:
return self.idsync.values
\ No newline at end of file
return self.idsync.values
def get_risk_profile(self, codes: List[int]=None):
return dict(
cov=None,
factor_cov=self.get_factor_cov(),
factor_loading=self.get_risk_exp(codes),
idsync=self.get_idsync(codes)
)
\ No newline at end of file
alphamind/tests/portfolio/test_meanvariancebuild.py
View file @ b482c543
......@@ -8,7 +8,6 @@ Created on 2017-6-27
import unittest
import numpy as np
import pandas as pd
from alphamind.portfolio.riskmodel import FullRiskModel
from alphamind.portfolio.meanvariancebuilder import mean_variance_builder
from alphamind.portfolio.meanvariancebuilder import target_vol_builder
......@@ -31,7 +30,7 @@ class TestMeanVarianceBuild(unittest.TestCase):
[1., 0., 1.]]).T
risk_target = (np.array([bm.sum(), 0.3]), np.array([bm.sum(), 0.7]))
model = FullRiskModel(pd.DataFrame(cov))
model = dict(cov=cov, factor_cov=None, factor_loading=None, idsync=None)
status, _, x = mean_variance_builder(er, model, bm, lbound, ubound, risk_exposure, risk_target)
self.assertTrue(status == 'optimal')
......@@ -58,7 +57,7 @@ class TestMeanVarianceBuild(unittest.TestCase):
[1., 0., 1.]]).T
risk_target = (np.array([bm.sum(), 0.3]), np.array([bm.sum(), 0.7]))
model = FullRiskModel(pd.DataFrame(cov))
model = dict(cov=cov, factor_cov=None, factor_loading=None, idsync=None)
status, _, x = mean_variance_builder(er, model, bm, lbound, ubound, risk_exposure, risk_target, lam=100)
self.assertTrue(status == 'optimal')
......@@ -82,7 +81,7 @@ class TestMeanVarianceBuild(unittest.TestCase):
risk_exposure = np.array([[1., 1., 1.]]).T
risk_target = (np.array([bm.sum()]), np.array([bm.sum()]))
model = FullRiskModel(pd.DataFrame(cov))
model = dict(cov=cov, factor_cov=None, factor_loading=None, idsync=None)
status, _, x = target_vol_builder(er, model, bm, lbound, ubound, risk_exposure, risk_target, 0.1)
self.assertTrue(status == 'optimal')
self.assertTrue(np.all(x <= ubound + 1.e-6))
......
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