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Jactus
2020-11-02 11:09:24 +08:00
parent b077d848f4
commit 661b3bffcc
11 changed files with 157 additions and 157 deletions
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121
qlib/model/riskmodel.py
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@@ -16,11 +16,11 @@ class RiskModel(BaseModel):
A risk model is used to estimate the covariance matrix of stock returns.
"""
MASK_NAN = 'mask'
FILL_NAN = 'fill'
IGNORE_NAN = 'ignore'
MASK_NAN = "mask"
FILL_NAN = "fill"
IGNORE_NAN = "ignore"
def __init__(self, nan_option: str = 'ignore', assume_centered: bool = False, scale_return: bool = True):
def __init__(self, nan_option: str = "ignore", assume_centered: bool = False, scale_return: bool = True):
"""
Args:
nan_option (str): nan handling option (`ignore`/`mask`/`fill`)
@@ -28,15 +28,19 @@ class RiskModel(BaseModel):
scale_return (bool): whether scale returns as percentage
"""
# nan
assert nan_option in [self.MASK_NAN, self.FILL_NAN, self.IGNORE_NAN], \
f'`nan_option={nan_option}` is not supported'
assert nan_option in [
self.MASK_NAN,
self.FILL_NAN,
self.IGNORE_NAN,
], f"`nan_option={nan_option}` is not supported"
self.nan_option = nan_option
self.assume_centered = assume_centered
self.scale_return = scale_return
def predict(self, X: Union[pd.Series, pd.DataFrame, np.ndarray],
return_corr: bool = False, is_price: bool = True) -> Union[pd.DataFrame, np.ndarray]:
def predict(
self, X: Union[pd.Series, pd.DataFrame, np.ndarray], return_corr: bool = False, is_price: bool = True
) -> Union[pd.DataFrame, np.ndarray]:
"""
Args:
X (pd.Series, pd.DataFrame or np.ndarray): data from which to estimate the covariance,
@@ -53,18 +57,18 @@ class RiskModel(BaseModel):
else:
if isinstance(X.index, pd.MultiIndex):
if isinstance(X, pd.DataFrame):
X = X.iloc[:, 0].unstack(level='instrument') # always use the first column
X = X.iloc[:, 0].unstack(level="instrument") # always use the first column
else:
X = X.unstack(level='instrument')
X = X.unstack(level="instrument")
else:
# X is 2D DataFrame
pass
columns = X.columns # will be used to restore dataframe
columns = X.columns # will be used to restore dataframe
X = X.values
# calculate pct_change
if is_price:
X = X[1:] / X[:-1] - 1 # NOTE: resulting `n - 1` rows
X = X[1:] / X[:-1] - 1 # NOTE: resulting `n - 1` rows
# scale return
if self.scale_return:
@@ -106,7 +110,7 @@ class RiskModel(BaseModel):
N = len(X)
if isinstance(X, np.ma.MaskedArray):
M = 1 - X.mask
N = M.T.dot(M) # each pair has distinct number of samples
N = M.T.dot(M) # each pair has distinct number of samples
return xTx / N
def _preprocess(self, X: np.ndarray) -> Union[np.ndarray, np.ma.MaskedArray]:
@@ -165,14 +169,14 @@ class ShrinkCovEstimator(RiskModel):
[7] https://www.econ.uzh.ch/dam/jcr:ffffffff-935a-b0d6-0000-0000648dfc98/covMarket.m.zip
"""
SHR_LW = 'lw'
SHR_OAS = 'oas'
SHR_LW = "lw"
SHR_OAS = "oas"
TGT_CONST_VAR = 'const_var'
TGT_CONST_CORR = 'const_corr'
TGT_SINGLE_FACTOR = 'single_factor'
TGT_CONST_VAR = "const_var"
TGT_CONST_CORR = "const_corr"
TGT_SINGLE_FACTOR = "single_factor"
def __init__(self, alpha: Union[str, float] = 0.0, target: Union[str, np.ndarray] = 'const_var', **kwargs):
def __init__(self, alpha: Union[str, float] = 0.0, target: Union[str, np.ndarray] = "const_var", **kwargs):
"""
Args:
alpha (str or float): shrinking parameter or estimator (`lw`/`oas`)
@@ -183,24 +187,26 @@ class ShrinkCovEstimator(RiskModel):
# alpha
if isinstance(alpha, str):
assert alpha in [self.SHR_LW, self.SHR_OAS], \
f'shrinking method `{alpha}` is not supported'
assert alpha in [self.SHR_LW, self.SHR_OAS], f"shrinking method `{alpha}` is not supported"
elif isinstance(alpha, (float, np.floating)):
assert 0 <= alpha <= 1, 'alpha should be between [0, 1]'
assert 0 <= alpha <= 1, "alpha should be between [0, 1]"
else:
raise TypeError('invalid argument type for `alpha`')
raise TypeError("invalid argument type for `alpha`")
self.alpha = alpha
# target
if isinstance(target, str):
assert target in [self.TGT_CONST_VAR, self.TGT_CONST_CORR, self.TGT_SINGLE_FACTOR], \
f'shrinking target `{target} is not supported'
assert target in [
self.TGT_CONST_VAR,
self.TGT_CONST_CORR,
self.TGT_SINGLE_FACTOR,
], f"shrinking target `{target} is not supported"
elif isinstance(target, np.ndarray):
pass
else:
raise TypeError('invalid argument type for `target`')
raise TypeError("invalid argument type for `target`")
if alpha == self.SHR_OAS and target != self.TGT_CONST_VAR:
raise NotImplementedError('currently `oas` can only support `const_var` as target')
raise NotImplementedError("currently `oas` can only support `const_var` as target")
self.target = target
def _predict(self, X: np.ndarray) -> np.ndarray:
@@ -215,7 +221,7 @@ class ShrinkCovEstimator(RiskModel):
# shrink covariance
if alpha > 0:
S *= (1 - alpha)
S *= 1 - alpha
F *= alpha
S += F
@@ -292,8 +298,8 @@ class ShrinkCovEstimator(RiskModel):
alpha = A / B
where `n`, `p` are the dim of observations and variables respectively.
"""
trS2 = np.sum(S**2)
tr2S = np.trace(S)**2
trS2 = np.sum(S ** 2)
tr2S = np.trace(S) ** 2
n, p = X.shape
@@ -310,10 +316,10 @@ class ShrinkCovEstimator(RiskModel):
"""
t, n = X.shape
y = X**2
phi = np.sum(y.T.dot(y) / t - S**2)
y = X ** 2
phi = np.sum(y.T.dot(y) / t - S ** 2)
gamma = np.linalg.norm(S - F, 'fro')**2
gamma = np.linalg.norm(S - F, "fro") ** 2
kappa = phi / gamma
alpha = max(0, min(1, kappa / t))
@@ -331,15 +337,15 @@ class ShrinkCovEstimator(RiskModel):
sqrt_var = np.sqrt(var)
r_bar = (np.sum(S / np.outer(sqrt_var, sqrt_var)) - n) / (n * (n - 1))
y = X**2
phi_mat = y.T.dot(y) / t - S**2
y = X ** 2
phi_mat = y.T.dot(y) / t - S ** 2
phi = np.sum(phi_mat)
theta_mat = (X**3).T.dot(X) / t - var[:, None] * S
theta_mat = (X ** 3).T.dot(X) / t - var[:, None] * S
np.fill_diagonal(theta_mat, 0)
rho = np.sum(np.diag(phi_mat)) + r_bar * np.sum(np.outer(1 / sqrt_var, sqrt_var) * theta_mat)
gamma = np.linalg.norm(S - F, 'fro')**2
gamma = np.linalg.norm(S - F, "fro") ** 2
kappa = (phi - rho) / gamma
alpha = max(0, min(1, kappa / t))
@@ -357,19 +363,21 @@ class ShrinkCovEstimator(RiskModel):
cov_mkt = np.asarray(X.T.dot(X_mkt) / len(X))
var_mkt = np.asarray(X_mkt.dot(X_mkt) / len(X))
y = X**2
phi = np.sum(y.T.dot(y)) / t - np.sum(S**2)
y = X ** 2
phi = np.sum(y.T.dot(y)) / t - np.sum(S ** 2)
rdiag = np.sum(y**2) / t - np.sum(np.diag(S)**2)
rdiag = np.sum(y ** 2) / t - np.sum(np.diag(S) ** 2)
z = X * X_mkt[:, None]
v1 = y.T.dot(z) / t - cov_mkt[:, None] * S
roff1 = np.sum(v1 * cov_mkt[:, None].T) / var_mkt - np.sum(np.diag(v1) * cov_mkt) / var_mkt
v3 = z.T.dot(z) / t - var_mkt * S
roff3 = np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt**2 - np.sum(np.diag(v3) * cov_mkt**2) / var_mkt**2
roff3 = (
np.sum(v3 * np.outer(cov_mkt, cov_mkt)) / var_mkt ** 2 - np.sum(np.diag(v3) * cov_mkt ** 2) / var_mkt ** 2
)
roff = 2 * roff1 - roff3
rho = rdiag + roff
gamma = np.linalg.norm(S - F, 'fro')**2
gamma = np.linalg.norm(S - F, "fro") ** 2
kappa = (phi - rho) / gamma
alpha = max(0, min(1, kappa / t))
@@ -386,11 +394,11 @@ class POETCovEstimator(RiskModel):
[2] http://econweb.rutgers.edu/yl1114/papers/poet/POET.m
"""
THRESH_SOFT = 'soft'
THRESH_HARD = 'hard'
THRESH_SCAD = 'scad'
THRESH_SOFT = "soft"
THRESH_HARD = "hard"
THRESH_SCAD = "scad"
def __init__(self, num_factors: int = 0, thresh: float = 1.0, thresh_method: str = 'soft', **kwargs):
def __init__(self, num_factors: int = 0, thresh: float = 1.0, thresh_method: str = "soft", **kwargs):
"""
Args:
num_factors (int): number of factors (if set to zero, no factor model will be used)
@@ -403,25 +411,28 @@ class POETCovEstimator(RiskModel):
"""
super().__init__(**kwargs)
assert num_factors >= 0, '`num_factors` requires a positive integer'
assert num_factors >= 0, "`num_factors` requires a positive integer"
self.num_factors = num_factors
assert thresh >= 0, '`thresh` requires a positive float number'
assert thresh >= 0, "`thresh` requires a positive float number"
self.thresh = thresh
assert thresh_method in [self.THRESH_HARD, self.THRESH_SOFT, self.THRESH_SCAD], \
'`thresh_method` should be `soft`/`hard`/`scad`'
assert thresh_method in [
self.THRESH_HARD,
self.THRESH_SOFT,
self.THRESH_SCAD,
], "`thresh_method` should be `soft`/`hard`/`scad`"
self.thresh_method = thresh_method
def _predict(self, X: np.ndarray) -> np.ndarray:
Y = X.T # NOTE: to match POET's implementation
Y = X.T # NOTE: to match POET's implementation
p, n = Y.shape
if self.num_factors > 0:
Dd, V = np.linalg.eig(Y.T.dot(Y))
V = V[:, np.argsort(Dd)]
F = V[:, -self.num_factors:][:, ::-1] * np.sqrt(n)
F = V[:, -self.num_factors :][:, ::-1] * np.sqrt(n)
LamPCA = Y.dot(F) / n
uhat = np.asarray(Y - LamPCA.dot(F.T))
Lowrank = np.asarray(LamPCA.dot(LamPCA.T))
@@ -434,12 +445,12 @@ class POETCovEstimator(RiskModel):
lamb = rate * self.thresh
SuPCA = uhat.dot(uhat.T) / n
SuDiag = np.diag(np.diag(SuPCA))
R = np.linalg.inv(SuDiag**0.5).dot(SuPCA).dot(np.linalg.inv(SuDiag**0.5))
R = np.linalg.inv(SuDiag ** 0.5).dot(SuPCA).dot(np.linalg.inv(SuDiag ** 0.5))
if self.thresh_method == self.THRESH_HARD:
M = R * (np.abs(R) > lamb)
elif self.thresh_method == self.THRESH_SOFT:
res = (np.abs(R) - lamb)
res = np.abs(R) - lamb
res = (res + np.abs(res)) / 2
M = np.sign(R) * res
else:
@@ -449,7 +460,7 @@ class POETCovEstimator(RiskModel):
M = M1 + M2 + M3
Rthresh = M - np.diag(np.diag(M)) + np.eye(p)
SigmaU = (SuDiag**0.5).dot(Rthresh).dot(SuDiag**0.5)
SigmaU = (SuDiag ** 0.5).dot(Rthresh).dot(SuDiag ** 0.5)
SigmaY = SigmaU + Lowrank
return SigmaY