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Black(new version) Format

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Young
2022-02-06 22:33:16 +08:00
parent 76b7b5f24b
commit 6a946761cf
14 changed files with 35 additions and 35 deletions
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4
qlib/model/riskmodel/poet.py
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@@ -63,7 +63,7 @@ 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)
@@ -78,7 +78,7 @@ 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

20
qlib/model/riskmodel/shrink.py
View File

@@ -174,7 +174,7 @@ class ShrinkCovEstimator(RiskModel):
alpha = A / B
where `n`, `p` are the dim of observations and variables respectively.
"""
trS2 = np.sum(S ** 2)
trS2 = np.sum(S**2)
tr2S = np.trace(S) ** 2
n, p = X.shape
@@ -192,8 +192,8 @@ 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
@@ -213,11 +213,11 @@ 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)
@@ -239,16 +239,16 @@ 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
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