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support optimization based strategy (#754)

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* support optimization based strategy

* fix riskdata not found & update doc

* refactor signal_strategy

* add portfolio example

* Update examples/portfolio/prepare_riskdata.py

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* fix typo

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* fix typo

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

* update doc

* fix riskmodel doc

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>

Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
This commit is contained in:
Dong Zhou
2021-12-28 18:44:20 +08:00
committed by GitHub
parent 4709909782
commit 1b8f0b4575
14 changed files with 668 additions and 262 deletions
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31
qlib/model/riskmodel/structured.py
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@@ -13,19 +13,30 @@ class StructuredCovEstimator(RiskModel):
"""Structured Covariance Estimator
This estimator assumes observations can be predicted by multiple factors
X = FB + U
where `F` can be specified by explicit risk factors or latent factors.
X = B @ F.T + U
where `X` contains observations (row) of multiple variables (column),
`F` contains factor exposures (column) for all variables (row),
`B` is the regression coefficients matrix for all observations (row) on
all factors (columns), and `U` is the residual matrix with shape like `X`.
Therefore the structured covariance can be estimated by
cov(X) = F cov(B) F.T + cov(U)
cov(X.T) = F @ cov(B.T) @ F.T + diag(var(U))
We use latent factor models to estimate the structured covariance.
Specifically, the following latent factor models are supported:
In finance domain, there are mainly three methods to design `F` [1][2]:
- Statistical Risk Model (SRM): latent factor models major components
- Fundamental Risk Model (FRM): human designed factors
- Deep Risk Model (DRM): neural network designed factors (like a blend of SRM & DRM)
In this implementation we use latent factor models to specify `F`.
Specifically, the following two latent factor models are supported:
- `pca`: Principal Component Analysis
- `fa`: Factor Analysis
Reference: [1] Fan, J., Liao, Y., & Liu, H. (2016). An overview of the estimation of large covariance and
precision matrices. Econometrics Journal, 19(1), C1–C32. https://doi.org/10.1111/ectj.12061
Reference:
[1] Fan, J., Liao, Y., & Liu, H. (2016). An overview of the estimation of large covariance and
precision matrices. Econometrics Journal, 19(1), C1–C32. https://doi.org/10.1111/ectj.12061
[2] Lin, H., Zhou, D., Liu, W., & Bian, J. (2021). Deep Risk Model: A Deep Learning Solution for
Mining Latent Risk Factors to Improve Covariance Matrix Estimation. arXiv preprint arXiv:2107.05201.
"""
FACTOR_MODEL_PCA = "pca"
@@ -70,10 +81,10 @@ class StructuredCovEstimator(RiskModel):
model = self.solver(self.num_factors, random_state=0).fit(X)
F = model.components_.T # num_features x num_factors
B = model.transform(X) # num_samples x num_factors
F = model.components_.T # variables x factors
B = model.transform(X) # observations x factors
U = X - B @ F.T
cov_b = np.cov(B.T) # num_factors x num_factors
cov_b = np.cov(B.T) # factors x factors
var_u = np.var(U, axis=0) # diagonal
if return_decomposed_components: