Implement Enhanced Indexing as a Portfolio Optimizer#280
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you-n-g merged 32 commits intomicrosoft:mainfrom Mar 17, 2021
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Implement Enhanced Indexing as a Portfolio Optimizer#280you-n-g merged 32 commits intomicrosoft:mainfrom
you-n-g merged 32 commits intomicrosoft:mainfrom
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…ortfolio constructions.
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* Update feedback.py to support all actions Feedback.py is updated to support all actions. * Update prompts.yaml to support all actions * Revised for CI * CI * fix a ci bug * fix a ci bug --------- Co-authored-by: WinstonLiye <1957922024@qq.com>
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Implement Enhanced Indexing as a Portfolio Optimizer
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Implement Enhanced Indexing as a Portfolio Optimizer. And implemented a structured covariance estimator to boost the optimization process.
Description
Enhanced Indexing is a popular portfolio construction strategy. It's one of the index-tracking strategies, which means the possibility of both alpha and rather tender volatility.
A structured covariance estimator assumes observations can be predicted by multiple factors
X = FB + UwhereFcan be specified by explicit risk factors or latent factors. Therefore the structured covariance can be estimated bycov(X) = F cov(B) F.T + cov(U).Motivation and Context
This pull request makes no change to current functions. It simply adds new featrues. Enhanced Indexing is a popular portfolio construction strategy while a structured covariance estimator will boost many optimization-based portfolio construction strategies.
How Has This Been Tested?
pytest qlib/tests/test_all_pipeline.pyunder the upper directory ofqlib.Screenshots of Test Results (if appropriate):
2. Your own tests:test_structured_cov_estimator.pytoqlib/tests/qlib/tests/. You could find the test file at https://drive.google.com/file/d/1eKO6OcyPZjR2PEGZ2zyZByKUbgVyAwII/view?usp=sharing.Types of changes