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Awesome GitHub RepositoriesStepwise Variable Selection
Iterative regression techniques used to identify the most statistically significant variables for a predictive model.
Distinct from Robust Regression: Focuses on variable selection and robustness over time rather than outlier-resistant estimation
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Awesome Stepwise Variable Selection GitHub Repositories
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- je-suis-tm/quant-trading
je-suis-tm/quant-trading
9,190在 GitHub 上查看This project is a Python financial analytics framework and quantitative trading library. It provides a suite of mathematical tools for asset pricing, statistical market analysis, and the development of algorithmic trading strategies. The library is distinguished by its focus on currency and commodity correlation modeling, using regression and normalization to identify exchange rate drivers. It features a specialized portfolio optimization engine that applies graph theory, such as clique centrality and degeneracy ordering, alongside quadratic programming to balance risk-adjusted returns. The
Identifies the most statistically significant variables across time periods using stepwise regression to ensure model reliability.
Pythonalgorithmic-tradingbollinger-bandscommodity-trading
