HWTeng-Teaching/202409-ML-FinTech
Oct 2024 – Dec 2024
3 Months active
Languages Used
JSONJupyter NotebookMarkdownPython
Technical Skills
Bayesian ClassificationBootstrap MethodsCross-ValidationData AnalysisISLPJupyter Notebook
Yi-Fruitdrops
PROFILE
Yi-fruitdrops
Ethan contributed to the HWTeng-Teaching/202409-ML-FinTech repository by developing machine learning tutorials, asset management features, and a dynamic stock return prediction system. He created Jupyter Notebook-based educational content covering classification, regression, and theoretical proofs, supporting onboarding and reproducibility. Ethan implemented a Random Forest-based workflow in Python and scikit-learn to predict stock returns for multiple equities, integrating K-Fold cross-validation and dynamic portfolio weighting to benchmark against equal-weight strategies. He also managed PDF and PowerPoint assets, enhancing governance and traceability. Ethan’s work demonstrated depth in statistical modeling, data analysis, and asset lifecycle management, addressing both technical and organizational project needs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
19Total
Bugs
0
Commits
19
Features
6
Lines of code
8,599
Activity Months3
Your Network
35 people
Work History
December 2024
3 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for HWTeng-Teaching/202409-ML-FinTech: Delivered two feature areas with business value: (1) Dynamic ML-based Stock Return Prediction and Weighting using a Random Forest Regressor for MSFT/XOM/PFE, including multi-period backtesting with K-Fold cross-validation and dynamic weight adjustment vs an equal-weight baseline; (2) Portfolio Growth Presentation Asset Lifecycle, demonstrating governance by adding and removing a PowerPoint asset (no code changes). No major bugs reported this month. Overall impact: data-driven investment decision support, reproducible ML experiments, and stronger artifact governance. Technologies/skills demonstrated: Python, scikit-learn, Random Forest, cross-validation, backtesting design, Git commit traceability, and asset lifecycle management.
3 Commits • 2 Features
Dec 1, 2024December 2024 monthly summary for HWTeng-Teaching/202409-ML-FinTech: Delivered two feature areas with business value: (1) Dynamic ML-based Stock Return Prediction and Weighting using a Random Forest Regressor for MSFT/XOM/PFE, including multi-period backtesting with K-Fold cross-validation and dynamic weight adjustment vs an equal-weight baseline; (2) Portfolio Growth Presentation Asset Lifecycle, demonstrating governance by adding and removing a PowerPoint asset (no code changes). No major bugs reported this month. Overall impact: data-driven investment decision support, reproducible ML experiments, and stronger artifact governance. Technologies/skills demonstrated: Python, scikit-learn, Random Forest, cross-validation, backtesting design, Git commit traceability, and asset lifecycle management.
December 2024
November 2024
14 Commits • 3 Features
Nov 1, 2024November 2024: Delivered foundational ML education content and improved repository maintainability for HWTeng-Teaching/202409-ML-FinTech. Key features include notebook-based tutorials for classification and regression, theoretical proofs notebooks for discriminant function theory and Bayes classifier, and comprehensive repository housekeeping. Targeted cleanup of stale notebooks and files reduced confusion and risk, supporting onboarding and reproducibility. This work enhances scalable learning materials, accelerates contributor ramp-up, and clarifies project structure for future development.
November 2024
14 Commits • 3 Features
Nov 1, 2024November 2024: Delivered foundational ML education content and improved repository maintainability for HWTeng-Teaching/202409-ML-FinTech. Key features include notebook-based tutorials for classification and regression, theoretical proofs notebooks for discriminant function theory and Bayes classifier, and comprehensive repository housekeeping. Targeted cleanup of stale notebooks and files reduced confusion and risk, supporting onboarding and reproducibility. This work enhances scalable learning materials, accelerates contributor ramp-up, and clarifies project structure for future development.
October 2024
2 Commits • 1 Features
Oct 1, 2024October 2024: Delivered targeted PDF asset refresh in the File Management System for HWTeng-Teaching/202409-ML-FinTech, improving asset accuracy, governance, and readiness for upcoming features. Added 15_Ethan.pdf and removed 20_宋羿廷.pdf; changes tracked via two commits, enabling better traceability and faster asset retrieval.
2 Commits • 1 Features
Oct 1, 2024October 2024: Delivered targeted PDF asset refresh in the File Management System for HWTeng-Teaching/202409-ML-FinTech, improving asset accuracy, governance, and readiness for upcoming features. Added 15_Ethan.pdf and removed 20_宋羿廷.pdf; changes tracked via two commits, enabling better traceability and faster asset retrieval.
October 2024
Activity
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Quality Metrics
Correctness91.6%
Maintainability91.6%
Architecture91.6%
Performance91.6%
AI Usage28.4%
Skills & Technologies
Programming Languages
JSONJupyter NotebookMarkdownPython
Technical Skills
BacktestingBayesian ClassificationBootstrap MethodsCross-ValidationData AnalysisData VisualizationFinancial ModelingISLPJupyter NotebookLasso RegressionLinear RegressionMachine LearningMathematical ProofPartial Least Squares RegressionPolynomial Regression
Repositories Contributed To
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