WHUFT/WHU_FinTech_Workshop
Dec 2024 – Dec 2024
1 Month active
Languages Used
Jupyter NotebookPythonSQL
Technical Skills
Data AnalysisFinancial ModelingNumPyPandasPythonQuantitative Finance
zcyMary
PROFILE
Zcymary
During December 2024, Zcyxhysjx developed a comprehensive Fama-French Factor Replication Notebook and Data Pipeline for the WHU_FinTech_Workshop repository. Leveraging Python, Pandas, and Statsmodels, they implemented an end-to-end workflow to replicate both three-factor and five-factor models, encompassing data extraction, merging, portfolio sorting, and regression-based evaluation. Their approach included refining the data selection process by removing market equity from final sorting variables, streamlining calculations for book-to-market and related factors. This modular, reproducible pipeline supports efficient backtesting and future research, providing a robust foundation for quantitative finance analysis and improving the accuracy and speed of factor model evaluation.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
6,578
Activity Months1
Your Network
10 people
Work History
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024: Delivered a comprehensive Fama-French Factor Replication Notebook and Data Pipeline for WHU_FinTech_Workshop. Built end-to-end functionality to replicate three-factor and five-factor models, covering theory, SMB/HML/RMW/CMA construction, data extraction, merging, portfolio sorting, factor replication, and regression-based evaluation. Refined data selection by removing market equity from final sorting variables to streamline calculations for book-to-market and related factors. This work establishes a reproducible, scalable framework for factor-based research and investment model evaluation, improving accuracy and efficiency of factor construction and backtesting.
2 Commits • 1 Features
Dec 1, 2024December 2024: Delivered a comprehensive Fama-French Factor Replication Notebook and Data Pipeline for WHU_FinTech_Workshop. Built end-to-end functionality to replicate three-factor and five-factor models, covering theory, SMB/HML/RMW/CMA construction, data extraction, merging, portfolio sorting, factor replication, and regression-based evaluation. Refined data selection by removing market equity from final sorting variables to streamline calculations for book-to-market and related factors. This work establishes a reproducible, scalable framework for factor-based research and investment model evaluation, improving accuracy and efficiency of factor construction and backtesting.
December 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture70.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
Data AnalysisFinancial ModelingNumPyPandasPythonQuantitative FinanceSQLAlchemyStatsmodels
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