WHUFT/WHU_FinTech_Workshop
Nov 2024 – Nov 2024
1 Month active
Languages Used
Jupyter NotebookPython
Technical Skills
Data AnalysisJupyter NotebooksMatplotlibPandasPython ProgrammingQuantitative Finance
Stargazingwhuems09
PROFILE
Stargazingwhuems09
During November 2024, the developer created a Beta Coefficient Estimation Notebook for the WHUFT/WHU_FinTech_Workshop repository, focusing on quantitative finance analytics. They implemented rolling window regression techniques using Python and Jupyter Notebooks, leveraging libraries such as Pandas, Matplotlib, and Statsmodels to analyze Beta dynamics across industries and time periods. The notebook integrated data preparation, CAPM theory explanation, and practical estimation methods, supporting both monthly and daily data. By organizing the workflow for clarity and reproducibility, the developer enhanced the accessibility of finance research and education, delivering a resource that supports data-driven decision-making and future learning within the workshop context.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
4,752
Activity Months1
Your Network
16 people
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for WHU_FinTech_Workshop highlighting delivery of a Beta Coefficient Estimation Notebook with rolling window regression and visualization. The notebook provides a structured explanation of CAPM concepts, estimation methods, data preparation, and practical rolling-window analyses for both monthly and daily data, with comprehensive visualization of Beta values across industries and time periods. This work enhances finance analytics training, reproducibility, and data-driven decision support.
1 Commits • 1 Features
Nov 1, 2024November 2024 monthly summary for WHU_FinTech_Workshop highlighting delivery of a Beta Coefficient Estimation Notebook with rolling window regression and visualization. The notebook provides a structured explanation of CAPM concepts, estimation methods, data preparation, and practical rolling-window analyses for both monthly and daily data, with comprehensive visualization of Beta values across industries and time periods. This work enhances finance analytics training, reproducibility, and data-driven decision support.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Jupyter NotebookPython
Technical Skills
Data AnalysisJupyter NotebooksMatplotlibPandasPython ProgrammingQuantitative FinanceSeabornStatistical ModelingStatsmodels
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