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
Developed a Beta Coefficient Estimation Notebook for the WHU_FinTech_Workshop repository, focusing on rolling window regression and visualization to support finance analytics training. The project integrated Python and Jupyter Notebooks with libraries such as Pandas, Matplotlib, and Seaborn to deliver a structured workflow for estimating and analyzing Beta coefficients using CAPM theory. The notebook included detailed data preparation, practical rolling-window analyses for both monthly and daily datasets, and comprehensive visualizations of Beta values across industries and time periods. This work enhanced reproducibility and accessibility, providing a reusable educational resource for quantitative finance research and data-driven decision-making in future sprints.
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
18 peopleWork 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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