GAOCheryl/QF5214_2025_G8
Mar 2025 – Apr 2025
2 Months active
Languages Used
CSVPythonMarkdown
Technical Skills
Algorithmic TradingBacktestingData AnalysisData PreprocessingDeep LearningFinancial Modeling
lxiaoy1111
PROFILE
Lxiaoy1111
Cheryl Liu developed a scalable financial time-series modeling and backtesting framework for the GAOCheryl/QF5214_2025_G8 repository, focusing on end-to-end strategy implementation and reproducibility. She architected a MASTER model leveraging temporal and spatial attention, integrating data preprocessing, model training, and market-cap weighted backtesting to demonstrate business value. Her work included building and refining multiple stock trading strategies, establishing a robust pipeline with measurable outputs, and maintaining clear project structure through code refactoring and documentation. Using Python, PyTorch, and Pandas, Cheryl emphasized maintainability and stakeholder visibility, delivering a well-documented, reproducible system that supports ongoing quantitative research and strategy development.
Overall Statistics
Feature vs Bugs
75%Features
Repository Contributions
42Total
Bugs
3
Commits
42
Features
9
Lines of code
598,372
Activity Months2
Your Network
9 people
Work History
April 2025
36 Commits • 6 Features
Apr 1, 2025April 2025 monthly summary for GAOCheryl/QF5214_2025_G8 focused on delivering a runnable modeling pipeline, enhancing documentation, and improving workspace hygiene to support long-term maintainability and reproducibility.
36 Commits • 6 Features
Apr 1, 2025April 2025 monthly summary for GAOCheryl/QF5214_2025_G8 focused on delivering a runnable modeling pipeline, enhancing documentation, and improving workspace hygiene to support long-term maintainability and reproducibility.
April 2025
March 2025
6 Commits • 3 Features
Mar 1, 2025March 2025 focused on establishing a scalable strategy framework, delivering end-to-end financial time-series modeling with backtesting capabilities, and implementing a suite of stock trading strategies with reproducible outputs for stakeholder visibility. Work emphasized code organization, maintainability, and business value through demonstrable backtesting results.
March 2025
6 Commits • 3 Features
Mar 1, 2025March 2025 focused on establishing a scalable strategy framework, delivering end-to-end financial time-series modeling with backtesting capabilities, and implementing a suite of stock trading strategies with reproducible outputs for stakeholder visibility. Work emphasized code organization, maintainability, and business value through demonstrable backtesting results.
Activity
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Quality Metrics
Correctness90.0%
Maintainability89.6%
Architecture90.0%
Performance87.2%
AI Usage21.0%
Skills & Technologies
Programming Languages
CSVMarkdownPython
Technical Skills
Algorithmic TradingBacktestingCode RefactoringData AnalysisData CleaningData EngineeringData ManagementData PreprocessingData ScienceDeep LearningDocumentationFile DeletionFinancial AnalysisFinancial ModelingMachine Learning
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