MUNQuantSociety/MQSMaster
Nov 2025 – Nov 2025
1 Month active
Languages Used
PythonYAML
Technical Skills
API TestingAPI testingCI/CDGitHub ActionsPythonPython Development
Ayesha973
PROFILE
Ayesha973
Aysha Ziaudin developed a backtesting framework for trading strategies in the MUNQuantSociety/MQSMaster repository, focusing on robust API endpoint testing and automated deployment. She implemented a CI/CD pipeline using GitHub Actions and YAML, integrating secure FMP API key management to ensure safe and reproducible deployments. Her work emphasized Python development and unit testing, expanding test coverage for financial market data APIs and enabling automated validation of new code. By establishing these workflows, Aysha improved release reliability and accelerated pull request feedback, laying a foundation for scalable strategy experimentation and more efficient development cycles within the MQSMaster project.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
8Total
Bugs
0
Commits
8
Features
2
Lines of code
502
Activity Months1
Your Network
13 people
Work History
November 2025
8 Commits • 2 Features
Nov 1, 2025November 2025 (2025-11) highlights for MUNQuantSociety/MQSMaster: Delivered a Backtesting Framework for Trading Strategies with expanded API endpoint tests and CI/CD YAML on main to enable automated testing and deployments. Implemented a CI/CD Pipeline for Python Projects with tests for financial market data APIs and secure FMP API key integration in GitHub Actions. These efforts increased test coverage, improved release reliability, and laid the groundwork for scalable strategy experimentation.
8 Commits • 2 Features
Nov 1, 2025November 2025 (2025-11) highlights for MUNQuantSociety/MQSMaster: Delivered a Backtesting Framework for Trading Strategies with expanded API endpoint tests and CI/CD YAML on main to enable automated testing and deployments. Implemented a CI/CD Pipeline for Python Projects with tests for financial market data APIs and secure FMP API key integration in GitHub Actions. These efforts increased test coverage, improved release reliability, and laid the groundwork for scalable strategy experimentation.
November 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability85.0%
Architecture85.0%
Performance85.0%
AI Usage25.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
API TestingAPI testingCI/CDGitHub ActionsPythonPython DevelopmentUnit Testingbacktestingdata analysis
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