DataScience-ArtificialIntelligence/OOPsJava
Nov 2024 – Nov 2024
1 Month active
Languages Used
Java
Technical Skills
Algorithm DesignData StructuresFile I/OFinancial AnalysisObject-Oriented Programming
Savya Sanchi Sharma
PROFILE
Savya Sanchi Sharma
Savyasanchi Sharma developed the Stock Portfolio Optimization feature for the DataScience-ArtificialIntelligence/OOPsJava repository, focusing on automating stock selection based on user-defined financial indicator weights and a budget constraint. Using Java and object-oriented programming principles, Savyasanchi designed an algorithm that calculates weighted scores for each stock, sorts them, and selects an optimal portfolio. The solution outputs results to both the console and a CSV file, supporting downstream reporting and reproducibility. By leveraging skills in algorithm design, data structures, and file I/O, Savyasanchi enabled data-driven investment decisions, reduced manual analysis time, and improved traceability, demonstrating depth in both technical and business problem-solving.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
682
Activity Months1
Work History
November 2024
1 Commits • 1 Features
Nov 1, 2024Month 2024-11 — Key accomplishments include delivering the Stock Portfolio Optimization feature in DataScience-ArtificialIntelligence/OOPsJava. The feature introduces a stock selection algorithm that computes a weighted score for each stock using user-defined indicator weights and a budget constraint, sorts stocks, and outputs the optimal portfolio to the console and as a CSV export. This enables data-driven, auditable portfolio decisions and supports downstream reporting. Commit reference: f6398f0a2bab17331e7ea2951ee3d88d3243f099 (OOPs-Project). No major bugs fixed this month; focus was on delivering core capability and establishing reproducible results. Overall impact: reduces manual analysis time, improves decision speed and traceability. Technologies/skills demonstrated: Java OOP patterns, algorithm design for weighted scoring under constraints, CSV I/O, console reporting.
1 Commits • 1 Features
Nov 1, 2024Month 2024-11 — Key accomplishments include delivering the Stock Portfolio Optimization feature in DataScience-ArtificialIntelligence/OOPsJava. The feature introduces a stock selection algorithm that computes a weighted score for each stock using user-defined indicator weights and a budget constraint, sorts stocks, and outputs the optimal portfolio to the console and as a CSV export. This enables data-driven, auditable portfolio decisions and supports downstream reporting. Commit reference: f6398f0a2bab17331e7ea2951ee3d88d3243f099 (OOPs-Project). No major bugs fixed this month; focus was on delivering core capability and establishing reproducible results. Overall impact: reduces manual analysis time, improves decision speed and traceability. Technologies/skills demonstrated: Java OOP patterns, algorithm design for weighted scoring under constraints, CSV I/O, console reporting.
November 2024
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Java
Technical Skills
Algorithm DesignData StructuresFile I/OFinancial AnalysisObject-Oriented Programming
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