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@article{201136,
        author = {shweta singh and Ashish Katore and Swapnil Thakre},
        title = {Enhanced Portfolio Optimization (EPO): A Quantitative Approach for Automated Stock Analysis and Strategy Engineering.},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12482-12484},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201136},
        abstract = {In today's fast-paced financial markets, manual stock analysis is becoming increasingly inefficient due to the large volume of data and rapid market fluctuations.
This paper presents an automated system based on Enhanced Portfolio Optimization (EPO) that integrates quantitative analysis and strategy engineering for effective stock monitoring and decision-making. The system utilizes real-time market data, processes it using Duck DB, and applies rule-based trading strategies to generate automated signals.
It minimizes human error, removes emotional bias, and ensures scalable analysis across multiple stocks.
Performance metrics such as profit and loss, win-rate, and drawdown are used to evaluate system efficiency. The proposed solution demonstrates improved speed, consistency, and accuracy in trading decisions.},
        keywords = {},
        month = {May},
        }